TCGA
Stefano Cacciatore
February 18, 2025
Last updated: 2025-02-18
Checks: 7 0
Knit directory: Tutorials/
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| html | 5baa04e | tkcaccia | 2025-02-17 | Build site. |
| html | f26aafb | tkcaccia | 2025-02-17 | Build site. |
| html | 1ce3cb4 | tkcaccia | 2025-02-16 | Build site. |
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| Rmd | fa7cd0a | tkcaccia | 2024-10-10 | updates |
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| Rmd | cbb2736 | tkcaccia | 2024-09-18 | Start my new project |
miRseq Analysis:
Analysing miRseq Gene Expression Data from a Colerectal Adenocarcinoma Cohort:
# install.packages("readxl")
library(readxl)
library(KODAMA)Prepare Clinical Data:
# Read in Clinical Data:
coad=read.csv("../Data/TCGA/COAD.clin.merged.picked.txt",sep="\t",check.names = FALSE, row.names = 1)
coad <- as.data.frame(coad)
# Clean column names: replace dots with dashes & convert to uppercase
colnames(coad) = toupper(colnames(coad))
# Transpose the dataframe so that rows become columns and vice versa
coad = t(coad) Prepare miRNA-seq expression data:
# Read RNA-seq expression data:
r = read.csv("../Data/TCGA/COAD.rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data.txt", sep = "\t", check.names = FALSE, row.names = 1)
# Remove the first row:
r = r[-1,]
# Convert expression data to numeric matrix format
temp = matrix(as.numeric(as.matrix(r)), ncol=ncol(r))
colnames(temp) = colnames(r)
rownames(temp) = rownames(r)
RNA = temp
# Transpose the matrix so that genes are rows and samples are columns
RNA = t(RNA) Extract patient and tissue information from column names:
tcgaID = list()
# Extract sample ID
tcgaID$sample.ID <- substr(colnames(r), 1, 16)
# Extract patient ID
tcgaID$patient <- substr(colnames(r), 1, 12)
# Extract tissue type
tcgaID$tissue <- substr(colnames(r), 14, 16)
tcgaID = as.data.frame(tcgaID) Select Primary Solid Tumor tissue data (“01A”):
sel=tcgaID$tissue == "01A"
tcgaID.sel = tcgaID[sel, ]
# Subset the RNA expression data to match selected samples
RNA.sel = RNA[sel, ]Intersect patient IDs between clinical and RNA data:
sel = intersect(tcgaID.sel$patient, rownames(coad))
# Subset the clinical data to include only selected patients:
coad.sel = coad[sel, ]
# Assign patient IDs as row names to the RNA data:
rownames(RNA.sel) = tcgaID.sel$patient
# Subset the RNA data to include only selected patients
RNA.sel = RNA.sel[sel, ]Prepare labels for pathology stages:
Classify stages
t1,t2, &t3as “low”Classify stages
t4,t4a, &t4bas “high”Convert any
tisstages toNA
labels = coad.sel[, "pathology_T_stage"]
labels[labels %in% c("t1", "t2", "t3", "tis")] = "low"
labels[labels %in% c("t4", "t4a", "t4b")] = "high"Log Transform the expression data for our selected gene
CXCL2:
CXCL2 <- log(1 + RNA.sel[, "CXCL2|2920"])
LCN2 <- log(1 + RNA.sel[,"LCN2|3934" ])Boxplot to visualize the distribution of log transformed gene expression by pathology stage:
colors=c("#0073c2bb","#efc000bb","#868686bb","#cd534cbb","#7aabdcbb","#003c67bb")
library(ggpubr)Loading required package: ggplot2df=data.frame(variable=CXCL2,labels=labels)
my_comparisons=list()
my_comparisons[[1]]=c(1,2)
Nplot1=ggboxplot(df, x = "labels", y = "variable",fill="labels",
width = 0.8,
palette=colors,
add = "jitter",
add.params = list(size = 2, jitter = 0.2,fill=3, shape=10))+
ylab("CXCL2 gene expression (FPKM)")+ xlab("")+
stat_compare_means(comparisons = my_comparisons,method="wilcox.test")
Nplot1
| Version | Author | Date |
|---|---|---|
| a7f82c5 | tkcaccia | 2024-09-18 |
Enrichment analysis
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")Bioconductor version '3.18' is out-of-date; the current release version '3.20'
is available with R version '4.4'; see https://bioconductor.org/installBiocManager::install("GSVA")'getOption("repos")' replaces Bioconductor standard repositories, see
'help("repositories", package = "BiocManager")' for details.
Replacement repositories:
CRAN: https://cran.rstudio.com/Bioconductor version 3.18 (BiocManager 1.30.23), R 4.3.3 (2024-02-29)Warning: package(s) not installed when version(s) same as or greater than current; use
`force = TRUE` to re-install: 'GSVA'Old packages: 'abind', 'ade4', 'anytime', 'ape', 'arrow', 'BH', 'BiocManager',
'bit', 'bit64', 'bitops', 'bookdown', 'boot', 'BRISC', 'broom',
'broom.helpers', 'bslib', 'car', 'caret', 'caTools', 'class', 'classInt',
'cli', 'clock', 'cluster', 'commonmark', 'coro', 'corrplot', 'cpp11',
'credentials', 'curl', 'data.table', 'dbscan', 'dendextend', 'Deriv',
'DescTools', 'digest', 'doBy', 'dotCall64', 'e1071', 'emayili', 'emmeans',
'EnvStats', 'evaluate', 'evd', 'expm', 'fastDummies', 'fastmatch', 'fastPLS',
'fields', 'fitdistrplus', 'FNN', 'fontawesome', 'foreign', 'fs',
'future.apply', 'geojsonR', 'gert', 'ggiraph', 'ggrepel', 'ggstats', 'git2r',
'gld', 'glue', 'golem', 'gower', 'gplots', 'graphlayouts', 'gtable',
'hardhat', 'harmony', 'hdf5r', 'httr2', 'igraph', 'KernSmooth', 'knitr',
'labelled', 'later', 'lava', 'leidenAlg', 'lme4', 'lmom', 'locfit', 'logger',
'lpSolve', 'lubridate', 'magick', 'maps', 'matrixStats', 'microbenchmark',
'minqa', 'mvtnorm', 'nlme', 'nnet', 'parabar', 'parallelly', 'patchwork',
'phangorn', 'phytools', 'pillar', 'pkgbuild', 'pkgdown', 'poweRlaw',
'processx', 'profvis', 'progressr', 'promises', 'ps', 'purrr', 'quantreg',
'R.oo', 'R6', 'ragg', 'randomForest', 'RANN', 'raster', 'RcppParallel',
'recipes', 'Rfast', 'rgl', 'rjson', 'rjtools', 'rpart', 'RSQLite',
'rstudioapi', 'sessioninfo', 'Seurat', 'sf', 'sfsmisc', 'shiny',
'shinyWidgets', 'Signac', 'sodium', 'sp', 'spam', 'spatial', 'spatstat.data',
'spatstat.explore', 'spatstat.geom', 'spatstat.random', 'spatstat.univar',
'spatstat.utils', 'spData', 'spdep', 'survival', 'survminer', 'systemfonts',
'terra', 'testthat', 'textshaping', 'timeDate', 'tinytex', 'torch', 'unix',
'usethis', 'waldo', 'wk', 'writexl', 'xfun', 'XML', 'zip'install.packages("GSA")
The downloaded binary packages are in
/var/folders/p9/7vjxs6dd7p70vybzdhy_0hw00000gn/T//RtmpfaEhpG/downloaded_packageslibrary("GSVA")
library("GSA")
library("KODAMA")
genes=t(RNA.sel)
t=unlist(lapply(strsplit(rownames(genes),"\\|"),function(x) x[1]))
selt=ave(1:length(t), t, FUN = length)
genes=genes[selt==1,]
rownames(genes)=t[selt==1]
geneset=GSA.read.gmt("../Data/Genesets/msigdb_v2023.2.Hs_GMTs/c2.cp.kegg_legacy.v2023.2.Hs.symbols.gmt")1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798991001011021031041051061071081091101111121131141151161171181191201211221231241251261271281291301311321331341351361371381391401411421431441451461471481491501511521531541551561571581591601611621631641651661671681691701711721731741751761771781791801811821831841851861
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185names(geneset$genesets)=geneset$geneset.names
geneset=geneset$genesets
#gsva_TCGA <- gsva(genes, geneset,min.sz = 5)
gsvapar = gsvaParam(genes,geneset)
gsva_TCGA=gsva(gsvapar)Warning in .filterFeatures_newAPI(dataMatrix): 283 rows with constant values
throughout the samples.Warning in .filterFeatures_newAPI(dataMatrix): Rows with constant values are
discarded.Estimating GSVA scores for 186 gene sets.
Estimating ECDFs with Gaussian kernels
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|==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%ma=multi_analysis(t(gsva_TCGA),CXCL2,FUN="correlation.test",method="spearman")
ma=ma[order(as.numeric(ma$`p-value`)),]
ma[1:10,] Feature rho
109 KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.43
171 KEGG_TIGHT_JUNCTION -0.32
99 KEGG_MELANOGENESIS -0.31
186 KEGG_WNT_SIGNALING_PATHWAY -0.31
3 KEGG_ADHERENS_JUNCTION -0.29
182 KEGG_VASOPRESSIN_REGULATED_WATER_REABSORPTION -0.29
133 KEGG_PPAR_SIGNALING_PATHWAY -0.28
153 KEGG_RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.27
22 KEGG_BASAL_CELL_CARCINOMA -0.27
51 KEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION 0.27
p-value FDR
109 6.72e-10 1.25e-07
171 6.09e-06 5.66e-04
99 1.12e-05 6.93e-04
186 1.71e-05 7.97e-04
3 5.45e-05 1.87e-03
182 6.02e-05 1.87e-03
133 1.05e-04 2.79e-03
153 1.54e-04 3.58e-03
22 1.99e-04 4.03e-03
51 2.17e-04 4.03e-03ma=multi_analysis(t(gsva_TCGA),LCN2,FUN="correlation.test",method="spearman")
ma=ma[order(as.numeric(ma$`p-value`)),]
ma[1:10,] Feature rho p-value FDR
153 KEGG_RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.35 8.04e-07 1.46e-04
42 KEGG_CYTOSOLIC_DNA_SENSING_PATHWAY 0.34 2.23e-06 1.46e-04
41 KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION 0.34 2.36e-06 1.46e-04
87 KEGG_INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION 0.33 3.61e-06 1.49e-04
135 KEGG_PRIMARY_IMMUNODEFICIENCY 0.33 4.01e-06 1.49e-04
109 KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.32 5.46e-06 1.69e-04
14 KEGG_APOPTOSIS 0.32 6.49e-06 1.72e-04
172 KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.30 2.19e-05 5.10e-04
3 KEGG_ADHERENS_JUNCTION -0.30 3.31e-05 6.84e-04
186 KEGG_WNT_SIGNALING_PATHWAY -0.29 3.74e-05 6.96e-04
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Africa/Johannesburg
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] GSA_1.03.3 GSVA_1.50.5 BiocManager_1.30.23
[4] ggpubr_0.6.0 ggplot2_3.5.1 KODAMA_3.0
[7] Matrix_1.6-5 umap_0.2.10.0 Rtsne_0.17
[10] minerva_1.5.10 readxl_1.4.3 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rstudioapi_0.16.0 jsonlite_1.8.9
[3] magrittr_2.0.3 farver_2.1.2
[5] rmarkdown_2.29 fs_1.6.4
[7] zlibbioc_1.48.2 vctrs_0.6.5
[9] memoise_2.0.1 DelayedMatrixStats_1.24.0
[11] RCurl_1.98-1.16 askpass_1.2.1
[13] rstatix_0.7.2 htmltools_0.5.8.1
[15] S4Arrays_1.2.1 broom_1.0.6
[17] cellranger_1.1.0 Rhdf5lib_1.24.2
[19] SparseArray_1.2.4 rhdf5_2.46.1
[21] sass_0.4.9 bslib_0.8.0
[23] cachem_1.1.0 whisker_0.4.1
[25] lifecycle_1.0.4 pkgconfig_2.0.3
[27] rsvd_1.0.5 R6_2.5.1
[29] fastmap_1.2.0 GenomeInfoDbData_1.2.11
[31] MatrixGenerics_1.14.0 digest_0.6.36
[33] colorspace_2.1-1 AnnotationDbi_1.64.1
[35] S4Vectors_0.40.2 ps_1.7.7
[37] rprojroot_2.0.4 irlba_2.3.5.1
[39] RSpectra_0.16-2 GenomicRanges_1.54.1
[41] RSQLite_2.3.7 beachmat_2.18.1
[43] labeling_0.4.3 fansi_1.0.6
[45] httr_1.4.7 abind_1.4-5
[47] compiler_4.3.3 bit64_4.0.5
[49] withr_3.0.2 backports_1.5.0
[51] BiocParallel_1.36.0 carData_3.0-5
[53] DBI_1.2.3 highr_0.11
[55] HDF5Array_1.30.1 ggsignif_0.6.4
[57] openssl_2.3.2 DelayedArray_0.28.0
[59] tools_4.3.3 httpuv_1.6.15
[61] glue_1.7.0 callr_3.7.6
[63] rhdf5filters_1.14.1 promises_1.3.0
[65] grid_4.3.3 getPass_0.2-4
[67] generics_0.1.3 gtable_0.3.5
[69] tidyr_1.3.1 ScaledMatrix_1.10.0
[71] BiocSingular_1.18.0 car_3.1-2
[73] utf8_1.2.4 XVector_0.42.0
[75] BiocGenerics_0.48.1 pillar_1.9.0
[77] stringr_1.5.1 later_1.3.2
[79] dplyr_1.1.4 lattice_0.22-6
[81] bit_4.0.5 annotate_1.80.0
[83] tidyselect_1.2.1 SingleCellExperiment_1.24.0
[85] Biostrings_2.70.3 knitr_1.48
[87] git2r_0.33.0 IRanges_2.36.0
[89] SummarizedExperiment_1.32.0 stats4_4.3.3
[91] xfun_0.46 Biobase_2.62.0
[93] matrixStats_1.3.0 stringi_1.8.4
[95] yaml_2.3.10 evaluate_0.24.0
[97] codetools_0.2-20 tibble_3.2.1
[99] graph_1.80.0 cli_3.6.3
[101] xtable_1.8-4 reticulate_1.40.0
[103] munsell_0.5.1 processx_3.8.4
[105] jquerylib_0.1.4 Rcpp_1.0.14
[107] GenomeInfoDb_1.38.8 png_0.1-8
[109] XML_3.99-0.17 parallel_4.3.3
[111] blob_1.2.4 sparseMatrixStats_1.14.0
[113] bitops_1.0-8 GSEABase_1.64.0
[115] scales_1.3.0 purrr_1.0.2
[117] crayon_1.5.3 rlang_1.1.5
[119] KEGGREST_1.42.0