RNA_CorrelationHeatMap
2026-01-14
Last updated: 2026-02-18
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Knit directory: CrossSpecies_CM_Diff_RNA/
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| File | Version | Author | Date | Message |
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| Rmd | c85837b | John D. Hurley | 2026-02-18 | SiteUpDate_CorrelationHeatMap |
| html | 7789dff | John D. Hurley | 2026-01-28 | Build site. |
| Rmd | 0fcf7c3 | John D. Hurley | 2026-01-28 | HeatMap knit update |
| html | d9f15a6 | John D. Hurley | 2026-01-28 | Build site. |
| Rmd | 19cd9fb | John D. Hurley | 2026-01-28 | Commenting out wokrflowr publish |
| Rmd | 3b54f65 | John D. Hurley | 2026-01-28 | Duplicate Code Labels |
| Rmd | de67e64 | John D. Hurley | 2026-01-28 | Commenting out code not in use. |
| Rmd | da444ed | John D. Hurley | 2026-01-28 | RMarkdown name update |
| Rmd | 085c1db | John D. Hurley | 2026-01-28 | Finalizing CorHeatMap |
h_samples_counts <- read.delim("~/diff_timeline_tes/RNA/Run1_Run2_Concat/featurecounts/h_samples_counts.txt", comment.char="#")
c_samples_counts <- read.delim("~/diff_timeline_tes/RNA/Run1_Run2_Concat/featurecounts/c_samples_counts.txt", comment.char="#")
RNA_joined_fc <- left_join(h_samples_counts,c_samples_counts, by = "Geneid")
#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
RNA_fc <- RNA_fc %>%
column_to_rownames("Geneid")
# #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)
ensembl_ids_unfilt <- rownames(RNA_fc)
entrez_ids_unfilt <- mapIds(org.Hs.eg.db,
keys = ensembl_ids_unfilt,
column = "ENTREZID",
keytype = "ENSEMBL",
multiVals = "first")
symbol_ids_unfilt <- mapIds(org.Hs.eg.db,
keys = ensembl_ids_unfilt,
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")
RNA_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/Raw_Data/RNA_fc_df.RDS")
RNA_Metadata <- read_excel("~/diff_timeline_tes/RNA/RNA_Metadata.xlsx")
# saveRDS(RNA_Metadata,"data/Raw_Data/RNA_Metadata.RDS")# -----------------------------
# 1. Prepare long-format data
# -----------------------------
day_levels <- c("Day0","Day2","Day4","Day5","Day15","Day30")
marker_levels <- c("OCT3/4", "Brachy", "ISL-1", "TNNT-2")
df_long_all <- RNA_Metadata_NoD4_NoRep %>%
dplyr::select(Cond, `OCT3/4`, Brachy, `ISL-1`, `TNNT-2`) %>%
mutate(
`OCT3/4` = as.numeric(`OCT3/4`),
Brachy = as.numeric(Brachy),
`ISL-1` = as.numeric(`ISL-1`),
`TNNT-2` = as.numeric(`TNNT-2`),
Group = ifelse(grepl("^H_", Cond), "H", "C")
) %>%
pivot_longer(
cols = c(`OCT3/4`, Brachy, `ISL-1`, `TNNT-2`),
names_to = "Marker",
values_to = "Percent_Positive"
) %>%
mutate(
Day = gsub(".*_(Day\\d+)$", "\\1", Cond),
Day = factor(Day, levels = day_levels, ordered = TRUE),
Marker = factor(Marker, levels = marker_levels, ordered = TRUE)
)
# -----------------------------
# 2. Compute summary with SEM
# -----------------------------
summary_df <- df_long_all %>%
group_by(Marker, Day, Group) %>%
summarise(
Mean = mean(Percent_Positive, na.rm = TRUE),
SD = sd(Percent_Positive, na.rm = TRUE),
N = sum(!is.na(Percent_Positive)),
SEM = SD / sqrt(N),
.groups = "drop"
)# -----------------------------
# 3. Plot lines over time with SEM error bars
# -----------------------------
ggplot(summary_df, aes(x = Day, y = Mean, color = Group, group = Group)) +
geom_line(linewidth = 1.1) +
geom_point(size = 2) +
geom_errorbar(
aes(ymin = Mean - SEM, ymax = Mean + SEM),
width = 0.2
) +
facet_wrap(~ Marker, nrow = 1) +
scale_color_manual(values = c("H" = "skyblue", "C" = "salmon")) +
scale_y_continuous(limits = c(0, 100)) +
labs(
title = "Mean Percent Positive Across Timepoints for Each Marker",
x = "Day",
y = "Mean Percent Positive (%)",
caption = "Error bars represent SEM (Standard Error of the Mean)"
) +
theme_minimal(base_size = 14) +
theme(
legend.position = "top",
axis.text.x = element_text(angle = 0, hjust = 0.5),
plot.caption = element_text(hjust = 0, face = "italic", size = 10)
)
#####Unfiltered####
RNA_fc <- RNA_fc_df %>%
dplyr::select(c(-"Entrez_ID", -"Symbol")) %>%
column_to_rownames("Ensemble")
# saveRDS(RNA_fc,"data/QC/RNA_fc.RDS")
RNA_log2cpm <- cpm(RNA_fc,log=TRUE)
hist(RNA_log2cpm, main = "Histogram of all counts (unfiltered)",
xlab =expression("Log"[2]*" counts-per-million"), col =4 )
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 = 0.6) # shrink label size
# saveRDS(RNA_log2cpm,"data/QC/RNA_log2cpm.RDS")#####RowMu>0####
row_means <- rowMeans(RNA_log2cpm)
Filt_RMG0_RNA_fc <- RNA_fc[row_means >0,]
# saveRDS(Filt_RMG0_RNA_fc,"data/QC/Filt_RMG0_RNA_fc.RDS")
Filt_RMG0_RNA_log2cpm <- cpm(Filt_RMG0_RNA_fc,log=TRUE)
# saveRDS(Filt_RMG0_RNA_log2cpm,"data/QC/RNA_log2cpm_RMG0.RDS")
hist(Filt_RMG0_RNA_log2cpm, main = "Histogram of filtered counts using rowMeans > 0 method",
xlab =expression("Log"[2]*" counts-per-million"), col =5 )
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
######Cor_HeatMap####
Cor_Filt_RMG0_RNA_log2cpm <- cor(Filt_RMG0_RNA_log2cpm, method = "spearman")
individual <- RNA_Metadata$Individual_Label
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",
H4R = "#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$sample_cor
# saveRDS(Cor_Filt_RMG0_RNA_log2cpm, "data/QC/Cor_Filt_RMG0_RNA_log2cpm.RDS")
# saveRDS(Cor_metadata, "data/QC/Cor_RNA_metadata.RDS")
# saveRDS(ann_colors,"data/QC/ann_colors.RDS")print(
pheatmap(Cor_Filt_RMG0_RNA_log2cpm,
fontsize_row = 5,
fontsize_col = 5,
annotation_col = Cor_metadata[, c("species_cor", "timepoint_cor","individual_cor")],
annotation_colors = ann_colors,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
main = "Sample-Sample Correlation \n(Spearman-log2CPM-RowMeans>0)")
)
####Subset####
RNA_Metadata_No4 <- RNA_Metadata %>%
filter(Timepoint != "Day4")
RNA_fc_NoD4 <- RNA_fc %>%
dplyr::select(-ends_with("_D4"))
RNA_log2cpm_NoD4 <- cpm(RNA_fc_NoD4,log=TRUE)
dim(RNA_log2cpm_NoD4)[1] 44125 74dim(RNA_fc)[1] 44125 88row_means_NoD4 <- rowMeans(RNA_log2cpm_NoD4)
Filt_RMG0_RNA_fc_NoD4 <- RNA_fc_NoD4[row_means_NoD4 >0,]
dim(Filt_RMG0_RNA_fc_NoD4)[1] 14838 74Filt_RMG0_RNA_log2cpm_NoD4 <- cpm(Filt_RMG0_RNA_fc_NoD4,log=TRUE)
# saveRDS(RNA_Metadata_No4,"data/QC/RNA_Metatdata_No4.RDS")
# saveRDS(Filt_RMG0_RNA_fc_NoD4,"data/QC/Filt_RMG0_RNA_fc_NoD4.RDS")
# saveRDS(Filt_RMG0_RNA_log2cpm_NoD4, "data/QC/Filt_RMG0_RNA_log2cpm_NoD4.RDS")######Cor_HeatMap####
Cor_Filt_RMG0_RNA_log2cpm_NoD4 <- cor(Filt_RMG0_RNA_log2cpm_NoD4, method = "spearman")
Cor_metadata_No4 <- Cor_metadata %>%
dplyr::filter(timepoint_cor !="Day4")
ann_colors_No4 <- ann_colors
ann_colors_No4$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)")
)
# git -> commit all changes
# git -> push
# wflow_publish("analysis/RNA_CorrelationHeatMap_Ensemble.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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ComplexHeatmap_2.24.1 ggfortify_0.4.19
[3] readxl_1.4.5 RUVSeq_1.42.0
[5] EDASeq_2.42.0 ShortRead_1.66.0
[7] GenomicAlignments_1.44.0 SummarizedExperiment_1.38.1
[9] MatrixGenerics_1.20.0 matrixStats_1.5.0
[11] Rsamtools_2.24.0 GenomicRanges_1.60.0
[13] Biostrings_2.76.0 GenomeInfoDb_1.44.3
[15] XVector_0.48.0 BiocParallel_1.42.1
[17] lubridate_1.9.5 forcats_1.0.1
[19] stringr_1.6.0 purrr_1.2.1
[21] tidyr_1.3.2 tidyverse_2.0.0
[23] Cormotif_1.54.0 affy_1.86.0
[25] pheatmap_1.0.13 org.Hs.eg.db_3.21.0
[27] AnnotationDbi_1.70.0 IRanges_2.42.0
[29] S4Vectors_0.46.0 Biobase_2.68.0
[31] BiocGenerics_0.54.1 generics_0.1.4
[33] readr_2.1.6 ggrepel_0.9.6
[35] dplyr_1.1.4 tibble_3.3.1
[37] ggplot2_4.0.2 edgeR_4.6.3
[39] limma_3.64.3 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] later_1.4.5 BiocIO_1.18.0 bitops_1.0-9
[4] filelock_1.0.3 R.oo_1.27.1 cellranger_1.1.0
[7] preprocessCore_1.70.0 XML_3.99-0.20 lifecycle_1.0.5
[10] httr2_1.2.2 pwalign_1.4.0 doParallel_1.0.17
[13] rprojroot_2.1.1 processx_3.8.6 lattice_0.22-7
[16] MASS_7.3-65 magrittr_2.0.4 sass_0.4.10
[19] rmarkdown_2.30 jquerylib_0.1.4 yaml_2.3.12
[22] httpuv_1.6.16 otel_0.2.0 DBI_1.2.3
[25] RColorBrewer_1.1-3 abind_1.4-8 R.utils_2.13.0
[28] RCurl_1.98-1.17 rappdirs_0.3.4 git2r_0.36.2
[31] circlize_0.4.17 GenomeInfoDbData_1.2.14 codetools_0.2-20
[34] DelayedArray_0.34.1 xml2_1.5.2 tidyselect_1.2.1
[37] shape_1.4.6.1 UCSC.utils_1.4.0 farver_2.1.2
[40] BiocFileCache_2.16.2 jsonlite_2.0.0 GetoptLong_1.1.0
[43] iterators_1.0.14 foreach_1.5.2 tools_4.5.1
[46] progress_1.2.3 Rcpp_1.1.1 glue_1.8.0
[49] gridExtra_2.3 SparseArray_1.8.1 xfun_0.56
[52] withr_3.0.2 BiocManager_1.30.27 fastmap_1.2.0
[55] latticeExtra_0.6-31 callr_3.7.6 digest_0.6.39
[58] timechange_0.4.0 R6_2.6.1 colorspace_2.1-2
[61] Cairo_1.7-0 jpeg_0.1-11 biomaRt_2.64.0
[64] RSQLite_2.4.5 R.methodsS3_1.8.2 rtracklayer_1.68.0
[67] prettyunits_1.2.0 httr_1.4.7 S4Arrays_1.8.1
[70] whisker_0.4.1 pkgconfig_2.0.3 gtable_0.3.6
[73] blob_1.3.0 S7_0.2.1 hwriter_1.3.2.1
[76] htmltools_0.5.9 clue_0.3-66 scales_1.4.0
[79] png_0.1-8 knitr_1.51 rstudioapi_0.18.0
[82] tzdb_0.5.0 rjson_0.2.23 curl_7.0.0
[85] cachem_1.1.0 GlobalOptions_0.1.3 parallel_4.5.1
[88] restfulr_0.0.16 pillar_1.11.1 vctrs_0.7.1
[91] promises_1.5.0 dbplyr_2.5.1 cluster_2.1.8.1
[94] evaluate_1.0.5 GenomicFeatures_1.60.0 cli_3.6.5
[97] locfit_1.5-9.12 compiler_4.5.1 rlang_1.1.7
[100] crayon_1.5.3 labeling_0.4.3 interp_1.1-6
[103] aroma.light_3.38.0 ps_1.9.1 getPass_0.2-4
[106] fs_1.6.6 stringi_1.8.7 deldir_2.0-4
[109] Matrix_1.7-4 hms_1.1.4 bit64_4.6.0-1
[112] KEGGREST_1.48.1 statmod_1.5.1 memoise_2.0.1
[115] affyio_1.78.0 bslib_0.10.0 bit_4.6.0