Gene expression heatmap using ARCHS4 data
2023-07-14
Last updated: 2023-07-14
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Knit directory: muse/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7188c55 | Dave Tang | 2023-07-14 | pheatmap resolution |
| html | e2d73eb | Dave Tang | 2023-07-14 | Build site. |
| Rmd | 3a5ec3f | Dave Tang | 2023-07-14 | Interactive heatmap |
| html | 7fa8b41 | Dave Tang | 2023-07-13 | Build site. |
| Rmd | 922aa56 | Dave Tang | 2023-07-13 | ARCHS4 heatmap |
Prepare data using base R.
lapply(
list.files("data/archs4/cancer", 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
12 CCND1 System.Cardiovascular System.Heart.VALVE 10.62560 11.68490
28 CCND1 System.Cardiovascular System.Heart.HEART 5.87724 10.15820
30 CCND1 System.Cardiovascular System.Heart.VENTRICLE 9.54469 10.37180
36 CCND1 System.Cardiovascular System.Heart.ATRIUM 8.44515 9.67321
5 CCND1 System.Connective Tissue.Bone.OSTEOBLAST 11.30840 12.09570
18 CCND1 System.Connective Tissue.Adipose tissue.ADIPOCYTE 8.38312 10.48580
median q3 max root system organ
12 12.0648 12.5311 13.7986 System Cardiovascular System Heart
28 10.9207 11.5210 12.8617 System Cardiovascular System Heart
30 10.8446 11.2841 11.9118 System Cardiovascular System Heart
36 10.5234 11.0560 11.4873 System Cardiovascular System Heart
5 12.6214 13.2789 14.0211 System Connective Tissue Bone
18 11.7684 12.7769 14.1867 System Connective Tissue Adipose tissue
tissue
12 Valve
28 Heart
30 Ventricle
36 Atrium
5 Osteoblast
18 AdipocyteBack to wide format.
my_df |>
dplyr::select(gene, median, tissue) |>
tidyr::pivot_wider(names_from = tissue, values_from = median) -> my_df_wideConvert 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
Valve Cardiovascular System
Heart Cardiovascular System
Ventricle Cardiovascular System
Atrium Cardiovascular System
Osteoblast Connective Tissue
Adipocyte Connective TissueHeatmap with system annotation.
pheatmap(my_mat, annotation_col = sample_anno)
Interactive heatmap.
plot_ly(
x=colnames(my_mat),
y=rownames(my_mat),
z = my_mat,
colors = colorRamp(c("green", "red")),
type = "heatmap"
)
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] plotly_4.10.2 ggplot2_3.4.2 pheatmap_1.0.12 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyr_1.3.0 sass_0.4.5 utf8_1.2.3 generics_0.1.3
[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] crosstalk_1.2.0 viridisLite_0.4.1 scales_1.2.1 lazyeval_0.2.2
[25] jquerylib_0.1.4 cli_3.6.1 rlang_1.1.0 ellipsis_0.3.2
[29] munsell_0.5.0 withr_2.5.0 cachem_1.0.7 yaml_2.3.7
[33] tools_4.3.0 dplyr_1.1.2 colorspace_2.1-0 httpuv_1.6.9
[37] vctrs_0.6.2 R6_2.5.1 lifecycle_1.0.3 git2r_0.32.0
[41] stringr_1.5.0 htmlwidgets_1.6.2 fs_1.6.2 pkgconfig_2.0.3
[45] callr_3.7.3 pillar_1.9.0 bslib_0.4.2 later_1.3.0
[49] gtable_0.3.3 data.table_1.14.8 glue_1.6.2 Rcpp_1.0.10
[53] highr_0.10 xfun_0.39 tibble_3.2.1 tidyselect_1.2.0
[57] rstudioapi_0.14 knitr_1.42 farver_2.1.1 htmltools_0.5.5
[61] rmarkdown_2.21 compiler_4.3.0 getPass_0.2-2