The goal of GeoTcgaData is to deal with RNA-seq, DNA Methylation, single nucleotide Variation and Copy number variation data in GEO and TCGA.
Erqiang Hu
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University.
if(!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("YuLab-SMU/GeoTcgaData")
library(GeoTcgaData)
GEO and TCGA provide us with a wealth of data, such as RNA-seq, DNA
Methylation, single nucleotide Variation and Copy number variation data.
It’s easy to download data from TCGA using the gdc tool or
TCGAbiolinks
, and some software provides organized TCGA data, such as
UCSC Xena ,
UCSCXenaTools,and
sangerbox, but processing these data into a
format suitable for bioinformatics analysis requires more work. This R
package was developed to handle these data.
This is a basic example which shows you how to solve a common problem:
It is convenient to use
TCGAbiolinks
or GDCRNATools
to
download and analysis Gene expression data. TCGAbiolinks
use edgeR
package to do differential expression analysis, while GDCRNATools
can
implement three most commonly used methods: limma, edgeR , and DESeq2 to
identify differentially expressed genes (DEGs).
Alicia Oshlack et. al. claimed that unlike the chip data, the RNA-seq data had one bias: the larger the transcript length / mean read count , the more likely it was to be identified as a differential gene, while there was no such trend in the chip data.
However, when we use their chip data for difference analysis( using the limma package), we find that chip data has the same trend as RNA-seq data. And we also found this trend in the difference analysis results given by the data authors.
It is worse noting that only technical replicate data, which has small gene dispersions, shows this bias. This is because in technical replicate RNA-seq data a long gene has more reads mapping to it compared to a short gene of similar expression, and most of the statistical methods used to detect differential expression have stronger detection ability for genes with more reads. However, we have not deduced why there is such a bias in the current difference analysis algorithms.
Some software, such as CQN , present a normalization algorithm to correct systematic biases(gene length bias and GC-content bias. But they did not provide sufficient evidence to prove that the correction is effective. We use the Marioni dataset to verify the correction effect of CQN and find that there is still a deviation after correction:
GOseq based on Wallenius’ noncentral hypergeometric distribution can effectively correct the gene length deviation in enrichment analysis. However, the current RNA-seq data often have no gene length bias, but only the expression amount(read count) bias, GOseq may overcorrect these data, correcting originally unbiased data into reverse bias.
GOseq also fails to correct for expression bias, therefore, read count bias correction is still a challenge for us.
use TCGAbiolinks
to download TCGA data
# download RNA-seq data
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-ACC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts")
GDCdownload(query, method = "api", files.per.chunk = 3,
directory = Your_Path)
dataRNA <- GDCprepare(query = query, directory = Your_Path,
save = TRUE, save.filename = "dataRNA.RData")
## get raw count matrix
dataPrep <- TCGAanalyze_Preprocessing(object = dataRNA,
cor.cut = 0.6,
datatype = "STAR - Counts")
Use differential_RNA
to do difference analysis. We provide the data of
human gene length and GC content in gene_cov
.
group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE)
library(cqn) # To avoid reporting errors: there is no function "rq"
## get gene length and GC content
library(org.Hs.eg.db)
genes_bitr <- bitr(rownames(gene_cov), fromType = "ENTREZID", toType = "ENSEMBL",
OrgDb = org.Hs.eg.db, drop = TRUE)
genes_bitr <- genes_bitr[!duplicated(genes_bitr[,2]), ]
gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ]
rownames(gene_cov2) <- genes_bitr$ENSEMBL
genes <- intersect(rownames(dataPrep), rownames(gene_cov2))
dataPrep <- dataPrep[genes, ]
geneLength <- gene_cov2[genes, "length"]
gccontent <- gene_cov2[genes, "GC"]
names(geneLength) <- names(gccontent) <- genes
## Difference analysis
DEGAll <- differential_RNA(counts = dataPrep, group = group,
geneLength = geneLength, gccontent = gccontent)
Use clusterProfiler
to do enrichment analytics:
diffGenes <- DEGAll$logFC
names(diffGenes) <- rownames(DEGAll)
diffGenes <- sort(diffGenes, decreasing = TRUE)
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL")
dotplot(gsego)
use TCGAbiolinks
to download TCGA data.
The codes may need to be modified if TCGAbiolinks
updates. So please
read its
documents.
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-ACC",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Human Methylation 450")
GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path)
The function Merge_methy_tcga
could Merge methylation data downloaded
from TCGA official website or TCGAbiolinks. This makes it easier to
extract differentially methylated genes in the downstream analysis. For
example:
merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data)
Then use differential_methy() to do difference analysis.
# if (!requireNamespace("ChAMP", quietly = TRUE))
# BiocManager::install("ChAMP")
library(ChAMP) # To avoid reporting errors
differential_gene <- differential_methy(cpgData = merge_result, sampleGroup = sample(c("C","T"),
ncol(merge_result[[1]]), replace = TRUE))
Note: ChAMP
has a large number of dependent packages. If you cannot
install it successfully, you can download each dependent package
separately(Source or Binary) and install it locally.
If your methylation data was downloaded from UCSC
Xena, you can use
differential_methy(ucscData = TRUE)
to get differential genes.
methy_file <- "TCGA.THCA.sampleMap_HumanMethylation450.gz"
methy <- fread(methy_file, sep = "\t", header = T)
library(ChAMP)
myImport <- champ.import(directory=system.file("extdata",package="ChAMPdata"))
myfilter <- champ.filter(beta=myImport$beta,pd=myImport$pd,detP=myImport$detP,beadcount=myImport$beadcount)
cpg_gene <- hm450.manifest.hg19[, c("probeID", "gene_HGNC")]
## or use IlluminaHumanMethylation450kanno.ilmn12.hg19 to get annotation data
# library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
# ann <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
# class(ann) <- "data.frame"
# cpg_gene <- ann[,c("Name", "UCSC_RefGene_Name", "UCSC_RefGene_Group")]
methy_df <- differential_methy(methy, cpg_gene, ucscData = TRUE)
We provide three models to get methylation difference genes:
if model = “cpg”, step1: calculate difference cpgs; step2: calculate difference genes;
if model = “gene”, step1: calculate the methylation level of genes; step2: calculate difference genes.
We find that only model = “gene” has no deviation of CpG number.
Use clusterProfiler
to do enrichment analytics:
differential_gene$p.adj <- p.adjust(differential_gene$pvalue)
genes <- differential_gene[differential_gene$p.adj < 0.05, "gene"]
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = "SYMBOL")
dotplot(ego)
use TCGAbiolinks to download TCGA data(Gene Level Copy Number Scores)
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-LGG",
data.category = "Copy Number Variation",
data.type = "Gene Level Copy Number Scores")
GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path)
data <- GDCprepare(query = query,
directory = Your_Path)
Do difference analysis of gene level copy number variation data using
differential_CNV
class(data) <- "data.frame"
cnvData <- data[, -c(1,2,3)]
rownames(cnvData) <- data[, 1]
sampleGroup = sample(c("A","B"), ncol(cnvData), replace = TRUE)
diffCnv <- differential_CNV(cnvData, sampleGroup)
Use clusterProfiler
to do enrichment analytics:
pvalues <- diffCnv$pvalue * sign(diffCnv$odds)
genes <- rownames(diffCnv)[diffCnv$pvalue < 0.05]
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL")
dotplot(ego)
Use TCGAbiolinks to download TCGA data
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-ACC",
data.category = "Simple Nucleotide Variation",
data.type = "Masked Somatic Mutation",
workflow.type = "MuSE Variant Aggregation and Masking")
GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path)
data_snp <- GDCprepare(query = query,
directory = Your_Path)
Use differential_SNP_tcga
to do difference analysis
samples <- unique(data_snp$Tumor_Sample_Barcode)
sampleType <- sample(c("A","B"), length(samples), replace = TRUE)
names(sampleType) <- samples
pvalue <- differential_SNP_tcga(snpData = data_snp, sampleType = sampleType)
# merge pvalue
Use clusterProfiler
to do enrichment analysis
pvalue2 <- sort(pvalue, decreasing = TRUE)
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
gsego <- gseGO(pvalue2, OrgDb = org.Hs.eg.db, keyType = "SYMBOL")
dotplot(gsego)
The function gene_ave
could average the expression data of different
ids for the same gene in the GEO chip data. For example:
aa <- c("MARCH1","MARC1","MARCH1","MARCH1","MARCH1")
bb <- c(2.969058399,4.722410064,8.165514853,8.24243893,8.60815086)
cc <- c(3.969058399,5.722410064,7.165514853,6.24243893,7.60815086)
file_gene_ave <- data.frame(aa=aa,bb=bb,cc=cc)
colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983")
result <- gene_ave(file_gene_ave, 1)
Multiple genes symbols may correspond to a same chip id. The result of
function repAssign
is to assign the expression of this id to each
gene, and function repRemove
deletes the expression. For example:
aa <- c("MARCH1 /// MMA","MARC1","MARCH2 /// MARCH3","MARCH3 /// MARCH4","MARCH1")
bb <- c("2.969058399","4.722410064","8.165514853","8.24243893","8.60815086")
cc <- c("3.969058399","5.722410064","7.165514853","6.24243893","7.60815086")
input_file <- data.frame(aa=aa,bb=bb,cc=cc)
repAssign_result <- repAssign(input_file," /// ")
repRemove_result <- repRemove(input_file," /// ")
data(profile)
result <- id_conversion_TCGA(profile)
#>
#>
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning in clusterProfiler::bitr(rownames(profiles), fromType = "ENSEMBL", :
#> 22.22% of input gene IDs are fail to map...
The parameter profile is a data.frame or matrix of gene expression data in TCGA.
Note: In previous versions(\< 1.0.0) the id_conversion
and
id_conversion
used HGNC data to convert human gene id. In future
versions, we will use clusterProfiler::bitr
for ID conversion.
library(clusterProfiler)
#> clusterProfiler v4.10.0 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
#>
#> If you use clusterProfiler in published research, please cite:
#> T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
#>
#> 载入程辑包:'clusterProfiler'
#> The following object is masked from 'package:stats':
#>
#> filter
library(org.Hs.eg.db)
#> 载入需要的程辑包:AnnotationDbi
#> 载入需要的程辑包:stats4
#> 载入需要的程辑包:BiocGenerics
#>
#> 载入程辑包:'BiocGenerics'
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The 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, intersect, is.unsorted, lapply, Map, mapply,
#> match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#> Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
#> table, tapply, union, unique, unsplit, which.max, which.min
#> 载入需要的程辑包:Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 载入需要的程辑包:IRanges
#> 载入需要的程辑包:S4Vectors
#>
#> 载入程辑包:'S4Vectors'
#> The following object is masked from 'package:clusterProfiler':
#>
#> rename
#> The following object is masked from 'package:utils':
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#> findMatches
#> The following objects are masked from 'package:base':
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#>
#> 载入程辑包:'IRanges'
#> The following object is masked from 'package:clusterProfiler':
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#> slice
#> The following object is masked from 'package:grDevices':
#>
#> windows
#>
#> 载入程辑包:'AnnotationDbi'
#> The following object is masked from 'package:clusterProfiler':
#>
#> select
bitr(c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS"), fromType = "SYMBOL",
toType = "ENSEMBL", OrgDb = org.Hs.eg.db, drop = FALSE)
#> 'select()' returned 1:many mapping between keys and columns
#> Warning in bitr(c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS"), fromType =
#> "SYMBOL", : 40% of input gene IDs are fail to map...
#> SYMBOL ENSEMBL
#> 1 A2ML1 ENSG00000166535
#> 2 A2ML1-AS1 <NA>
#> 3 A4GALT ENSG00000128274
#> 4 A12M1 <NA>
#> 5 AAAS ENSG00000094914
#> 6 AAAS ENSG00000291836
countToFpkm
and countToTpm
could convert count data
to FPKM or TPM data.data(gene_cov)
lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3)
rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3")
colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3")
result <- countToFpkm(lung_squ_count2,
keyType = "SYMBOL",
gene_cov = gene_cov
)
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning in clusterProfiler::bitr(rownames(gene_cov), fromType = "ENTREZID", :
#> 0.07% of input gene IDs are fail to map...
result
#> sample1 sample2 sample3
#> DISC1 11449.25 18318.79 20036.18
#> TCOF1 29140.08 29140.08 29140.08
#> SPPL3 69473.39 55578.71 52105.04
lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3)
rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3")
colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3")
result <- countToTpm(lung_squ_count2,
keyType = "SYMBOL",
gene_cov = gene_cov
)
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning in clusterProfiler::bitr(rownames(gene_cov), fromType = "ENTREZID", :
#> 0.07% of input gene IDs are fail to map...
result
#> sample1 sample2 sample3
#> DISC1 104024.7 177787.5 197827.0
#> TCOF1 264758.8 282810.2 287714.3
#> SPPL3 631216.4 539402.3 514458.7
Note: Now the combined clinical data can be downloaded directly from TCGAbiolinks.
library(TCGAbiolinks)
## get BCR Biotab data
query <- GDCquery(project = "TCGA-ACC",
data.category = "Clinical",
data.type = "Clinical Supplement",
data.format = "BCR Biotab")
GDCdownload(query)
clinical.BCRtab.all <- GDCprepare(query)
names(clinical.BCRtab.all)
## get indexed data
clinical <- GDCquery_clinic(project = "TCGA-ACC", type = "clinical")
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