CTL04: Heatmap overlapping DEGs
Date: August 13, 2025
Purpose
Heatmaps of DEGs in common among CTL04 samples.
1. Environment SetUp
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, collapse = TRUE)library(tidyr)
library(dplyr)
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library(SummarizedExperiment)
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library(sechm)
library(ashr)
library(sva)
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library(DESeq2)
OutputFolder <- params$OutputFolderif (dir.exists(OutputFolder) == FALSE) {
dir.create(OutputFolder, recursive=FALSE)
}2. Data Upload and Preparation
DEA <- readRDS(paste0(params$InputFolder, 'DEARes.rds'))SE_Bio <- readRDS(params$SE)colData(SE_Bio)$Condition <- as.factor(colData(SE_Bio)$Condition)
colData(SE_Bio)$Condition <- factor(colData(SE_Bio)$Condition, levels = c("DMSO", setdiff(levels(colData(SE_Bio)$Condition), "DMSO")))SE_Bio <- SE_Bio[!duplicated(rowData(SE_Bio)$GeneName), ]
rownames(SE_Bio) <- rowData(SE_Bio)$GeneName
SE_Bio
## class: SummarizedExperiment
## dim: 19892 66
## metadata(0):
## assays(1): counts
## rownames(19892): TSPAN6 TNMD ... AC114402.3 AC084756.2
## rowData names(9): Gene EnsGene ... Start End
## colnames(66): MTR_044_S1_CTL MTR_045_S2_CTL ... MTR_108_S63_DMSO
## MTR_109_S64_DMSO
## colData names(20): InternalUniqueID HRID ... SeqPlatform lib.sizeSE object containing information about 19892 genes in 66 samples.
SE_DEA <- SE_Bio
SE_DEA
## class: SummarizedExperiment
## dim: 19892 66
## metadata(0):
## assays(1): counts
## rownames(19892): TSPAN6 TNMD ... AC114402.3 AC084756.2
## rowData names(9): Gene EnsGene ... Start End
## colnames(66): MTR_044_S1_CTL MTR_045_S2_CTL ... MTR_108_S63_DMSO
## MTR_109_S64_DMSO
## colData names(20): InternalUniqueID HRID ... SeqPlatform lib.sizeVst Transformation
assays(SE_DEA)$vst <- vst(as.matrix(assays(SE_DEA)$counts), blind=TRUE)
## converting counts to integer mode
SE_DEA
## class: SummarizedExperiment
## dim: 19892 66
## metadata(0):
## assays(2): counts vst
## rownames(19892): TSPAN6 TNMD ... AC114402.3 AC084756.2
## rowData names(9): Gene EnsGene ... Start End
## colnames(66): MTR_044_S1_CTL MTR_045_S2_CTL ... MTR_108_S63_DMSO
## MTR_109_S64_DMSO
## colData names(20): InternalUniqueID HRID ... SeqPlatform lib.sizeSetting Metadata
metadata(SE_DEA)$anno_colors <- list(
Condition = c('DMSO' = 'grey30', 'CTL' = 'azure3',
'AhHyd_Ag'='#F8766D', 'AhHyd_Inh'='#F8766D50',
'Andr_Ag'='#fccb17', 'Andr_Inh'='#C49A0050',
"Estr_Ag"= '#53B400', "Estr_Inh"= '#53B40050',
'GC_Ag' = '#00C094', 'GC_Inh' = '#00C09450',
'LivX_Ag' = '#00B6EB', 'LivX_Inh' = '#00B6EB50',
'Ret_Ag' = '#A58AFF', 'Ret_Inh' = '#A58AFF50',
'Thyr_Ag' = '#FB61D7', 'Thyr_Inh' = '#FB61D750'
))
ScaledCols <- c('darkblue', "purple","white","lightgoldenrod1", 'goldenrod1')3. Comparison across pathways: Agonist and Inhibitor
3.1 Heatmaps for Agonist
DEGs_Agonist <- list(
Thyr_Ag_Up= row.names(DEA$Thyr$Agonist$DEGs[DEA$Thyr$Agonist$DEGs$log2FoldChange > 0, ]),
Thyr_Ag_Down= row.names(DEA$Thyr$Agonist$DEGs[DEA$Thyr$Agonist$DEGs$log2FoldChange < 0, ]),
Ret_Ag_Up= row.names(DEA$Ret$Agonist$DEGs[DEA$Ret$Agonist$DEGs$log2FoldChange > 0, ]),
Ret_Ag_Down= row.names(DEA$Ret$Agonist$DEGs[DEA$Ret$Agonist$DEGs$log2FoldChange < 0, ]),
Estr_Ag_Up= row.names(DEA$Estr$Agonist$DEGs[DEA$Estr$Agonist$DEGs$log2FoldChange > 0, ]),
Estr_Ag_Down= row.names(DEA$Estr$Agonist$DEGs[DEA$Estr$Agonist$DEGs$log2FoldChange < 0, ]),
Andr_Ag_Up= row.names(DEA$Andr$Agonist$DEGs[DEA$Andr$Agonist$DEGs$log2FoldChange > 0, ]),
Andr_Ag_Down= row.names(DEA$Andr$Agonist$DEGs[DEA$Andr$Agonist$DEGs$log2FoldChange < 0, ]),
GC_Ag_Up= row.names(DEA$GC$Agonist$DEGs[DEA$GC$Agonist$DEGs$log2FoldChange > 0, ]),
GC_Ag_Down= row.names(DEA$GC$Agonist$DEGs[DEA$GC$Agonist$DEGs$log2FoldChange < 0, ]),
LivX_Ag_Up= row.names(DEA$LivX$Agonist$DEGs[DEA$LivX$Agonist$DEGs$log2FoldChange > 0, ]),
LivX_Ag_Down= row.names(DEA$LivX$Agonist$DEGs[DEA$LivX$Agonist$DEGs$log2FoldChange < 0, ]),
AhHyd_Ag_Up= row.names(DEA$AhHyd$Agonist$DEGs[DEA$AhHyd$Agonist$DEGs$log2FoldChange > 0, ]),
AhHyd_Ag_Down= row.names(DEA$AhHyd$Agonist$DEGs[DEA$AhHyd$Agonist$DEGs$log2FoldChange < 0, ])
)3.1.1 Up-regulated genes
Heatmap Up-regulated DEGs in common between Retinoic Acid agonist and Thyroid agonist
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO", "Ret_Ag", "Thyr_Ag")
DEGs <- intersect(DEGs_Agonist$Ret_Ag_Up, DEGs_Agonist$Thyr_Ag_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
48 genes in common.
Heatmap Up-regulated DEGs in common between Retinoic Acid agonist and Androgen agonist
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO", "Ret_Ag", "Andr_Ag")
DEGs <- intersect(DEGs_Agonist$Ret_Ag_Up, DEGs_Agonist$Andr_Ag_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
58 genes in common.
Heatmap Up-regulated DEGs in common between GC agonist and LivX agonist
No overlap with these conditions
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Ag", "GC_Ag")
DEGs <- intersect(DEGs_Agonist$LivX_Ag_Up, DEGs_Agonist$GC_Ag_Up)
# sechm(SE_DEA[,S], features=DEGs, assayName="vst",
# gaps_at="Condition",
# top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
# do.scale=TRUE, breaks=0.8)1 genes in common.
3.1.2 Down-regulated genes
Heatmap Down-regulated DEGs in common between Retinoic agonist and Thyroid agonist
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO", "Ret_Ag", "Thyr_Ag")
DEGs <- intersect(DEGs_Agonist$Ret_Ag_Down, DEGs_Agonist$Thyr_Ag_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
36 genes in common.
Heatmap Down-regulated DEGs in common between GC agonist and LivX agonist
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Ag", "GC_Ag")
DEGs <- intersect(DEGs_Agonist$LivX_Ag_Down, DEGs_Agonist$GC_Ag_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
3 genes in common.
3.2 Heatmaps for Inhibitor
DEGs_Inh <- list(
Thyr_Inh_Up= row.names(DEA$Thyr$Inhibitor$DEGs[DEA$Thyr$Inhibitor$DEGs$log2FoldChange > 0, ]),
Thyr_Inh_Down= row.names(DEA$Thyr$Inhibitor$DEGs[DEA$Thyr$Inhibitor$DEGs$log2FoldChange < 0, ]),
Ret_Inh_Up= row.names(DEA$Ret$Inhibitor$DEGs[DEA$Ret$Inhibitor$DEGs$log2FoldChange > 0, ]),
Ret_Inh_Down= row.names(DEA$Ret$Inhibitor$DEGs[DEA$Ret$Inhibitor$DEGs$log2FoldChange < 0, ]),
Estr_Inh_Up= row.names(DEA$Estr$Inhibitor$DEGs[DEA$Estr$Inhibitor$DEGs$log2FoldChange > 0, ]),
Estr_Inh_Down= row.names(DEA$Estr$Inhibitor$DEGs[DEA$Estr$Inhibitor$DEGs$log2FoldChange < 0, ]),
Andr_Inh_Up= row.names(DEA$Andr$Inhibitor$DEGs[DEA$Andr$Inhibitor$DEGs$log2FoldChange > 0, ]),
Andr_Inh_Down= row.names(DEA$Andr$Inhibitor$DEGs[DEA$Andr$Inhibitor$DEGs$log2FoldChange < 0, ]),
GC_Inh_Up= row.names(DEA$GC$Inhibitor$DEGs[DEA$GC$Inhibitor$DEGs$log2FoldChange > 0, ]),
GC_Inh_Down= row.names(DEA$GC$Inhibitor$DEGs[DEA$GC$Inhibitor$DEGs$log2FoldChange < 0, ]),
LivX_Inh_Up= row.names(DEA$LivX$Inhibitor$DEGs[DEA$LivX$Inhibitor$DEGs$log2FoldChange > 0, ]),
LivX_Inh_Down= row.names(DEA$LivX$Inhibitor$DEGs[DEA$LivX$Inhibitor$DEGs$log2FoldChange < 0, ]),
AhHyd_Inh_Up= row.names(DEA$AhHyd$Inhibitor$DEGs[DEA$AhHyd$Inhibitor$DEGs$log2FoldChange > 0, ]),
AhHyd_Inh_Down= row.names(DEA$AhHyd$Inhibitor$DEGs[DEA$AhHyd$Inhibitor$DEGs$log2FoldChange < 0, ])
)3.2.1 Up-regulated genes
Heatmap Up-regulated DEGs in common between LiverX inhibitor and GC inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- intersect(DEGs_Inh$LivX_Inh_Up, DEGs_Inh$GC_Inh_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
93 genes in common.
Heatmap Up-regulated DEGs in common between Thyroid inhibitor and LiverX inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- intersect(DEGs_Inh$Thyr_Inh_Up, DEGs_Inh$LivX_Inh_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Up-regulated DEGs in common between GC inhibitor and Thyroid inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- intersect(DEGs_Inh$GC_Inh_Up, DEGs_Inh$Thyr_Inh_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Up-regulated DEGs in common between LiverX inhibitor, Thyroid inhibitor and GC inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- Reduce(intersect, list(DEGs_Inh$GC_Inh_Up, DEGs_Inh$Thyr_Inh_Up, DEGs_Inh$LivX_Inh_Up))
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
3.2.2 Down-regulated genes
Heatmap Down-regulated DEGs in common between LiverX inhibitor and GC inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- intersect(DEGs_Inh$LivX_Inh_Down, DEGs_Inh$GC_Inh_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
155 genes in common.
Heatmap Down-regulated DEGs in common between Thyroid inhibitor and LiverX inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- intersect(DEGs_Inh$Thyr_Inh_Down, DEGs_Inh$LivX_Inh_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Down-regulated DEGs in common between GC inhibitor and Thyroid inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- intersect(DEGs_Inh$GC_Inh_Down, DEGs_Inh$Thyr_Inh_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Down-regulated DEGs in common between LiverX inhibitor, Thyroid inhibitor and GC inhibitor
S <- colData(SE_DEA)[, "Condition"] %in% c("DMSO","LivX_Inh", "GC_Inh", "Thyr_Inh")
DEGs <- Reduce(intersect, list(DEGs_Inh$GC_Inh_Down, DEGs_Inh$Thyr_Inh_Down, DEGs_Inh$LivX_Inh_Down))
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
3.3 Heatmaps for AgvsInh
DEGs_AgvsInh <- list(
Thyr_AgvsInh_Up= row.names(DEA$Thyr$AgvsInh$DEGs[DEA$Thyr$AgvsInh$DEGs$log2FoldChange > 0, ]),
Thyr_AgvsInh_Down= row.names(DEA$Thyr$AgvsInh$DEGs[DEA$Thyr$AgvsInh$DEGs$log2FoldChange < 0, ]),
Ret_AgvsInh_Up= row.names(DEA$Ret$AgvsInh$DEGs[DEA$Ret$AgvsInh$DEGs$log2FoldChange > 0, ]),
Ret_AgvsInh_Down= row.names(DEA$Ret$AgvsInh$DEGs[DEA$Ret$AgvsInh$DEGs$log2FoldChange < 0, ]),
Estr_AgvsInh_Up= row.names(DEA$Estr$AgvsInh$DEGs[DEA$Estr$AgvsInh$DEGs$log2FoldChange > 0, ]),
Estr_AgvsInh_Down= row.names(DEA$Estr$AgvsInh$DEGs[DEA$Estr$AgvsInh$DEGs$log2FoldChange < 0, ]),
Andr_AgvsInh_Up= row.names(DEA$Andr$AgvsInh$DEGs[DEA$Andr$AgvsInh$DEGs$log2FoldChange > 0, ]),
Andr_AgvsInh_Down= row.names(DEA$Andr$AgvsInh$DEGs[DEA$Andr$AgvsInh$DEGs$log2FoldChange < 0, ]),
GC_AgvsInh_Up= row.names(DEA$GC$AgvsInh$DEGs[DEA$GC$AgvsInh$DEGs$log2FoldChange > 0, ]),
GC_AgvsInh_Down= row.names(DEA$GC$AgvsInh$DEGs[DEA$GC$AgvsInh$DEGs$log2FoldChange < 0, ]),
LivX_AgvsInh_Up= row.names(DEA$LivX$AgvsInh$DEGs[DEA$LivX$AgvsInh$DEGs$log2FoldChange > 0, ]),
LivX_AgvsInh_Down= row.names(DEA$LivX$AgvsInh$DEGs[DEA$LivX$AgvsInh$DEGs$log2FoldChange < 0, ]),
AhHyd_AgvsInh_Up= row.names(DEA$AhHyd$AgvsInh$DEGs[DEA$AhHyd$AgvsInh$DEGs$log2FoldChange > 0, ]),
AhHyd_AgvsInh_Down= row.names(DEA$AhHyd$AgvsInh$DEGs[DEA$AhHyd$AgvsInh$DEGs$log2FoldChange < 0, ])
)3.3.1 Up-regulated genes
Heatmap Up-regulated DEGs in common between LiverX AgvsInh and GC AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("LivX_Inh", "GC_Inh", "LivX_Ag", "GC_Ag")
DEGs <- intersect(DEGs_AgvsInh$GC_AgvsInh_Up, DEGs_AgvsInh$LivX_AgvsInh_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Up-regulated DEGs in common between LiverX AgvsInh and Thyr AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("LivX_Inh", "Thyr_Inh", "LivX_Ag", "Thyr_Ag")
DEGs <- intersect(DEGs_AgvsInh$Thyr_AgvsInh_Up, DEGs_AgvsInh$LivX_AgvsInh_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Up-regulated DEGs in common between GC AgvsInh and Thyr AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("GC_Inh", "Thyr_Inh", "GC_Ag", "Thyr_Ag")
DEGs <- intersect(DEGs_AgvsInh$Thyr_AgvsInh_Up, DEGs_AgvsInh$GC_AgvsInh_Up)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Up-regulated DEGs in common between GC AgvsInh, Thyr AgvsInh and LivX AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("Thyr_Inh", "LivX_Inh", "Thyr_Ag", "LivX_Ag", "GC_Ag", "GC_Inh")
DEGs <- Reduce(intersect, list(DEGs_AgvsInh$LivX_AgvsInh_Up, DEGs_AgvsInh$Thyr_AgvsInh_Up, DEGs_AgvsInh$GC_AgvsInh_Up))
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors, show_rownames = T,
do.scale=TRUE, breaks=0.8)
***
3.3.2 Down-regulated genes
Heatmap Down-regulated DEGs in common between LiverX AgvsInh and GC AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("LivX_Inh", "GC_Inh", "LivX_Ag", "GC_Ag")
DEGs <- intersect(DEGs_AgvsInh$GC_AgvsInh_Down, DEGs_AgvsInh$LivX_AgvsInh_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Down-regulated DEGs in common between Thyr AgvsInh and GC AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("Thyr_Inh", "GC_Inh", "Thyr_Ag", "GC_Ag")
DEGs <- intersect(DEGs_AgvsInh$GC_AgvsInh_Down, DEGs_AgvsInh$Thyr_AgvsInh_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Down-regulated DEGs in common between Thyr AgvsInh and LivX AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("Thyr_Inh", "LivX_Inh", "Thyr_Ag", "LivX_Ag")
DEGs <- intersect(DEGs_AgvsInh$LivX_AgvsInh_Down, DEGs_AgvsInh$Thyr_AgvsInh_Down)
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors,
do.scale=TRUE, breaks=0.8)
Heatmap Down-regulated DEGs in common between Thyr AgvsInh, LivX AgvsInh and GC AgvsInh
S <- colData(SE_DEA)[, "Condition"] %in% c("Thyr_Inh", "LivX_Inh", "Thyr_Ag", "LivX_Ag", "GC_Ag", "GC_Inh")
DEGs <- Reduce(intersect, list(DEGs_AgvsInh$LivX_AgvsInh_Down, DEGs_AgvsInh$Thyr_AgvsInh_Down, DEGs_AgvsInh$GC_AgvsInh_Down))
sechm(SE_DEA[,S], features=DEGs, assayName="vst",
gaps_at="Condition",
top_annotation=c('Condition'), hmcols=ScaledCols, anno_colors = metadata(SE_DEA)$anno_colors, show_rownames = T,
do.scale=TRUE, breaks=0.8)
4. Session info
SessionInfo <- sessionInfo()
Date <- date()Date
## [1] "Wed Aug 13 12:55:52 2025"
SessionInfo
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## 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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] DESeq2_1.38.3 sva_3.46.0
## [3] BiocParallel_1.32.5 genefilter_1.80.3
## [5] mgcv_1.8-41 nlme_3.1-162
## [7] ashr_2.2-54 sechm_1.6.0
## [9] SummarizedExperiment_1.28.0 Biobase_2.58.0
## [11] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [13] IRanges_2.32.0 S4Vectors_0.36.1
## [15] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
## [17] matrixStats_0.63.0 ggplot2_3.4.1
## [19] dplyr_1.1.0 tidyr_1.3.0
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.16 colorspace_2.1-0 rjson_0.2.21
## [4] circlize_0.4.15 XVector_0.38.0 GlobalOptions_0.1.2
## [7] clue_0.3-64 rstudioapi_0.14 bit64_4.0.5
## [10] AnnotationDbi_1.60.0 fansi_1.0.4 codetools_0.2-19
## [13] splines_4.2.1 doParallel_1.0.17 cachem_1.0.7
## [16] geneplotter_1.76.0 knitr_1.42 jsonlite_1.8.4
## [19] annotate_1.76.0 cluster_2.1.4 png_0.1-8
## [22] compiler_4.2.1 httr_1.4.5 Matrix_1.5-3
## [25] fastmap_1.1.1 limma_3.54.1 cli_3.6.1
## [28] htmltools_0.5.4 tools_4.2.1 gtable_0.3.1
## [31] glue_1.6.2 GenomeInfoDbData_1.2.9 V8_4.2.2
## [34] Rcpp_1.0.10 jquerylib_0.1.4 vctrs_0.6.2
## [37] Biostrings_2.66.0 iterators_1.0.14 xfun_0.37
## [40] stringr_1.5.0 lifecycle_1.0.3 irlba_2.3.5.1
## [43] XML_3.99-0.13 ca_0.71.1 edgeR_3.40.2
## [46] zlibbioc_1.44.0 scales_1.2.1 TSP_1.2-2
## [49] parallel_4.2.1 RColorBrewer_1.1-3 ComplexHeatmap_2.14.0
## [52] yaml_2.3.7 curl_5.0.0 memoise_2.0.1
## [55] sass_0.4.5 stringi_1.7.12 RSQLite_2.3.0
## [58] SQUAREM_2021.1 highr_0.10 randomcoloR_1.1.0.1
## [61] foreach_1.5.2 seriation_1.4.1 truncnorm_1.0-8
## [64] shape_1.4.6 rlang_1.1.1 pkgconfig_2.0.3
## [67] bitops_1.0-7 evaluate_0.20 lattice_0.20-45
## [70] invgamma_1.1 purrr_1.0.1 bit_4.0.5
## [73] tidyselect_1.2.0 magrittr_2.0.3 R6_2.5.1
## [76] generics_0.1.3 DelayedArray_0.24.0 DBI_1.1.3
## [79] pillar_1.8.1 withr_2.5.0 survival_3.5-3
## [82] KEGGREST_1.38.0 RCurl_1.98-1.10 mixsqp_0.3-48
## [85] tibble_3.2.1 crayon_1.5.2 utf8_1.2.3
## [88] rmarkdown_2.20 GetoptLong_1.0.5 locfit_1.5-9.7
## [91] grid_4.2.1 blob_1.2.3 digest_0.6.31
## [94] xtable_1.8-4 munsell_0.5.0 registry_0.5-1
## [97] bslib_0.4.2