Last updated: 2023-07-13

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File Version Author Date Message
Rmd 922aa56 Dave Tang 2023-07-13 ARCHS4 heatmap

Prepare data using base R.

lapply(
  list.files("data/archs4", pattern = ".csv$", full.names = TRUE),
  function(x){
    cbind(gene = sub("\\.\\w+$", "", basename(x)), read.csv(x))
  }
) |>
  do.call("rbind", args = _) -> my_df

# Split `id` column.
do.call("rbind", strsplit(x = my_df$id, split = "\\.")) |>
  as.data.frame() -> id_split

colnames(id_split) <- c('root', 'system', 'organ', 'tissue')

# Rename tissues.
cap_first <- function(x){
  s <- strsplit(x, "")[[1]][1]
  return(sub(s, toupper(s), x))
}

id_split$tissue <- tolower(id_split$tissue)
id_split$tissue <- sapply(id_split$tissue, cap_first)

my_df <- cbind(my_df, id_split)

# Order `my_df` by system.
my_df <- my_df[order(my_df$gene, my_df$system), ]
my_df$tissue <- factor(my_df$tissue, levels = unique(my_df$tissue))

head(my_df)
    gene                                                id      min       q1
8  ADH1C      System.Cardiovascular System.Heart.VENTRICLE 1.935320 4.166910
11 ADH1C         System.Cardiovascular System.Heart.ATRIUM 0.113644 2.419680
14 ADH1C          System.Cardiovascular System.Heart.HEART 0.113644 0.113644
15 ADH1C          System.Cardiovascular System.Heart.VALVE 0.113644 0.113644
6  ADH1C   System.Connective Tissue.Adipose tissue.ADIPOSE 0.113644 4.166910
12 ADH1C System.Connective Tissue.Adipose tissue.ADIPOCYTE 0.113644 2.162720
    median      q3     max   root                system          organ
8  4.93037 5.78635 7.32913 System Cardiovascular System          Heart
11 4.08298 4.84279 5.79763 System Cardiovascular System          Heart
14 3.17370 5.28658 7.83074 System Cardiovascular System          Heart
15 3.11494 4.04248 5.81340 System Cardiovascular System          Heart
6  5.53310 7.32318 8.75978 System     Connective Tissue Adipose tissue
12 3.89584 6.07935 8.56720 System     Connective Tissue Adipose tissue
      tissue
8  Ventricle
11    Atrium
14     Heart
15     Valve
6    Adipose
12 Adipocyte

Back to wide format.

my_df |>
  dplyr::select(gene, median, tissue) |>
  tidyr::pivot_wider(names_from = tissue, values_from = median) -> my_df_wide

Convert to matrix and plot.

my_mat <- as.matrix(my_df_wide[, -1])
row.names(my_mat) <- my_df_wide$gene

pheatmap(my_mat)

Create sample annotation.

my_order <- colnames(my_mat)

my_df |>
  dplyr::select(system, tissue) |>
  dplyr::distinct() |>
  dplyr::arrange(match(tissue, my_order)) |>
  dplyr::select(-tissue) -> sample_anno

row.names(sample_anno) <- my_order
head(sample_anno)
                         system
Ventricle Cardiovascular System
Atrium    Cardiovascular System
Heart     Cardiovascular System
Valve     Cardiovascular System
Adipose       Connective Tissue
Adipocyte     Connective Tissue

Heatmap with system annotation.

pheatmap(my_mat, annotation_col = sample_anno)


sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] 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   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
[1] pheatmap_1.0.12 workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] sass_0.4.5         utf8_1.2.3         generics_0.1.3     tidyr_1.3.0       
 [5] stringi_1.7.12     digest_0.6.31      magrittr_2.0.3     evaluate_0.20     
 [9] grid_4.3.0         RColorBrewer_1.1-3 fastmap_1.1.1      rprojroot_2.0.3   
[13] jsonlite_1.8.5     processx_3.8.1     whisker_0.4.1      ps_1.7.5          
[17] promises_1.2.0.1   httr_1.4.5         purrr_1.0.1        fansi_1.0.4       
[21] scales_1.2.1       jquerylib_0.1.4    cli_3.6.1          rlang_1.1.0       
[25] munsell_0.5.0      withr_2.5.0        cachem_1.0.7       yaml_2.3.7        
[29] tools_4.3.0        dplyr_1.1.2        colorspace_2.1-0   httpuv_1.6.9      
[33] vctrs_0.6.2        R6_2.5.1           lifecycle_1.0.3    git2r_0.32.0      
[37] stringr_1.5.0      fs_1.6.2           pkgconfig_2.0.3    callr_3.7.3       
[41] pillar_1.9.0       bslib_0.4.2        later_1.3.0        gtable_0.3.3      
[45] glue_1.6.2         Rcpp_1.0.10        highr_0.10         xfun_0.39         
[49] tibble_3.2.1       tidyselect_1.2.0   rstudioapi_0.14    knitr_1.42        
[53] farver_2.1.1       htmltools_0.5.5    rmarkdown_2.21     compiler_4.3.0    
[57] getPass_0.2-2