DESeq2_MACS_Heatmap
Figure ED1 - Heatmap
This is a heatmap and cluster analysis of the adult bulk RNA sequencing data to show cell type specificity.
tpm <- read.csv("../1.align/expression/TPM.csv", stringsAsFactors = TRUE, row.names = 1)
metadata <- read.csv("metadata.csv", stringsAsFactors = TRUE)
metadata$Trt <- factor(metadata$Trt, levels=c("SAL","LPS"))
metadata$condition1 <- as.factor(paste(metadata$Group, metadata$Region, metadata$Trt, sep = "."))
metadata$condition2 <- factor(paste(metadata$Group, metadata$Trt, sep = "."), levels=c("CON.SAL", "CON.LPS", "MIA.SAL", "MIA.LPS"))
rownames(metadata) <- metadata$ID
all.equal(colnames(tpm), as.character(metadata$ID))## [1] TRUEgenes <- gsub("\\..*","",rownames(tpm))
symbols <- mapIds(org.Mm.eg.db, keys=genes, column="SYMBOL", keytype="ENSEMBL", multiVals="first")## 'select()' returned 1:many mapping between keys and columnslog_tpm <- log(tpm+1)
log_tpm$symbol <- symbolsHeatmap
library(pheatmap)
GOI <- c("Hexb", "Tyrobp", "Tmem119", "Fcrls", "Gpr34", "P2ry12", "P2ry13", "Celf4", "Tubb3", "Eno2", "Aqp4", "Gfap", "Aldh1l1", "Mog", "Cldn11", "Olig1", "Lyve1", "Ttr", "Cd163", "Camp", "Cd36", "Clec4d")
p <- log_tpm[match(GOI, log_tpm$symbol),]
pheatmap(p[,1:24], cluster_rows = FALSE, cluster_cols = TRUE, border_color = NA, main = "Heatmap of Cell Type Specific Markers", labels_row = p$symbol)
Cluster
library(ggfortify)
tree = hclust(dist(t(log_tpm[,1:24])), method = "average")
plot(tree, main = "hclust, average log(TPM)", sub="", xlab="", cex.lab = 1, cex.axis = 1, cex.main = 1)
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggfortify_0.4.11 pheatmap_1.0.12
## [3] org.Mm.eg.db_3.12.0 AnnotationDbi_1.52.0
## [5] DESeq2_1.30.1 SummarizedExperiment_1.20.0
## [7] Biobase_2.50.0 MatrixGenerics_1.2.1
## [9] matrixStats_0.58.0 GenomicRanges_1.42.0
## [11] GenomeInfoDb_1.26.7 IRanges_2.24.1
## [13] S4Vectors_0.28.1 BiocGenerics_0.36.1
## [15] cowplot_1.1.1 forcats_0.5.1
## [17] stringr_1.4.0 dplyr_1.0.6
## [19] purrr_0.3.4 readr_1.4.0
## [21] tidyr_1.1.3 tibble_3.1.2
## [23] ggplot2_3.3.3 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fs_1.5.0 lubridate_1.7.10
## [4] bit64_4.0.5 RColorBrewer_1.1-2 httr_1.4.2
## [7] tools_4.0.3 backports_1.2.1 bslib_0.2.5.1
## [10] utf8_1.2.1 R6_2.5.0 DBI_1.1.1
## [13] colorspace_2.0-1 withr_2.4.2 gridExtra_2.3
## [16] tidyselect_1.1.1 bit_4.0.4 compiler_4.0.3
## [19] cli_2.5.0 rvest_1.0.0 xml2_1.3.2
## [22] DelayedArray_0.16.3 sass_0.4.0 scales_1.1.1
## [25] genefilter_1.72.1 digest_0.6.27 rmarkdown_2.8
## [28] XVector_0.30.0 pkgconfig_2.0.3 htmltools_0.5.1.1
## [31] highr_0.9 fastmap_1.1.0 dbplyr_2.1.1
## [34] rlang_0.4.11 readxl_1.3.1 rstudioapi_0.13
## [37] RSQLite_2.2.7 jquerylib_0.1.4 generics_0.1.0
## [40] jsonlite_1.7.2 BiocParallel_1.24.1 RCurl_1.98-1.3
## [43] magrittr_2.0.1 GenomeInfoDbData_1.2.4 Matrix_1.3-3
## [46] Rcpp_1.0.6 munsell_0.5.0 fansi_0.5.0
## [49] lifecycle_1.0.0 stringi_1.6.2 yaml_2.2.1
## [52] zlibbioc_1.36.0 blob_1.2.1 grid_4.0.3
## [55] crayon_1.4.1 lattice_0.20-44 splines_4.0.3
## [58] haven_2.4.1 annotate_1.68.0 hms_1.1.0
## [61] locfit_1.5-9.4 knitr_1.33 pillar_1.6.1
## [64] geneplotter_1.68.0 reprex_2.0.0 XML_3.99-0.6
## [67] glue_1.4.2 evaluate_0.14 modelr_0.1.8
## [70] vctrs_0.3.8 cellranger_1.1.0 gtable_0.3.0
## [73] assertthat_0.2.1 cachem_1.0.5 xfun_0.23
## [76] xtable_1.8-4 broom_0.7.6 survival_3.2-11
## [79] memoise_2.0.0 ellipsis_0.3.2