library(dplyr)
library(ggplot2)TCGA Analysis
1 Importing TCGA HNSC data with TCGA biolinks
1.1 Importing RNAseq data
# Query platform for HNSC bulk RNA sequencing data
query <- TCGAbiolinks::GDCquery(
project = "TCGA-HNSC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
)
# Download the data for HNSC patients
TCGAbiolinks::GDCdownload(query,files.per.chunk = 10)
# Prepare expression matrix with geneID in the rows and samples (barcode) in the columns
# rsem.genes.results as values
HNSC.Rnaseq.SE <- TCGAbiolinks::GDCprepare(query)
# For gene expression if you need to see a boxplot correlation and AAIC plot to define outliers you can run
HNSC.RNAseq_CorOutliers <- TCGAbiolinks::TCGAanalyze_Preprocessing(HNSC.Rnaseq.SE)
# normalization of genes
dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(
tabDF = HNSC.RNAseq_CorOutliers,
geneInfo = TCGAbiolinks::geneInfoHT
)
# quantile filter of genes
dataFilt <- TCGAbiolinks::TCGAanalyze_Filtering(
tabDF = dataNorm,
method = "quantile",
qnt.cut = 0.25
)
# selection of normal samples "NT"
samplesNT <- TCGAbiolinks::TCGAquery_SampleTypes(
barcode = colnames(dataFilt),
typesample = c("NT")
)
# selection of tumor samples "TP"
samplesTP <- TCGAbiolinks::TCGAquery_SampleTypes(
barcode = colnames(dataFilt),
typesample = c("TP")
)
datasampleNT <- dataFilt[,samplesNT]
datasampleTP <- dataFilt[,samplesTP]
# write into csv
write.csv(datasampleTP, 'RNAseq/Tumor_HNSC_bulk.csv',row.names = T)
write.csv(datasampleNT, 'RNAseq/Normal_HNSC_bulk.csv',row.names = T)1.2 Transform into ENSEMBL gene IDs using biomaRt
rna <- read.csv('RNAseq/Tumor_HNSC_bulk.csv', row.names = 1)
rownames(rna) <- gsub("\\.\\d+$", "", rownames(rna))
ensembl.genes <- rownames(rna)
geneIDs1 <- ensembldb::select(EnsDb.Hsapiens.v86::EnsDb.Hsapiens.v86, keys= ensembl.genes, keytype = "GENEID", columns = c("SYMBOL","GENEID"))
# Keep Only Unique ID Names
geneIDs1 <- geneIDs1 %>% distinct(SYMBOL, .keep_all= TRUE)
#Dropping geneIDs with no available conversion
rna <- rna[rownames(rna) %in% geneIDs1$GENEID,]
#Changing the rownames to symbols
rownames(rna) <- geneIDs1$SYMBOL
#Keeping only short sample IDs
colnames(rna) <- gsub('\\.','-',substr(colnames(rna),1,12))
# write into csv
write.csv(rna, 'RNAseq/Tumor_HNSC_bulk_PreProcessed.csv',row.names = T)2 Retrieving Clinical Data
#Reading clinical data
clinical <- read.csv('Clinical/TCGA_HNSC_Surival.csv', row.names = 1, sep = ';')
#Adding clinical data descriptions
#Keratin state
clinical <- clinical %>% mutate(K_state = case_when(
as.numeric(as.character(clinical$K)) >= 10 ~ "Keratin High",
as.numeric(as.character(clinical$K)) < 10 ~ "Keratin Low")
)
#MGC Content
clinical <- clinical %>% mutate(MGC_Three = case_when(
as.numeric(as.character(clinical$MCG_total_mm2)) >= 1 ~ "MGC High",
as.numeric(as.character(clinical$MCG_total_mm2)) < 0.2 ~ "MGC Low",
as.numeric(as.character(clinical$MCG_total_mm2)) < 1 & as.numeric(as.character(clinical$MCG_total_mm2)) >= 0.2 ~ "MGC Medium")
)3 DEG analysis using DESEq2
#Reading RNAseq data
rna <- read.csv('RNAseq/Tumor_HNSC_bulk_PreProcessed.csv', row.names = 1)
rna <- type.convert(rna, as.is = TRUE)
colnames(rna) <- gsub('\\.','-',substr(colnames(rna),1,12))#Keeping only relevant bulk data
rna <- rna[,colnames(rna) %in% rownames(clinical)]#Keep only the patients with available bulk RNA seq
clinical_rna <- clinical[rownames(clinical) %in% colnames(rna),]#Need to reorder the colnames and rownames to run DESeq2
rna = rna[,order(colnames(rna))]
clinical_rna = clinical_rna[order(rownames(clinical_rna)),]3.1 Running DESEq2 model
#Run DESEq2 model
dds <- DESeq2::DESeqDataSetFromMatrix(countData = rna,
colData = clinical_rna,
design = ~ MGC_Three)
dds <- DESeq2::DESeq(dds)
DESeq2::resultsNames(dds) # lists the coefficients[1] "Intercept" "MGC_Three_MGC.Low_vs_MGC.High"
[3] "MGC_Three_MGC.Medium_vs_MGC.High"dds_vst <- DESeq2::vst(dds)
matrix_vst <- SummarizedExperiment::assay(dds_vst)#Retrieving results
res <- DESeq2::results(dds, name="MGC_Three_MGC.Low_vs_MGC.High")
#Change sign of Log2Fold Change
res$log2FoldChange = -res$log2FoldChangeView(as.data.frame(res))#Save the DEGs into a csv file
write.csv(as.data.frame(res), file="MGC_High_Vs_MGC_Low_DEGs.csv")3.2 Volcano Plot
EnhancedVolcano::EnhancedVolcano(res,
lab = rownames(res),
x = "log2FoldChange",
y = "padj",
title = "",
subtitle = "MGC Low vs. MGC High",
selectLab = c("MARCO",
"CHIT1",
"DCSTAMP",
"ARG1",
"FBP1",
"SPP1",
"LPL",
"ITGAX",
"OSCAR",
"SULT1C2"),
xlab = bquote(~Log[2]~ "fold change"),
ylab = bquote(~-Log[10]~adjusted~italic(P)),
pCutoff = 0.05,
FCcutoff = 0.25,
colAlpha = 1,
legendPosition = "",
legendLabSize = 10,
legendIconSize = 3.0,
border = "full",
borderColour = "black",
gridlines.major = FALSE,
gridlines.minor = FALSE,
drawConnectors = TRUE,
widthConnectors = 1,
colConnectors = 'black',
ylim = c(0,6) ,
boxedLabels = TRUE,
labCol = 'black',
labFace = 'bold')
3.3 MA Plot
ggpubr::ggmaplot(res, main = expression("MGC High" %->% "MGC Low"),
fdr = 0.05, fc = 0.25, size = 0.4,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(res$name),
legend = "top", top = 20,
font.label = c("bold", 11), label.rectangle = TRUE,
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
3.4 Bulk RNAseq signature genes
# Convert 'res' to a data frame
res <- as.data.frame(res)
# Select rows with significant gene data based on criteria
# Keep rows where 'padj' is less than 0.05, 'padj' is not missing (not NA), and 'log2FoldChange' is greater than 0.25
signif_res <- res[res$padj < 0.05 & !is.na(res$padj) & res$log2FoldChange > 0.25, ]
# Extract the gene names (row names) from the 'signif_res' data frame
signif_res <- rownames(signif_res)print(signif_res) [1] "MATK" "TENM1" "MARCO" "FAM184B"
[5] "PTGER3" "POLB" "NECAB2" "ABCC1"
[9] "POMC" "MMP8" "ARG1" "SPP1"
[13] "PLA2G5" "NCAN" "CHIT1" "TAF8"
[17] "FAM8A1" "FGFBP2" "DNASE2B" "KCNH5"
[21] "SLC27A2" "ITGAX" "ZNF18" "WIF1"
[25] "HK3" "ARL5A" "PITX2" "DCSTAMP"
[29] "FBP1" "HTRA4" "OSCAR" "NMRAL1P1"
[33] "CYP4F11" "FRG2C" "LPL" "BDNF"
[37] "RP11-218M22.1" "PRSS36" "KRT76" "CYP4F3"
[41] "MPPED1" "ATP13A5" "FAM69C" "KRT77"
[45] "SIGLEC15" "AADACL2" "SULT1C2" "TMEM116"
[49] "CES1" "C6orf15" "RP11-699L21.1" "LINC01272"
[53] "RP1-223B1.1" "AC079305.8" "RP1-27K12.2" "RP11-288G3.4"
[57] "ESRRAP2" "DUX4L26" "LINC01010" "RP11-431M7.3"
[61] "LINC00942" "RP11-20D14.6" "RP11-21L23.4" "CAP2P1"
[65] "RP11-384L8.1" "CTD-2135D7.5" "AC005336.4" "LINC01224"
[69] "RP1-47A17.1" "RP11-81A22.4" # Write the selected gene names to a CSV file named 'signatures_genes_bulk_RNAseq.csv'
write.csv(signif_res, 'signatures_genes_bulk_RNAseq.csv')3.5 Common gene signature with Visium Spots
# Helper function to display Venn diagram
display_venn <- function(x, ...){
library(VennDiagram)
grid.newpage()
venn_object <- venn.diagram(x, filename = NULL, ...)
grid.draw(venn_object)
}
Visium_MGC_Signature <- readr::read_csv("Visium_MGC_Signature.csv")
MGC_Markers <- Visium_MGC_Signature$...1
x <- list(MGC_Markers,signif_res)
# Further customization
display_venn(
x,
category.names = c("Visium","Bulk"),
# Circles
lwd = 1,
fill = c("#999999", "#E69F00"),
# Numbers
cex = 2,
# Set names
cat.cex = 1,
cat.fontface = "bold",
cat.default.pos = "outer")
3.6 Gene Ontology based on DEGs
# Create background dataset for hypergeometric testing using all genes tested for
#significance in the results
all_genes <- as.character(rownames(res))
# Extract significant results
signif_res <- res[res$padj < 0.05 & !is.na(res$padj) & res$log2FoldChange > 0.25, ]
signif_genes <- as.character(rownames(signif_res))
# Run GO enrichment analysis
ego <- clusterProfiler::enrichGO(gene = signif_genes,
universe = all_genes,
keyType = "SYMBOL",
OrgDb = org.Hs.eg.db::org.Hs.eg.db,
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.05,
readable = TRUE)
# Output results from GO analysis to a table
cluster_summary <- data.frame(ego)gg1 <- ggplot(cluster_summary,
aes(x = reorder(Description,p.adjust), y = -log10(p.adjust), size = -log10(p.adjust), fill = -log10(p.adjust))) +
expand_limits(y = 1) +
geom_point(shape = 21) +
scale_size(range = c(2.5,12.5)) +
scale_fill_continuous(low = 'royalblue', high = 'red4') +
xlab('') + ylab('Enrichment score') +
labs(
title = 'GO Biological processes',
subtitle = 'Enriched Terms',
caption = 'Cut-off lines drawn at equivalents of p=0.05, p=0.01, p=0.001') +
geom_hline(yintercept = c(-log10(0.05), -log10(0.01), -log10(0.001)),
linetype = c("dotted", "longdash", "solid"),
colour = c("black", "black", "black"),
size = c(0.5, 1.5, 3)) +
theme_bw(base_size = 24) +
theme(
legend.position = 'right',
legend.background = element_rect(),
plot.title = element_text(angle = 0, size = 16, face = 'bold', vjust = 1),
plot.subtitle = element_text(angle = 0, size = 14, face = 'bold', vjust = 1),
plot.caption = element_text(angle = 0, size = 12, face = 'bold', vjust = 1),
axis.text.x = element_text(angle = 0, size = 12, face = 'bold', hjust = 1.10),
axis.text.y = element_text(angle = 0, size = 12, face = 'bold', vjust = 0.5,color = cluster_summary$TopicNum),
axis.title = element_text(size = 12, face = 'bold'),
axis.title.x = element_text(size = 12, face = 'bold'),
axis.title.y = element_text(size = 12, face = 'bold'),
axis.line = element_line(colour = 'black'),
#Legend
legend.key = element_blank(), # removes the border
legend.key.size = unit(1, "cm"), # Sets overall area/size of the legend
legend.text = element_text(size = 14, face = "bold"), # Text size
title = element_text(size = 14, face = "bold")) +
coord_flip()
gg1
#To color genes by log2 fold changes
signif_res_lFC <- signif_res$log2FoldChange
clusterProfiler::cnetplot(ego,
categorySize="pvalue",
showCategory = 12,
foldChange= signif_res_lFC,
vertex.label.font=6) 
4 Session Info
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.2.1 (2022-06-23)
os macOS Big Sur ... 10.16
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Paris
date 2024-01-23
pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0)
annotate 1.74.0 2022-04-26 [1] Bioconductor
AnnotationDbi 1.58.0 2022-04-26 [1] Bioconductor
ape 5.7-1 2023-03-13 [1] CRAN (R 4.2.0)
aplot 0.1.10 2023-03-08 [1] CRAN (R 4.2.0)
backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
Biobase 2.58.0 2022-11-01 [1] Bioconductor
BiocGenerics 0.44.0 2022-11-01 [1] Bioconductor
BiocParallel 1.32.5 2022-12-23 [1] Bioconductor
Biostrings 2.64.1 2022-08-18 [1] Bioconductor
bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.2.0)
blob 1.2.4 2023-03-17 [1] CRAN (R 4.2.0)
broom 1.0.5 2023-06-09 [1] CRAN (R 4.2.0)
cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
car 3.1-2 2023-03-30 [1] CRAN (R 4.2.0)
carData 3.0-5 2022-01-06 [1] CRAN (R 4.2.0)
cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
clusterProfiler 4.4.4 2022-06-21 [1] Bioconductor
codetools 0.2-19 2023-02-01 [1] CRAN (R 4.2.0)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
data.table 1.14.8 2023-02-17 [1] CRAN (R 4.2.0)
DBI 1.1.3 2022-06-18 [1] CRAN (R 4.2.0)
DelayedArray 0.24.0 2022-11-01 [1] Bioconductor
DESeq2 1.36.0 2022-04-26 [1] Bioconductor
digest 0.6.32 2023-06-26 [1] CRAN (R 4.2.0)
DO.db 2.9 2022-10-07 [1] Bioconductor
DOSE 3.22.1 2022-08-30 [1] Bioconductor
downloader 0.4 2015-07-09 [1] CRAN (R 4.2.0)
dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
EnhancedVolcano 1.14.0 2022-04-26 [1] Bioconductor
enrichplot 1.16.2 2022-08-30 [1] Bioconductor
evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.0)
fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.1)
farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
fastmatch 1.1-3 2021-07-23 [1] CRAN (R 4.2.0)
fgsea 1.22.0 2022-04-26 [1] Bioconductor
formatR 1.14 2023-01-17 [1] CRAN (R 4.2.0)
futile.logger * 1.4.3 2016-07-10 [1] CRAN (R 4.2.0)
futile.options 1.0.1 2018-04-20 [1] CRAN (R 4.2.0)
genefilter 1.78.0 2022-04-26 [1] Bioconductor
geneplotter 1.74.0 2022-04-26 [1] Bioconductor
generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
GenomeInfoDb 1.34.9 2023-02-02 [1] Bioconductor
GenomeInfoDbData 1.2.9 2023-02-27 [1] Bioconductor
GenomicRanges 1.50.2 2022-12-16 [1] Bioconductor
ggforce 0.4.1 2022-10-04 [1] CRAN (R 4.2.0)
ggfun 0.1.1 2023-06-24 [1] CRAN (R 4.2.0)
ggnewscale 0.4.9 2023-05-25 [1] CRAN (R 4.2.0)
ggplot2 * 3.4.3 2023-08-14 [1] CRAN (R 4.2.1)
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ggpubr 0.6.0 2023-02-10 [1] CRAN (R 4.2.0)
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GO.db 3.15.0 2022-10-07 [1] Bioconductor
GOSemSim 2.22.0 2022-04-26 [1] Bioconductor
graphlayouts 1.0.0 2023-05-01 [1] CRAN (R 4.2.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.0)
gridGraphics 0.5-1 2020-12-13 [1] CRAN (R 4.2.0)
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hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
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KEGGREST 1.36.3 2022-07-14 [1] Bioconductor
knitr 1.43 2023-05-25 [1] CRAN (R 4.2.0)
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lambda.r 1.2.4 2019-09-18 [1] CRAN (R 4.2.0)
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lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1)
locfit 1.5-9.8 2023-06-11 [1] CRAN (R 4.2.0)
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MASS 7.3-60 2023-05-04 [1] CRAN (R 4.2.0)
Matrix 1.5-4.1 2023-05-18 [1] CRAN (R 4.2.0)
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nlme 3.1-162 2023-01-31 [1] CRAN (R 4.2.0)
org.Hs.eg.db 3.15.0 2022-10-07 [1] Bioconductor
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RCurl 1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
readr 2.1.4 2023-02-10 [1] CRAN (R 4.2.0)
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rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
rmarkdown 2.23 2023-07-01 [1] CRAN (R 4.2.0)
RSQLite 2.3.1 2023-04-03 [1] CRAN (R 4.2.0)
rstatix 0.7.2 2023-02-01 [1] CRAN (R 4.2.0)
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stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
SummarizedExperiment 1.28.0 2022-11-01 [1] Bioconductor
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withr 2.5.1 2023-09-26 [1] CRAN (R 4.2.1)
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XML 3.99-0.14 2023-03-19 [1] CRAN (R 4.2.0)
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XVector 0.38.0 2022-11-01 [1] Bioconductor
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yulab.utils 0.0.6 2022-12-20 [1] CRAN (R 4.2.0)
zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
[1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
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