Libraries

Load the required libraries.

library(tidyverse)
── Attaching core tidyverse packages ──────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     ── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggplot2)
library(scales)

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor
library(pals)
library(ggrepel)
library(patchwork)

citation("tidyverse")
To cite package ‘tidyverse’ in publications use:

  Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester
  J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V,
  Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” _Journal of Open
  Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Welcome to the {tidyverse}},
    author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
    year = {2019},
    journal = {Journal of Open Source Software},
    volume = {4},
    number = {43},
    pages = {1686},
    doi = {10.21105/joss.01686},
  }
citation("ggplot2")
To cite ggplot2 in publications, please use

  H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

A BibTeX entry for LaTeX users is

  @Book{,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
  }
citation("scales")
To cite package ‘scales’ in publications use:

  Wickham H, Seidel D (2022). _scales: Scale Functions for Visualization_. R package version 1.2.1,
  <https://CRAN.R-project.org/package=scales>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {scales: Scale Functions for Visualization},
    author = {Hadley Wickham and Dana Seidel},
    year = {2022},
    note = {R package version 1.2.1},
    url = {https://CRAN.R-project.org/package=scales},
  }
citation("pals")
To cite package ‘pals’ in publications use:

  Wright K (2021). _pals: Color Palettes, Colormaps, and Tools to Evaluate Them_. R package version
  1.7, <https://CRAN.R-project.org/package=pals>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {pals: Color Palettes, Colormaps, and Tools to Evaluate Them},
    author = {Kevin Wright},
    year = {2021},
    note = {R package version 1.7},
    url = {https://CRAN.R-project.org/package=pals},
  }
citation("ggrepel")
To cite package ‘ggrepel’ in publications use:

  Slowikowski K (2023). _ggrepel: Automatically Position Non-Overlapping Text Labels with 'ggplot2'_. R
  package version 0.9.3, <https://CRAN.R-project.org/package=ggrepel>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {ggrepel: Automatically Position Non-Overlapping Text Labels with
'ggplot2'},
    author = {Kamil Slowikowski},
    year = {2023},
    note = {R package version 0.9.3},
    url = {https://CRAN.R-project.org/package=ggrepel},
  }
citation("patchwork")
To cite package ‘patchwork’ in publications use:

  Pedersen T (2022). _patchwork: The Composer of Plots_. R package version 1.1.2,
  <https://CRAN.R-project.org/package=patchwork>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {patchwork: The Composer of Plots},
    author = {Thomas Lin Pedersen},
    year = {2022},
    note = {R package version 1.1.2},
    url = {https://CRAN.R-project.org/package=patchwork},
  }
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.8 (Ootpa)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Chicago
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.1.2 ggrepel_0.9.3   pals_1.7        scales_1.2.1    lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0  
 [8] dplyr_1.1.2     purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1    ggplot2_3.4.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.3      compiler_4.3.0    maps_3.4.1        Rcpp_1.0.10       tidyselect_1.2.0  dichromat_2.0-0.1
 [7] R6_2.5.1          generics_0.1.3    mapproj_1.2.11    knitr_1.43        munsell_0.5.0     pillar_1.9.0     
[13] tzdb_0.4.0        rlang_1.1.1       utf8_1.2.3        stringi_1.7.12    xfun_0.39         timechange_0.2.0 
[19] cli_3.6.1         withr_2.5.0       magrittr_2.0.3    grid_4.3.0        rstudioapi_0.14   hms_1.1.3        
[25] lifecycle_1.0.3   vctrs_0.6.3       glue_1.6.2        fansi_1.0.4       colorspace_2.1-0  tools_4.3.0      
[31] pkgconfig_2.0.3  

Data Import and Setup

Reads the Gencode summarized data files from Kraken Output (https://github.com/npbhavya/Kraken2-output-manipulation) into R.

gencode_PB=read.csv(file="gencode_PM_PB_species_kraken_summary", header=TRUE)
gencode_PL=read.csv(file="gencode_PM_PL_species_kraken_summary", header=TRUE)
gencode_PM=read.csv(file="gencode_PM_PM_species_kraken_summary", header=TRUE)
gencode_PS=read.csv(file="gencode_PM_PS_species_kraken_summary", header=TRUE)
gencode_star=read.delim(file="star_alignment_plot.tsv", header=TRUE, sep="\t")
gencode_PB$PM_14_PB_RN_BA_220606=as.integer(gencode_PB$PM_14_PB_RN_BA_220606)
gencode_PB$PM_58_PB_RN_BA_220916=as.integer(gencode_PB$PM_58_PB_RN_BA_220916)
gencode_PL$PM_15_PL_RN_BA_220622=as.integer(gencode_PL$PM_15_PL_RN_BA_220622)
gencode_PL$PM_58_PL_RN_BA_220923=as.integer(gencode_PL$PM_58_PL_RN_BA_220923)
gencode_PM$PM_15_PM_RN_BA_220614=as.integer(gencode_PM$PM_15_PM_RN_BA_220614)
gencode_PM$PM_58_PM_RN_BA_220920=as.integer(gencode_PM$PM_58_PM_RN_BA_220920)
gencode_PS$PM_17_PS_RN_BA_220628=as.integer(gencode_PS$PM_17_PS_RN_BA_220628)
gencode_PS$PM_58_PS_RN_BA_220921=as.integer(gencode_PS$PM_58_PS_RN_BA_220921)

Pivots the data from wide to long format, renames the new columns, arranges the columns in ascending order by sample name and descending order by number of counts, groups the rows by sample, removes the “Homo” rows, slices the top 5 rows for each sample, and removes all information from the sample codes besides the project identifier, the subject number, and the collection/extraction method identifier.

gencode_PB_long_top5=gencode_PB %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PB_long_top5$Sample=gsub("_RN.*", "", gencode_PB_long_top5$Sample)
sum(gencode_PB_long_top5$Counts)
[1] 37289760
gencode_PL_long_top5=gencode_PL %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PL_long_top5$Sample=gsub("_RN.*", "", gencode_PL_long_top5$Sample)
sum(gencode_PL_long_top5$Counts)
[1] 161591358
gencode_PM_long_top5=gencode_PM %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PM_long_top5$Sample=gsub("_RN.*", "", gencode_PM_long_top5$Sample)
sum(gencode_PM_long_top5$Counts)
[1] 121341438
gencode_PS_long_top5=gencode_PS %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PS_long_top5$Sample=gsub("_RN.*", "", gencode_PS_long_top5$Sample)
sum(gencode_PS_long_top5$Counts)
[1] 164300830

Examples of before and after data format manipulation.

head(gencode_PB, n=10)
head(gencode_PB_long_top5, n=10)
gencode_STAR_PB=gencode_star %>%
  filter(str_detect(Category, "PB_RN_BA"))
gencode_STAR_PL=gencode_star %>%
  filter(str_detect(Category, "PL_RN_BA"))
gencode_STAR_PM=gencode_star %>%
  filter(str_detect(Category, "PM_RN_BA"))
gencode_STAR_PS=gencode_star %>%
  filter(str_detect(Category, "PS_RN_BA"))

gencode_STAR_PB_combined=gencode_PB %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PB, gencode_PB, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PB_combined$Sample=gsub("_RN.*", "", gencode_STAR_PB_combined$Sample)

gencode_PB %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PB, gencode_PB, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(58318198/(58318198+2620435021))*100
[1] 2.177065
gencode_STAR_PL_combined=gencode_PL %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PL, gencode_PL, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PL_combined$Sample=gsub("_RN.*", "", gencode_STAR_PL_combined$Sample)

gencode_PL %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PL, gencode_PL, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(196998568/(196998568+1864812120))*100
[1] 9.554639
gencode_STAR_PM_combined=gencode_PM %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PM, gencode_PM, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PM_combined$Sample=gsub("_RN.*", "", gencode_STAR_PM_combined$Sample)

gencode_PM %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PM, gencode_PM, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(152994840/(152994840+2618897827))*100
[1] 5.519508
gencode_STAR_PS_combined=gencode_PS %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PS, gencode_PS, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PS_combined$Sample=gsub("_RN.*", "", gencode_STAR_PS_combined$Sample)

gencode_PS %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PS, gencode_PS, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(217314524/(217314524+1425300221))*100
[1] 13.22979

Defines the function for use in creating the y-axis labels for the plots.

everysecond=function(x){
  x=sort(unique(x))
  x[seq(2, length(x), 2)]=""
  x
}

Summarization of the occurrence of a top 5 Species across all top 5 for each collection method.

gencode_PB_long_top5_summary=gencode_PB %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

gencode_PL_long_top5_summary=gencode_PL %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

gencode_PM_long_top5_summary=gencode_PM %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

gencode_PS_long_top5_summary=gencode_PS %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

An example of summarized data format manipulation.

gencode_PB_long_top5_summary

Plots

Brain (PB)

# Counts
# ggplot()+
#   geom_col(data=gencode_PB_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PB_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PB_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PB_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), fill=Taxa), color="black")+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Occurrence")+  
#   ylab("Taxa")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n)), fill="black", color="black", alpha=0.5)+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), label=n), hjust=-0.2)+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Occurrence")+  
#   ylab("Taxa")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n, fill=Taxa), color="black")+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1), angle=60, hjust=1),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Taxa")+  
#   ylab("Occurrence")

# Taxa Occurrence
# ggplot()+
#   geom_segment(data=gencode_PB_long_top5_summary, aes(y=reorder(Taxa, n), yend=reorder(Taxa, n), x=0, xend=n),
#                color="black")+
#   geom_point(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), color=Taxa), size=3) +
#   scale_color_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Occurrence")+  
#   ylab("Taxa")

# Counts
brain1=ggplot()+
  geom_col(data=gencode_PB_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PB_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(10)), name="Taxa", 
                    limits=c("Babesia bovis", "Clostridium baratii", "Clostridium perfringens", "Myroides phaeus",
                             "Paeniclostridium sordellii", "Paraclostridium bifermentans",
                             "Proteus mirabilis", "Romboutsia hominis", "Romboutsia ilealis", 
                             "Viridibacillus sp. JNUCC-6"),
                    labels=c("B. bovis", "C. baratii", "C. perfringens", "M. phaeus",
                             "P. sordellii", "P. bifermentans",
                             "P. mirabilis", "R. hominis", "R. ilealis", 
                             "V. sp. JNUCC-6"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=5))

# Taxa Occurrence
brain2=ggplot()+
  geom_col(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.75), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
brain3=ggplot()+
  geom_col(data=gencode_STAR_PB_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PB_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))

Combined Taxa Figure

brain_patchwork=(brain3 + brain1) / brain2 + plot_layout(nrow=2)
brain_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_brain_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)

Lung (PL)

# Counts
# ggplot()+
#   geom_col(data=gencode_PL_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PL_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PL_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PL_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PL_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PL_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Counts
lung1=ggplot()+
  geom_col(data=gencode_PL_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PL_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(15)), name="Taxa", 
                    limits=c("Anaerostipes hadrus", "Clostridium baratii", "Clostridium bornimense", 
                             "Clostridium botulinum", "Clostridium cellulovorans", "Clostridium drakei",
                             "Clostridium novyi", "Clostridium sp. JN-9", "Clostridium thermarum", 
                             "Paeniclostridium sordellii", "Paraclostridium bifermentans",
                             "Peptacetobacter hiranonis", "Romboutsia hominis", "Romboutsia ilealis", 
                             "Romboutsia sp. CE17"),
                    labels=c("A. hadrus", "C. baratii", "C. bornimense", "C. botulinum", "C. cellulovorans", "C. drakei",
                             "C. novyi", "C. sp. JN-9", "C. thermarum", "P. sordellii", "P. bifermentans",
                             "P. hiranonis", "R. hominis", "R. ilealis", "R. sp. CE17"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=8))

# Taxa Occurrence
lung2=ggplot()+
  geom_col(data=gencode_PL_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PL_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.5), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
lung3=ggplot()+
  geom_col(data=gencode_STAR_PL_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PL_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))

Combined Taxa Figure

lung_patchwork=(lung3 + lung1) / lung2 + plot_layout(nrow=2)
lung_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_lung_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)

Muscle (PM)

# Counts
# ggplot()+
#   geom_col(data=gencode_PM_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PM_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         # legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.title=element_blank(),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PM_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PM_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PM_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PM_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Counts
muscle1=ggplot()+
  geom_col(data=gencode_PM_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PM_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(14)), name="Taxa", 
                    limits=c("Clostridium baratii", "Clostridium chauvoei", "Clostridium isatidis", "Clostridium novyi",
                             "Clostridium septicum", "Haemophilus parainfluenzae", "Ignatzschineria sp. HR5S32", 
                             "Myroides phaeus", "Paeniclostridium sordellii", "Photobacterium damselae",
                             "Photobacterium toruni", "Vagococcus teuberi", "Veillonella atypica", "Veillonella parvula"),
                    labels=c("C. baratii", "C. chauvoei", "C. isatidis", "C. novyi",
                             "C. septicum", "H. parainfluenzae", "I. sp. HR5S32", 
                             "M. phaeus", "P. sordellii", "P. damselae",
                             "P. toruni", "V. teuberi", "V. atypica", "V. parvula"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=7))

# Taxa Occurrence
muscle2=ggplot()+
  geom_col(data=gencode_PM_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PM_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.5), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
muscle3=ggplot()+
  geom_col(data=gencode_STAR_PM_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PM_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))

Combined Taxa Figure

muscle_patchwork=(muscle3 + muscle1) / muscle2 + plot_layout(nrow=2)
muscle_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_muscle_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)

Blood (PS)

# Counts
# ggplot()+
#   geom_col(data=gencode_PS_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PS_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         # legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.title=element_blank(),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PS_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PS_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PS_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PS_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Counts
blood1=ggplot()+
  geom_col(data=gencode_PS_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PS_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(21)), name="Taxa", 
                    limits=c("Anaerostipes hadrus", "Bacteroides salyersiae", "Blautia obeum", "Blautia wexlerae",
                             "Carnobacterium divergens", "Clostridium baratii", "Clostridium gasigenes", 
                             "Clostridium manihotivorum", "Clostridium novyi", "Clostridium perfringens", 
                             "Clostridium septicum", "Enterobacter hormaechei", "Ewingella americana", 
                             "Limnobaculum parvum", "Paeniclostridium sordellii", "Phocaeicola dorei", 
                             "Pseudomonas lundensis", "Romboutsia hominis", "Romboutsia ilealis", "Rouxiella badensis",
                             "Vagococcus teuberi"),
                    labels=c("A. hadrus", "B. salyersiae", "B. obeum", "B. wexlerae",
                             "C. divergens", "C. baratii", "C. gasigenes", 
                             "C. manihotivorum", "C. novyi", "C. perfringens", 
                             "C. speticum", "E. hormaechei", "E. americana", 
                             "L. parvum", "P. sordellii", "P. dorei", 
                             "P. lundensis", "R. hominis", "R. ilealis", "R. badensis",
                             "V. teuberi"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=11))

# Taxa Occurrence
blood2=ggplot()+
  geom_col(data=gencode_PS_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PS_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.5), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
blood3=ggplot()+
  geom_col(data=gencode_STAR_PS_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PS_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))

Combined Taxa Figure

blood_patchwork=(blood3 + blood1) / blood2 + plot_layout(nrow=2)
blood_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_blood_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)

---
title: "FAA GEN analysis: Postmortem Project, Taxonomy Visualization"
author: Christopher J. Tracy
date: 2023.08.04-2023.08.18
output:
  html_notebook:
editor_options:
  markdown:
    wrap: 144
---

**Libraries**
===
Load the required libraries.
```{r}
library(tidyverse)
library(ggplot2)
library(scales)
library(pals)
library(ggrepel)
library(patchwork)

citation("tidyverse")
citation("ggplot2")
citation("scales")
citation("pals")
citation("ggrepel")
citation("patchwork")

sessionInfo()
```

**Data Import and Setup**
===
Reads the Gencode summarized data files from Kraken Output (https://github.com/npbhavya/Kraken2-output-manipulation) into R.
```{r warning=FALSE}
gencode_PB=read.csv(file="gencode_PM_PB_species_kraken_summary", header=TRUE)
gencode_PL=read.csv(file="gencode_PM_PL_species_kraken_summary", header=TRUE)
gencode_PM=read.csv(file="gencode_PM_PM_species_kraken_summary", header=TRUE)
gencode_PS=read.csv(file="gencode_PM_PS_species_kraken_summary", header=TRUE)
gencode_star=read.delim(file="star_alignment_plot.tsv", header=TRUE, sep="\t")
gencode_PB$PM_14_PB_RN_BA_220606=as.integer(gencode_PB$PM_14_PB_RN_BA_220606)
gencode_PB$PM_58_PB_RN_BA_220916=as.integer(gencode_PB$PM_58_PB_RN_BA_220916)
gencode_PL$PM_15_PL_RN_BA_220622=as.integer(gencode_PL$PM_15_PL_RN_BA_220622)
gencode_PL$PM_58_PL_RN_BA_220923=as.integer(gencode_PL$PM_58_PL_RN_BA_220923)
gencode_PM$PM_15_PM_RN_BA_220614=as.integer(gencode_PM$PM_15_PM_RN_BA_220614)
gencode_PM$PM_58_PM_RN_BA_220920=as.integer(gencode_PM$PM_58_PM_RN_BA_220920)
gencode_PS$PM_17_PS_RN_BA_220628=as.integer(gencode_PS$PM_17_PS_RN_BA_220628)
gencode_PS$PM_58_PS_RN_BA_220921=as.integer(gencode_PS$PM_58_PS_RN_BA_220921)
```

Pivots the data from wide to long format, renames the new columns, arranges the columns in ascending order by sample name and descending order by number of counts, groups the rows by sample, removes the "Homo" rows, slices the top 5 rows for each sample, and removes all information from the sample codes besides the project identifier, the subject number, and the collection/extraction method identifier.
```{r}
gencode_PB_long_top5=gencode_PB %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PB_long_top5$Sample=gsub("_RN.*", "", gencode_PB_long_top5$Sample)
sum(gencode_PB_long_top5$Counts)

gencode_PL_long_top5=gencode_PL %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PL_long_top5$Sample=gsub("_RN.*", "", gencode_PL_long_top5$Sample)
sum(gencode_PL_long_top5$Counts)

gencode_PM_long_top5=gencode_PM %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PM_long_top5$Sample=gsub("_RN.*", "", gencode_PM_long_top5$Sample)
sum(gencode_PM_long_top5$Counts)

gencode_PS_long_top5=gencode_PS %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5)
gencode_PS_long_top5$Sample=gsub("_RN.*", "", gencode_PS_long_top5$Sample)
sum(gencode_PS_long_top5$Counts)
```

Examples of before and after data format manipulation.
```{r}
head(gencode_PB, n=10)
head(gencode_PB_long_top5, n=10)
```

```{r}
gencode_STAR_PB=gencode_star %>%
  filter(str_detect(Category, "PB_RN_BA"))
gencode_STAR_PL=gencode_star %>%
  filter(str_detect(Category, "PL_RN_BA"))
gencode_STAR_PM=gencode_star %>%
  filter(str_detect(Category, "PM_RN_BA"))
gencode_STAR_PS=gencode_star %>%
  filter(str_detect(Category, "PS_RN_BA"))

gencode_STAR_PB_combined=gencode_PB %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PB, gencode_PB, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PB_combined$Sample=gsub("_RN.*", "", gencode_STAR_PB_combined$Sample)

gencode_PB %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PB, gencode_PB, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(58318198/(58318198+2620435021))*100

gencode_STAR_PL_combined=gencode_PL %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PL, gencode_PL, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PL_combined$Sample=gsub("_RN.*", "", gencode_STAR_PL_combined$Sample)

gencode_PL %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PL, gencode_PL, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(196998568/(196998568+1864812120))*100

gencode_STAR_PM_combined=gencode_PM %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PM, gencode_PM, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PM_combined$Sample=gsub("_RN.*", "", gencode_STAR_PM_combined$Sample)

gencode_PM %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PM, gencode_PM, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(152994840/(152994840+2618897827))*100

gencode_STAR_PS_combined=gencode_PS %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PS, gencode_PS, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  select(Sample, Contamination, Uniquely_mapped) %>%
  pivot_longer(!Sample, names_to="Category", values_to="Counts")
gencode_STAR_PS_combined$Sample=gsub("_RN.*", "", gencode_STAR_PS_combined$Sample)

gencode_PS %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE))) %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Category=name, Contamination=value) %>%
  left_join(gencode_STAR_PS, gencode_PS, by=join_by(Category==Category)) %>%
  rename(Sample=Category) %>%
  arrange(Sample) %>%
  summarise(across(where(is.numeric), ~ sum(.x, na.rm=TRUE)))
(217314524/(217314524+1425300221))*100
```

Defines the function for use in creating the y-axis labels for the plots.
```{r}
everysecond=function(x){
  x=sort(unique(x))
  x[seq(2, length(x), 2)]=""
  x
}
```

Summarization of the occurrence of a top 5 Species across all top 5 for each collection method.
```{r}
gencode_PB_long_top5_summary=gencode_PB %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

gencode_PL_long_top5_summary=gencode_PL %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

gencode_PM_long_top5_summary=gencode_PM %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())

gencode_PS_long_top5_summary=gencode_PS %>%
  pivot_longer(cols=starts_with("PM")) %>%
  rename(Sample=name, Counts=value) %>%
  arrange(Sample, desc(Counts)) %>% 
  group_by(Sample) %>%
  filter(!str_detect(Taxa, "Homo sapiens")) %>%
  slice(1:5) %>%
  ungroup(Sample) %>%
  group_by(Taxa) %>%
  summarise(n=n())
```

An example of summarized data format manipulation.
```{r}
gencode_PB_long_top5_summary
```

**Plots**
===
### Brain (PB)
```{r warning=FALSE}
# Counts
# ggplot()+
#   geom_col(data=gencode_PB_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PB_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PB_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PB_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), fill=Taxa), color="black")+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Occurrence")+  
#   ylab("Taxa")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n)), fill="black", color="black", alpha=0.5)+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), label=n), hjust=-0.2)+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Occurrence")+  
#   ylab("Taxa")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n, fill=Taxa), color="black")+
#   geom_text(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1), angle=60, hjust=1),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Taxa")+  
#   ylab("Occurrence")

# Taxa Occurrence
# ggplot()+
#   geom_segment(data=gencode_PB_long_top5_summary, aes(y=reorder(Taxa, n), yend=reorder(Taxa, n), x=0, xend=n),
#                color="black")+
#   geom_point(data=gencode_PB_long_top5_summary, aes(x=n, y=reorder(Taxa, n), color=Taxa), size=3) +
#   scale_color_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#   xlab("Occurrence")+  
#   ylab("Taxa")

# Counts
brain1=ggplot()+
  geom_col(data=gencode_PB_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PB_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(10)), name="Taxa", 
                    limits=c("Babesia bovis", "Clostridium baratii", "Clostridium perfringens", "Myroides phaeus",
                             "Paeniclostridium sordellii", "Paraclostridium bifermentans",
                             "Proteus mirabilis", "Romboutsia hominis", "Romboutsia ilealis", 
                             "Viridibacillus sp. JNUCC-6"),
                    labels=c("B. bovis", "C. baratii", "C. perfringens", "M. phaeus",
                             "P. sordellii", "P. bifermentans",
                             "P. mirabilis", "R. hominis", "R. ilealis", 
                             "V. sp. JNUCC-6"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=5))

# Taxa Occurrence
brain2=ggplot()+
  geom_col(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PB_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.75), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
brain3=ggplot()+
  geom_col(data=gencode_STAR_PB_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PB_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))
```

Combined Taxa Figure
```{r}
brain_patchwork=(brain3 + brain1) / brain2 + plot_layout(nrow=2)
brain_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_brain_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)
```

### Lung (PL)
```{r warning=FALSE}
# Counts
# ggplot()+
#   geom_col(data=gencode_PL_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PL_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PL_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PL_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PL_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PL_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Counts
lung1=ggplot()+
  geom_col(data=gencode_PL_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PL_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(15)), name="Taxa", 
                    limits=c("Anaerostipes hadrus", "Clostridium baratii", "Clostridium bornimense", 
                             "Clostridium botulinum", "Clostridium cellulovorans", "Clostridium drakei",
                             "Clostridium novyi", "Clostridium sp. JN-9", "Clostridium thermarum", 
                             "Paeniclostridium sordellii", "Paraclostridium bifermentans",
                             "Peptacetobacter hiranonis", "Romboutsia hominis", "Romboutsia ilealis", 
                             "Romboutsia sp. CE17"),
                    labels=c("A. hadrus", "C. baratii", "C. bornimense", "C. botulinum", "C. cellulovorans", "C. drakei",
                             "C. novyi", "C. sp. JN-9", "C. thermarum", "P. sordellii", "P. bifermentans",
                             "P. hiranonis", "R. hominis", "R. ilealis", "R. sp. CE17"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=8))

# Taxa Occurrence
lung2=ggplot()+
  geom_col(data=gencode_PL_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PL_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.5), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
lung3=ggplot()+
  geom_col(data=gencode_STAR_PL_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PL_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))
```

Combined Taxa Figure
```{r}
lung_patchwork=(lung3 + lung1) / lung2 + plot_layout(nrow=2)
lung_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_lung_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)
```

### Muscle (PM)
```{r warning=FALSE}
# Counts
# ggplot()+
#   geom_col(data=gencode_PM_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PM_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         # legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.title=element_blank(),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PM_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PM_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PM_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PM_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Counts
muscle1=ggplot()+
  geom_col(data=gencode_PM_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PM_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(14)), name="Taxa", 
                    limits=c("Clostridium baratii", "Clostridium chauvoei", "Clostridium isatidis", "Clostridium novyi",
                             "Clostridium septicum", "Haemophilus parainfluenzae", "Ignatzschineria sp. HR5S32", 
                             "Myroides phaeus", "Paeniclostridium sordellii", "Photobacterium damselae",
                             "Photobacterium toruni", "Vagococcus teuberi", "Veillonella atypica", "Veillonella parvula"),
                    labels=c("C. baratii", "C. chauvoei", "C. isatidis", "C. novyi",
                             "C. septicum", "H. parainfluenzae", "I. sp. HR5S32", 
                             "M. phaeus", "P. sordellii", "P. damselae",
                             "P. toruni", "V. teuberi", "V. atypica", "V. parvula"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=7))

# Taxa Occurrence
muscle2=ggplot()+
  geom_col(data=gencode_PM_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PM_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.5), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
muscle3=ggplot()+
  geom_col(data=gencode_STAR_PM_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PM_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))
```

Combined Taxa Figure
```{r}
muscle_patchwork=(muscle3 + muscle1) / muscle2 + plot_layout(nrow=2)
muscle_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_muscle_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)
```

### Blood (PS)
```{r warning=FALSE}
# Counts
# ggplot()+
#   geom_col(data=gencode_PS_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
#   scale_y_discrete(labels=everysecond(gencode_PS_long_top5$Sample), expand=expansion(mult=0.03))+
#   scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"),
#         # legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.title=element_blank(),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))

# Proportion
# ggplot()+
#   geom_col(data=gencode_PS_long_top5, aes(x=Counts, y=Sample, fill=Taxa), color="black", position="fill")+
#   scale_y_discrete(labels=everysecond(gencode_PS_long_top5$Sample))+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="bottom",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Proportion")

# Taxa Occurrence
# ggplot()+
#   geom_col(data=gencode_PS_long_top5_summary, aes(x=n, y=Taxa, fill=Taxa), color="black")+
#   geom_text(data=gencode_PS_long_top5_summary, aes(x=n, y=Taxa, label=n), hjust=-0.2)+
#   scale_fill_manual(values=as.vector(polychrome(26)))+
#   theme_minimal()+
#   theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
#         axis.text.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.x=element_text(color="black", face="bold", size=rel(1)),
#         axis.title.y=element_text(color="black", face="bold", size=rel(1)),
#         panel.border=element_rect(color="black", linewidth=1, fill=NA),
#         strip.background=element_rect(color="black", linewidth=1),
#         strip.text=element_text(color="black", face="bold", size=rel(0.8)),
#         axis.line=element_blank(),
#         axis.ticks=element_line(linewidth=1),
#         legend.position="none",
#         legend.key.size=unit(rel(0.5), "cm"), 
#         legend.title=element_text(color="black", face="bold", size=rel(0.6)),
#         legend.text=element_text(color="black", face="bold", size=rel(0.6)),
#         plot.title=element_text(color="black", face="bold", size=rel(1)),
#         plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
#     xlab("Occurrence")

# Counts
blood1=ggplot()+
  geom_col(data=gencode_PS_long_top5, aes(x=Counts, y=Sample, fill=if_else(Counts>1000000, Taxa, NA)), color="black")+
  scale_y_discrete(labels=everysecond(gencode_PS_long_top5$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(21)), name="Taxa", 
                    limits=c("Anaerostipes hadrus", "Bacteroides salyersiae", "Blautia obeum", "Blautia wexlerae",
                             "Carnobacterium divergens", "Clostridium baratii", "Clostridium gasigenes", 
                             "Clostridium manihotivorum", "Clostridium novyi", "Clostridium perfringens", 
                             "Clostridium septicum", "Enterobacter hormaechei", "Ewingella americana", 
                             "Limnobaculum parvum", "Paeniclostridium sordellii", "Phocaeicola dorei", 
                             "Pseudomonas lundensis", "Romboutsia hominis", "Romboutsia ilealis", "Rouxiella badensis",
                             "Vagococcus teuberi"),
                    labels=c("A. hadrus", "B. salyersiae", "B. obeum", "B. wexlerae",
                             "C. divergens", "C. baratii", "C. gasigenes", 
                             "C. manihotivorum", "C. novyi", "C. perfringens", 
                             "C. speticum", "E. hormaechei", "E. americana", 
                             "L. parvum", "P. sordellii", "P. dorei", 
                             "P. lundensis", "R. hominis", "R. ilealis", "R. badensis",
                             "V. teuberi"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="italic", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=11))

# Taxa Occurrence
blood2=ggplot()+
  geom_col(data=gencode_PS_long_top5_summary, aes(x=reorder(Taxa, n), y=n), fill="black", color="black", alpha=0.75)+
  geom_text(data=gencode_PS_long_top5_summary, aes(x=reorder(Taxa, n), y=n, label=n), vjust=-0.4, size=1.1)+
  scale_x_discrete(expand=expansion(mult=0.02))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(1)),
        axis.text.x=element_text(color="black", face="italic", size=rel(0.5), angle=60, hjust=1, vjust=1),
        # axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_blank(),
        axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.x=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="none",
        legend.key.size=unit(rel(0.5), "cm"), 
        legend.title=element_text(color="black", face="bold", size=rel(0.6)),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  xlab("Taxa")+  
  ylab("Occurrence")

# Counts
blood3=ggplot()+
  geom_col(data=gencode_STAR_PS_combined, aes(x=Counts, y=Sample, fill=Category), color="black")+
  scale_y_discrete(labels=everysecond(gencode_STAR_PS_combined$Sample), expand=expansion(mult=0.03))+
  scale_x_continuous(labels=scales::label_number_si(), expand=expansion(mult=0.03))+
  scale_fill_manual(values=as.vector(polychrome(2)), name="Category",
                    limits=c("Contamination", "Uniquely_mapped"),
                    labels=c("Contaminants", "Uniquely Mapped"))+
  theme_minimal()+
  theme(axis.text.y=element_text(color="black", face="bold", size=rel(0.75)),
        axis.text.x=element_text(color="black", face="bold", size=rel(1)),
        axis.title.x=element_text(color="black", face="bold", size=rel(1)),
        # axis.title.y=element_text(color="black", face="bold", size=rel(1)),
        axis.title.y=element_blank(),
        panel.border=element_rect(color="black", linewidth=1, fill=NA),
        panel.grid.major.y=element_blank(),
        strip.background=element_rect(color="black", linewidth=1),
        strip.text=element_text(color="black", face="bold", size=rel(0.8)),
        axis.line=element_blank(),
        axis.ticks=element_line(linewidth=1),
        legend.position="bottom",
        legend.key.size=unit(rel(0.25), "cm"),
        # legend.title=element_text(color="black", face="bold", size=rel(1)),
        legend.title=element_blank(),
        legend.text=element_text(color="black", face="bold", size=rel(0.6)),
        plot.title=element_text(color="black", face="bold", size=rel(1)),
        plot.margin=unit(c(0.1, 0.3, 0.1, 0.1), "cm"))+
  guides(fill=guide_legend(nrow=1))
```

Combined Taxa Figure
```{r}
blood_patchwork=(blood3 + blood1) / blood2 + plot_layout(nrow=2)
blood_patchwork + plot_annotation(tag_levels="A")
ggsave("PM_blood_taxonomy_plots.png", width=7, height=10, unit="in", dpi=320)
```