RNA_CorrelationHeatMap
2026-01-14
Last updated: 2026-01-22
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Knit directory: CrossSpecies_CM_Diff_RNA/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7299279 | John D. Hurley | 2026-01-22 | wflow_publish("analysis/RNA_CorrelationHeatMap.Rmd") |
| Rmd | b4bc4cd | John D. Hurley | 2026-01-22 | Updating Index HeatMap |
| html | 9aa0597 | John D. Hurley | 2026-01-22 | Website Update HeatMap |
| Rmd | 88bce45 | John D. Hurley | 2026-01-22 | Correcting Eval = FALSE statement |
| Rmd | 59e658c | John D. Hurley | 2026-01-22 | Test |
| html | 59e658c | John D. Hurley | 2026-01-22 | Test |
| html | 9264bd4 | John D. Hurley | 2026-01-21 | Change in RMD organization |
| Rmd | 0725194 | John D. Hurley | 2026-01-21 | Final QC Heatmap |
| Rmd | 5d2c47d | John D. Hurley | 2026-01-21 | Large Changes to Flow and Organization to QC |
####Library Loading####
library("edgeR")Loading required package: limmalibrary("ggplot2")
library("tibble")
library("dplyr")
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary("ggrepel")
library("readr")
library("org.Hs.eg.db")Loading required package: AnnotationDbiLoading required package: stats4Loading required package: BiocGenericsLoading required package: generics
Attaching package: 'generics'The following object is masked from 'package:dplyr':
explainThe following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'The following object is masked from 'package:dplyr':
combineThe following object is masked from 'package:limma':
plotMAThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, aperm, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.minLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.Loading required package: IRangesLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:dplyr':
first, renameThe following object is masked from 'package:utils':
findMatchesThe following objects are masked from 'package:base':
expand.grid, I, unname
Attaching package: 'IRanges'The following objects are masked from 'package:dplyr':
collapse, desc, sliceThe following object is masked from 'package:grDevices':
windows
Attaching package: 'AnnotationDbi'The following object is masked from 'package:dplyr':
selectlibrary("AnnotationDbi")
library("pheatmap")
library("Cormotif")Loading required package: affylibrary("tidyverse")── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.1 ✔ stringr 1.5.2
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.1.0 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ lubridate::%within%() masks IRanges::%within%()
✖ IRanges::collapse() masks dplyr::collapse()
✖ Biobase::combine() masks BiocGenerics::combine(), dplyr::combine()
✖ IRanges::desc() masks dplyr::desc()
✖ tidyr::expand() masks S4Vectors::expand()
✖ dplyr::filter() masks stats::filter()
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✖ lubridate::second<-() masks S4Vectors::second<-()
✖ AnnotationDbi::select() masks dplyr::select()
✖ IRanges::slice() masks dplyr::slice()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("workflowr")
####Loadind R Objects####
RNA_fc_df <- readRDS("data/QC/RNA_fc_df.RDS")
RNA_fc <- readRDS("data/QC/RNA_fc_Ensemble.RDS")
RNA_log2cpm <- readRDS("data/QC/RNA_log2cpm_Ensemble.RDS")
Filt_RMG0_RNA_fc <- readRDS("data/QC/Filt_RMG0_RNA_fc_Ensemble.RDS")
Filt_RMG0_RNA_log2cpm <- readRDS("data/QC/Filt_RMG0_RNA_log2cpm_Ensemble.RDS")
RNA_Metadata <- readRDS("data/QC/RNA_Metadata.RDS")
cor_Filt_RMG0_RNA_log2cpm <- readRDS("data/QC/Cor_Filt_RMG0_RNA_log2cpm.RDS")
Cor_metadata <- readRDS("data/QC/Cor_metadata.RDS")
ann_colors <- readRDS("data/QC/ann_colors.RDS")
RNA_Metadata_No4 <- readRDS("data/QC/RNA_Metatdata_No4.RDS")
Filt_RMG0_RNA_fc_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_fc_NoD4_Ensemble.RDS")
Filt_RMG0_RNA_log2cpm_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_log2cpm_NoD4_Ensemble.RDS")
Cor_metadata_No4 <- readRDS("data/QC/Cor_metadata_No4.RDS")
cor_Filt_RMG0_RNA_log2cpm_NoD4 <- readRDS("data/QC/cor_Filt_RMG0_RNA_log2cpm_NoD4.RDS")
ann_colors_No4 <- readRDS("data/QC/ann_colors_no4.RDS")chimp_RNA_pantro5_fc <- read.delim("~/diff_timeline_tes/RNA/Run1_Run2_Concat/featurecounts/c_samples_counts.txt", comment.char="#",row.names=1)
human_RNA_hg38_fc <- read.delim("~/diff_timeline_tes/RNA/Run1_Run2_Concat/featurecounts/h_samples_counts.txt", comment.char="#",row.names=1)
human_df <- human_RNA_hg38_fc %>%
tibble::rownames_to_column("gene")
chimp_df <- chimp_RNA_pantro5_fc %>%
tibble::rownames_to_column("gene")
RNA_joined_fc <- left_join(human_df, chimp_df, by = "gene")
#To keep only OrthoGenes and sample columns
RNA_fc <- RNA_joined_fc[ , !(names(RNA_joined_fc) %in% c("gene","Chr.x","Start.x","End.x","Strand.x","Length.x","Geneid.x","Chr.y","Start.y","End.y","Strand.y","Length.y","Geneid.y"))]
# 89 columns; 7 x 6 = 42 Human Exp, 7 x 6 = 42 Chimp Exp, 4 Human Replicate
rownames(RNA_fc) <- RNA_joined_fc$gene
#Rename column Names to More Useful Info
col_names <- c("H28126_D0",
"H28126_D2",
"H28126_D4",
"H28126_D5",
"H28126_D15",
"H28126_D30",
"H17_D0",
"H17_D2",
"H17_D4",
"H17_D5",
"H17_D15",
"H17_D30",
"H78_D0",
"H78_D2",
"H78_D4",
"H78_D5",
"H78_D15",
"H78_D30",
"H20682_D0",
"H20682_D2",
"H20682_D4",
"H20682_D5",
"H20682_D15",
"H20682_D30",
"H22422_D0",
"H22422_D2",
"H22422_D4",
"H22422_D5",
"H22422_D15",
"H22422_D30",
"H21792_D0",
"H21792_D2",
"H21792_D4",
"H21792_D5",
"H21792_D15",
"H21792_D30",
"H24280_D0",
"H24280_D2",
"H24280_D4",
"H24280_D5",
"H24280_D15",
"H24280_D30",
"H20682R_D0",
"H20682R_D2",
"H20682R_D5",
"H20682R_D30",
"C3649_D0",
"C3649_D2",
"C3649_D4",
"C3649_D5",
"C3649_D15",
"C3649_D30",
"C4955_D0",
"C4955_D2",
"C4955_D4",
"C4955_D5",
"C4955_D15",
"C4955_D30",
"C3651_D0",
"C3651_D2",
"C3651_D4",
"C3651_D5",
"C3651_D15",
"C3651_D30",
"C40210_D0",
"C40210_D2",
"C40210_D4",
"C40210_D5",
"C40210_D15",
"C40210_D30",
"C8861_D0",
"C8861_D2",
"C8861_D4",
"C8861_D5",
"C8861_D15",
"C8861_D30",
"C40280_D0",
"C40280_D2",
"C40280_D4",
"C40280_D5",
"C40280_D15",
"C40280_D30",
"C3647_D0",
"C3647_D2",
"C3647_D4",
"C3647_D5",
"C3647_D15",
"C3647_D30"
)
colnames(RNA_fc) <- col_names
dim(RNA_fc)[1] 44125 88# saveRDS(RNA_fc,"data/QC/RNA_fc_Ensemble.RDS")
sum(duplicated(rownames(RNA_fc)))[1] 0ensembl_ids_unfilt <- rownames(RNA_fc)
entrez_ids_unfilt <- mapIds(org.Hs.eg.db,
keys = ensembl_ids_unfilt,
column = "ENTREZID",
keytype = "ENSEMBL",
multiVals = "first")'select()' returned 1:many mapping between keys and columnssymbol_ids_unfilt <- mapIds(org.Hs.eg.db,
keys = ensembl_ids_unfilt,
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")'select()' returned 1:many mapping between keys and columnsRNA_fc_df <- as.data.frame(RNA_fc)
RNA_fc_df <- RNA_fc_df %>%
rownames_to_column(var = "Ensemble") %>%
dplyr::mutate(
Entrez_ID = entrez_ids_unfilt,
Symbol = symbol_ids_unfilt
) %>%
dplyr::select(
Ensemble, # 1st column
Entrez_ID, # 2nd column
Symbol, # 3rd column
everything() # rest unchanged
)
# saveRDS(RNA_fc_df,"data/QC/RNA_fc_df.RDS")#####Unfiltered####
RNA_log2cpm <- cpm(RNA_fc,log=TRUE)
print(hist(RNA_log2cpm, main = "Histogram of all counts (unfiltered)",
xlab =expression("Log"[2]*" counts-per-million"), col =4 ))
| Version | Author | Date |
|---|---|---|
| 9aa0597 | John D. Hurley | 2026-01-22 |
$breaks
[1] -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
$counts
[1] 2026734 332340 206543 159084 134510 130097 148151 195453 219757
[10] 174507 94391 39658 15034 5070 1267 310 70 21
[19] 3
$density
[1] 5.219506e-01 8.558846e-02 5.319160e-02 4.096935e-02 3.464074e-02
[6] 3.350425e-02 3.815375e-02 5.033557e-02 5.659464e-02 4.494128e-02
[11] 2.430878e-02 1.021324e-02 3.871749e-03 1.305691e-03 3.262941e-04
[16] 7.983518e-05 1.802730e-05 5.408190e-06 7.725985e-07
$mids
[1] -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5
[16] 12.5 13.5 14.5 15.5
$xname
[1] "RNA_log2cpm"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"boxplot(RNA_log2cpm, main = "Boxplots of log cpm per sample
(unfiltered)", xaxt = "n", xlab= "")
axis(1,
at = 1:length(col_names), # positions (one per sample)
labels = col_names, # your labels vector
las = 2, # rotate text vertically (like srt=90)
cex.axis = 0.3) # shrink label size
| Version | Author | Date |
|---|---|---|
| 9aa0597 | John D. Hurley | 2026-01-22 |
# saveRDS(RNA_log2cpm,"data/QC/RNA_log2cpm_Ensemble.RDS")#####RowMu>0####
row_means <- rowMeans(RNA_log2cpm)
Filt_RMG0_RNA_fc <- RNA_fc[row_means >0,]
Filt_RMG0_RNA_log2cpm <- cpm(Filt_RMG0_RNA_fc,log=TRUE)
hist(Filt_RMG0_RNA_log2cpm, main = "Histogram of filtered counts using rowMeans > 0 method",
xlab =expression("Log"[2]*" counts-per-million"), col =5 )
| Version | Author | Date |
|---|---|---|
| 9aa0597 | John D. Hurley | 2026-01-22 |
# saveRDS(Filt_RMG0_RNA_fc,"data/QC/Filt_RMG0_RNA_fc_Ensemble.RDS")
# saveRDS(Filt_RMG0_RNA_log2cpm,"data/QC/Filt_RMG0_RNA_log2cpm_Ensemble.RDS")boxplot(Filt_RMG0_RNA_log2cpm, main = "Boxplots of log cpm per sample (RowMeans>0)",xaxt = "n", xlab= "")
axis(1,
at = 1:length(col_names), # positions (one per sample)
labels = col_names, # your labels vector
las = 2, # rotate text vertically (like srt=90)
cex.axis = 0.3) # shrink label size
| Version | Author | Date |
|---|---|---|
| 9aa0597 | John D. Hurley | 2026-01-22 |
######Cor_HeatMap####
cor_Filt_RMG0_RNA_log2cpm <- cor(Filt_RMG0_RNA_log2cpm, method = "spearman")
individual <- RNA_Metadata$Individual
species <- RNA_Metadata$Species
timepoint <- RNA_Metadata$Timepoint
timepoint <- factor(timepoint,levels = c("Day0","Day2","Day4","Day5","Day15","Day30"))
Cor_metadata <- data.frame(
sample_cor = colnames(Filt_RMG0_RNA_log2cpm),
species_cor = species,
timepoint_cor = timepoint,
individual_cor = individual
)
ann_colors <- list(
timepoint_cor = c(
"Day0" = "#883268", # Purple
"Day2" = "#3E7274", # blue
"Day4" = "#5AAA464D", # light green
"Day5" = "#94C47D", # Green
"Day15" = "#C03830", # red
"Day30" = "#830C05" # dark red
),
species_cor = c(
"H" = "#171717", # black
"C" = "#17171717" # light grey
),
individual_cor = c(
H1 = "#091638", #Blue-Green Darkest
H2 = "#11185B",
H3 = "#0F2C71",
H4 = "#0D568F",
H5 = "#1D8296",
H6 = "#46A389",
H7 = "#9DD484", #Blue-Green Lightest
C1 = "#340702", #Brown-Orange darkest
C2 = "#5D0B02",
C3 = "#951302",
C4 = "#D32804",
C5 = "#F74019",
C6 = "#FA7A38",
C7 = "#FCC598"
)
)
rownames(Cor_metadata) <- Cor_metadata$sample_cor
# saveRDS(cor_Filt_RMG0_RNA_log2cpm, "data/QC/Cor_Filt_RMG0_RNA_log2cpm.RDS")
# saveRDS(Cor_metadata, "data/QC/Cor_metadata.RDS")
# saveRDS(ann_colors,"data/QC/ann_colors.RDS")print(
pheatmap(cor_Filt_RMG0_RNA_log2cpm,
fontsize_row = 5,
fontsize_col = 5,
annotation_col = Cor_metadata[, c("species_cor", "timepoint_cor","individual_cor")],
annotation_colors = ann_colors,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
main = "Sample-Sample Correlation \n(Spearman-log2CPM-RowMeans>0)")
)
| Version | Author | Date |
|---|---|---|
| 9aa0597 | John D. Hurley | 2026-01-22 |
####Subset####
RNA_Metadata_No4 <- RNA_Metadata %>%
filter(timepoint != "Day4")
RNA_fc_NoD4 <- RNA_fc %>%
dplyr::select(-ends_with("_D4"))
RNA_log2cpm_NoD4 <- cpm(RNA_fc_NoD4,log=TRUE)
dim(RNA_log2cpm_NoD4)[1] 44125 74dim(RNA_fc)[1] 44125 88row_means_NoD4 <- rowMeans(RNA_log2cpm_NoD4)
Filt_RMG0_RNA_fc_NoD4 <- RNA_fc_NoD4[row_means_NoD4 >0,]
dim(Filt_RMG0_RNA_fc_NoD4)[1] 14838 74Filt_RMG0_RNA_log2cpm_NoD4 <- cpm(Filt_RMG0_RNA_fc_NoD4,log=TRUE)
# saveRDS(RNA_Metadata_No4,"data/QC/RNA_Metatdata_No4.RDS")
# saveRDS(Filt_RMG0_RNA_fc_NoD4,"data/QC/Filt_RMG0_RNA_fc_NoD4_Ensemble.RDS")
# saveRDS(Filt_RMG0_RNA_log2cpm_NoD4, "data/QC/Filt_RMG0_RNA_log2cpm_NoD4_Ensemble.RDS")######Cor_HeatMap####
cor_Filt_RMG0_RNA_log2cpm_NoD4 <- cor(Filt_RMG0_RNA_log2cpm_NoD4, method = "spearman")
Cor_metadata_No4 <- Cor_metadata %>%
dplyr::filter(timepoint_cor !="Day4")
ann_colors_No4 <- ann_colors
ann_colors_No4$timepoint_cor <- ann_colors$timepoint_cor[
names(ann_colors$timepoint_cor) != "Day4"
]
# saveRDS(Cor_metadata_No4, "data/QC/Cor_metadata_No4.RDS")
# saveRDS(cor_Filt_RMG0_RNA_log2cpm_NoD4, "data/QC/cor_Filt_RMG0_RNA_log2cpm_NoD4.RDS")
# saveRDS(ann_colors_No4,"data/QC/ann_colors_no4.RDS")print(
pheatmap(cor_Filt_RMG0_RNA_log2cpm_NoD4,
fontsize_row = 5,
fontsize_col = 5,
annotation_col = Cor_metadata_No4[, c("species_cor", "timepoint_cor","individual_cor")],
annotation_colors = ann_colors_No4,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
main = "Sample-Sample Correlation (Spearman) \n (log2CPM-RowMeans>0-NoDay4)")
)
| Version | Author | Date |
|---|---|---|
| 9aa0597 | John D. Hurley | 2026-01-22 |
# #git -> commit all changes
# #git -> push
wflow_publish("analysis/RNA_CorrelationHeatMap.Rmd")
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] lubridate_1.9.4 forcats_1.0.1 stringr_1.5.2
[4] purrr_1.1.0 tidyr_1.3.1 tidyverse_2.0.0
[7] Cormotif_1.54.0 affy_1.86.0 pheatmap_1.0.13
[10] org.Hs.eg.db_3.21.0 AnnotationDbi_1.70.0 IRanges_2.42.0
[13] S4Vectors_0.46.0 Biobase_2.68.0 BiocGenerics_0.54.0
[16] generics_0.1.4 readr_2.1.5 ggrepel_0.9.6
[19] dplyr_1.1.4 tibble_3.3.0 ggplot2_4.0.0
[22] edgeR_4.6.3 limma_3.64.3 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 farver_2.1.2 blob_1.3.0
[4] Biostrings_2.76.0 S7_0.2.0 fastmap_1.2.0
[7] promises_1.3.3 digest_0.6.37 timechange_0.3.0
[10] lifecycle_1.0.5 statmod_1.5.0 processx_3.8.6
[13] KEGGREST_1.48.1 RSQLite_2.4.3 magrittr_2.0.3
[16] compiler_4.5.1 rlang_1.1.6 sass_0.4.10
[19] tools_4.5.1 yaml_2.3.10 knitr_1.51
[22] bit_4.6.0 RColorBrewer_1.1-3 withr_3.0.2
[25] grid_4.5.1 preprocessCore_1.70.0 git2r_0.36.2
[28] scales_1.4.0 cli_3.6.5 rmarkdown_2.30
[31] crayon_1.5.3 otel_0.2.0 rstudioapi_0.17.1
[34] httr_1.4.7 tzdb_0.5.0 DBI_1.2.3
[37] cachem_1.1.0 BiocManager_1.30.27 XVector_0.48.0
[40] vctrs_0.6.5 jsonlite_2.0.0 callr_3.7.6
[43] hms_1.1.4 bit64_4.6.0-1 locfit_1.5-9.12
[46] jquerylib_0.1.4 affyio_1.78.0 glue_1.8.0
[49] ps_1.9.1 stringi_1.8.7 gtable_0.3.6
[52] later_1.4.4 GenomeInfoDb_1.44.3 UCSC.utils_1.4.0
[55] pillar_1.11.1 htmltools_0.5.8.1 GenomeInfoDbData_1.2.14
[58] R6_2.6.1 rprojroot_2.1.1 evaluate_1.0.5
[61] lattice_0.22-7 png_0.1-8 memoise_2.0.1
[64] httpuv_1.6.16 bslib_0.9.0 Rcpp_1.1.0
[67] whisker_0.4.1 xfun_0.53 fs_1.6.6
[70] getPass_0.2-4 pkgconfig_2.0.3