The useage of GeoTcgaData

Installation

Get the development version from github:

if(!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("huerqiang/GeoTcgaData")

Or the released version from CRAN:

install.packages("GeoTcgaData")

Introduction

GEO and TCGA provide us with a wealth of data, such as RNA-seq, DNA Methylation, and Copy number variation data. It’s easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

library(GeoTcgaData)

Common operations on GeoTcgaData

This is a basic example which shows you how to solve a common problem:

RNA-seq data integration and differential gene extraction

The function classify_sample and diff_gene could get the differentially expressioned genes using DESeq2 package. For examples:

library(DESeq2)
profile2 <- classify_sample(kegg_liver) 
jieguo <- diff_gene(profile2)

The parameter kegg_liver is a matrix or data.frame of gene expression data(count) in TCGA.

DNA Methylation data integration

The function Merge_methy_tcga could Merge methylation data downloaded from TCGA. This makes it easier to extract differentially methylated genes in the downstream analysis. For example:

dirr = system.file(file.path("extdata","methy"),package="GeoTcgaData")
merge_result <- Merge_methy_tcga(dirr)

Copy number variation data integration and differential gene extraction

The function ann_merge could merge the copy number variation data downloaded from TCGA using gdc. For example:

metadatafile_name <- "metadata.cart.2018-11-09.json"
jieguo2 <- ann_merge(dirr = system.file(file.path("extdata","cnv"),package="GeoTcgaData"),metadatafile=metadatafile_name)

The parameter dirr is a string for the direction of copy number variation data downloaded from TCGA. The parameter metadatafile is the metadata file download from TCGA. The function prepare_chi and differential_cnv could do chi-square test to find copy number variation differential genes. For example:

jieguo3 <- matrix(c(-1.09150,-1.47120,-0.87050,-0.50880,
-0.50880,2.0,2.0,2.0,2.0,2.0,2.601962,2.621332,2.621332,
                    2.621332,2.621332,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,
                    2.0,2.0,2.0,2.0,2.0,2.0,2.0),nrow=5)
rownames(jieguo3) <- c("AJAP1", "FHAD1", "CLCNKB", "CROCCP2", "AL137798.3")
colnames(jieguo3) <- c("TCGA-DD-A4NS-10A-01D-A30U-01", "TCGA-ED-A82E-01A-11D-A34Y-01", 
"TCGA-WQ-A9G7-01A-11D-A36W-01", "TCGA-DD-AADN-01A-11D-A40Q-01", 
"TCGA-ZS-A9CD-10A-01D-A36Z-01", "TCGA-DD-A1EB-11A-11D-A12Y-01")
rt <- prepare_chi(jieguo3)
chiResult <- differential_cnv(rt)

The parameter of prepare_chi is the result of function ann_merge and the parameter of function differential_cnv is the result of prepare_chi.

GEO chip data processing

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("Gene Symbol", "MARCH1", "MARC1", "MARCH1", "MARCH1", "MARCH1")
bb <- c("GSM1629982", "2.969058399", "4.722410064", "8.165514853", "8.24243893", "8.60815086")
cc <- c("GSM1629982", "3.969058399", "5.722410064", "7.165514853", "6.24243893", "7.60815086")
file1 <- data.frame(aa=aa,bb=bb,cc=cc)
result <- gene_ave(file1)

Multiple genes symbols may correspond to a same chip id. The result of function rep1 is to assign the expression of this id to each gene, and function rep2 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_fil <- data.frame(aa=aa,bb=bb,cc=cc)
rep1_result <- rep1(input_fil," /// ")
rep1_result <- rep2(input_fil," /// ")

Other downstream analyses

The function id_conversion_vector could convert gene id from one of symbol, RefSeq_ID, Ensembl_ID, NCBI_Gene_ID, UCSC_ID, and UniProt_ID to another. For example:

id_conversion_vector("symbol", "Ensembl_ID", c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS")) 
#> [1] "ENSG00000166535" "ENSG00000256661" "ENSG00000128274" "not available"  
#> [5] "ENSG00000094914"

Especially, the function id_conversion could convert ENSEMBL gene id to gene Symbol in TCGA. For example:

result <- id_conversion(profile)

The parameter profile is a data.frame or matrix of gene expression data in TCGA.

The function countToFpkm_matrix and countToTpm_matrix could convert count data to FPKM or TPM data.

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")
jieguo <- countToFpkm_matrix(lung_squ_count2)

lung_squ_count2 <- matrix(c(0.11,0.22,0.43,0.14,0.875,0.66,0.77,0.18,0.29),ncol=3)
rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3")
colnames(lung_squ_count2) <- c("sample1","sample2","sample3")
jieguo <- countToTpm_matrix(lung_squ_count2)

The function tcga_cli_deal could combine clinical information obtained from TCGA and extract survival data. For example:

tcga_cli <- tcga_cli_deal(system.file(file.path("extdata","tcga_cli"),package="GeoTcgaData"))