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data_preprocess_v3.R
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library("limma")
source("metaAnalyze.R")
library("biomaRt")
ensembl = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl", host = "uswest.ensembl.org")
View(listDatasets(ensembl))
View(listFilters(ensembl))
q = listAttributes(ensembl)
View(q[grep("affy|agilent|illumina", q$name),])
#### AGILENT
####
parentFolderPath = "./Data_in"
studyName = "GSE100191"
x = read.maimages(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
source = "agilent", green.only = TRUE)
y = neqc(x, status = x$genes$ControlType, negctrl = -1, regular = 0)
colnames(y) = unname(fixGeoSampleNames(colnames(y)))
rownames(y$E) = y$genes$ProbeName
agilent_ensembl = c("agilent_wholegenome_4x44k_v2", "entrezgene")
mapping = getBM(attributes = agilent_ensembl,
mart = ensembl,
# filters = "agilent_wholegenome_4x44k_v2",
# values = rownames(y$E),
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene",
probeColname = "agilent_wholegenome_4x44k_v2")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE100191.rds")
#==================
studyName = "GSE68215"
# x = SCAN_TwoColor(inFilePattern = paste0(parentFolderPath, "/", studyName, "/*.txt.gz"))
x = read.maimages(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
source = "agilent", green.only = FALSE)
# y = x[,seq(1,72,2)]
x = new("EListRaw", list(E = x$R,
Eb = x$Rb,
targets = x$targets,
genes = x$genes,
source = x$source))
y = neqc(x, status = x$genes$ControlType, negctrl = -1, regular = 0)
colnames(y) = unname(fixGeoSampleNames(colnames(y)))
rownames(y$E) = y$genes$ProbeName
agilent_ensembl = c("agilent_wholegenome_4x44k_v1", "entrezgene")
mapping = getBM(attributes = agilent_ensembl,
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene",
probeColname = "agilent_wholegenome_4x44k_v1")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
any(is.na(exprsByGene))
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE68215.rds")
#=================
studyName = "GSE78068"
x = read.maimages(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
source = "agilent", green.only = TRUE)
y = neqc(x, status = x$genes$ControlType, negctrl = -1, regular = 0)
colnames(y) = unname(fixGeoSampleNames(colnames(y)))
rownames(y$E) = y$genes$ProbeName
agilent_ensembl = c("agilent_wholegenome_4x44k_v1", "entrezgene")
mapping = getBM(attributes = agilent_ensembl,
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene",
probeColname = "agilent_wholegenome_4x44k_v1")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
any(is.na(exprsByGene))
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE78068.rds")
### ILLUMINA ==========
studyName = "GSE37107"
q = read.ilmn(files = "GSE37107_non-normalized.txt.gz", path = paste0(parentFolderPath),
probeid = "PROBE_ID", expr = "RIT", other.columns = "Detection")
esetGEO = getGEO(filename = "./Data_in/GEO/GSE37107_series_matrix.txt.gz")
# pl = getGEO("GPL6947")
y = neqc(q)
colnames(y) = esetGEO$geo_accession
mapping = getBM(attributes = c("illumina_humanht_12_v3", "entrezgene"),
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene",
probeColname = "illumina_humanht_12_v3")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
any(is.na(exprsByGene))
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE37107.rds")
##================
studyName = "GSE48348"
w = read.csv( "./Data_in/GEO/GSE48348_non-normalized.txt.gz", sep = "\t", header = FALSE, stringsAsFactors = FALSE)
for (i in 1:(1468/2)) {
cat("Substituting sample", i, "\n")
w[1, 2*i] = paste0("Sample_S", i)
}
write_delim(w, path = "./Data_in/GEO/GSE48348_non-normalized_corr.txt", delim = "\t", col_names = FALSE)
q = read.ilmn(files = "GSE48348_non-normalized_corr.txt", path = paste0(parentFolderPath),
probeid = "ID_REF", expr = "Sample", other.columns = "Detection")
esetGEO = getGEO(filename = "./Data_in/GEO/GSE48348_series_matrix.txt.gz")
y = neqc(q)
colnames(y) = esetGEO$geo_accession
mapping = getBM(attributes = c("illumina_humanht_12_v3", "entrezgene"),
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene",
probeColname = "illumina_humanht_12_v3")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
any(is.na(exprsByGene))
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE48348.rds")
#===========
studyName = "GSE12051"
# x = read.maimages(files=dir(paste0(parentFolderPath, "/", studyName)), path=paste0(parentFolderPath, "/", studyName),
# source="smd", columns = list(E = "Beadstudio_intensity"))
q = read.ilmn(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
probeid = "ProbeSet_name",
expr = "Beadstudio_intensity")
mode(q$E) <- "numeric"
y = backgroundCorrect.matrix(q$E, method = "normexp", normexp.method = "mle", offset = 16)
y = log2(y)
y = normalizeQuantiles(y)
library("illuminaHumanv1.db")
x <- illuminaHumanv1ENTREZID
mapped_probes <- mappedkeys(x)
xx = as.list(x[mapped_probes])
z = xx[rownames(q)]
mapping = data.frame(unlist(z))
mapping$ID = rownames(mapping)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "unlist.z.",
probeColname = "ID")
exprsByGene = calcExprsByGeneEmat(y, mapping)
any(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
esetGEO = getGEO(filename = "./Data_in/GEO/GSE12051_series_matrix.txt.gz")
eset <- new("ExpressionSet", exprs = exprsByGene)
names = esetGEO$geo_accession#[which(esetGEO$description %in% colnames(eset))]
colnames(eset) = names
saveRDS(eset, "./Data_in/GEO/GSE12051.rds")
#=========
studyName = "GSE58795"
platformInfo = "gpl10379hursta2a520709custommmpm.cdf.gzcdf"
require(platformInfo, character.only=TRUE)
cwd = setwd(file.path(parentFolderPath, studyName))
# cdfFile = dir(pattern="cdf.gz")
# env = make.cdf.env(filename=cdfFile, compress=TRUE) #hursta2a520709cdf # gpl10379hursta2a520709custommmpm.cdf.gzcdf
require("gpl10379hursta2a520709custommmpm.cdf.gzcdf", character.only = TRUE)
celfiles = affy::list.celfiles()
eset = justRMA(filenames = celfiles,
cdfname = platformInfo,
normalize = TRUE,
background = TRUE,
verbose = TRUE,
destructive = FALSE)
setwd(cwd)
sampleNames(eset) = fixGeoSampleNames(sampleNames(eset))
pl = getGEO("GPL10379")
geneColname = "EntrezGeneID"
probeColname = "ID"
featureDf = pl@dataTable@table
idx = sapply(featureDf, is.factor)
featureDf[idx] = lapply(featureDf[idx], as.character) # convert factors into characters
mapping = getGeneProbeMappingDirect(featureDf, geneColname = geneColname, probeColname = probeColname)
exprsByGene = calcExprsByGene(eset, mapping)
sum(is.na(exprsByGene))
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset = ExpressionSet(assayData = exprsByGene,
phenoData = phenoData(eset),
experimentData = experimentData(eset))
saveRDS(eset, "./Data_in/GEO/GSE58795.rds")
#========
# SYNOVIUM
studyName = "GSE39340"
q = read.ilmn(files = "GSE39340_non_normalized.txt.gz", path = paste0(parentFolderPath),
probeid = "ID_REF", expr = "AVG_Signal", other.columns = "Detection")
esetGEO = getGEO(filename = "./Data_in/GEO/GSE39340_series_matrix.txt.gz")
# pl = getGEO("GPL6947")
y = neqc(q)
colnames(y) = esetGEO$geo_accession
mapping = getBM(attributes = c("illumina_humanht_12_v4", "entrezgene"),
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene",
probeColname = "illumina_humanht_12_v4")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
sum(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
# if (any(is.na(exprsByGene))) {
# warning(sprintf('Imputing missing expression values for study %s.', studyName))
# resultImputed = impute.knn(exprsByGene)
# exprsByGene = resultImputed$data
# }
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE39340.rds")
############################
RG <- read.maimages(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
source = "genepix",
other.columns = c("F635 SD","B635 SD","F532 SD",
"B532 SD","B532 Mean","B635 Mean","F Pixels","B Pixels"))
RGmodel <- kooperberg(RG)
#=========
studyName = "GSE21537"
x = read.maimages(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
source = "genepix.mean", green.only = FALSE)
x = new("EListRaw", list(E = x$R, Eb = x$Rb, targets = x$targets, genes = x$genes, source = x$source))
colnames(x) = unname(fixGeoSampleNames(colnames(x)))
x$genes$ID = gsub(" ", "", x$genes$ID)
pl = getGEO("GPL7768")
featureDf = pl@dataTable@table
idx = sapply(featureDf, is.factor)
featureDf[idx] = lapply(featureDf[idx], as.character) # convert factors into characters
mapping = getGeneProbeMappingDirect(featureDf,
geneColname = "Entrez Gene",
probeColname = "ID")
y = limma::backgroundCorrect(x, method = "normexp", normexp.method = "mle", offset = 16)
y$E = log2(y$E)
y$E = normalizeBetweenArrays(y$E, method = "quantile")
rownames(y$E) = y$genes$ID
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
sum(is.na(exprsByGene))
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE21537.rds")
## ====
studyName = "GSE3698"
x = read.maimages(files = dir(paste0(parentFolderPath, "/", studyName)),
path = paste0(parentFolderPath, "/", studyName),
source = "genepix.mean", green.only = TRUE)
colnames(x) = unname(fixGeoSampleNames(colnames(x)))
pl = getGEO("GPL3050")
featureDf = pl@dataTable@table
idx = sapply(featureDf, is.factor)
featureDf[idx] = lapply(featureDf[idx], as.character) # convert factors into characters
mapping = getGeneProbeMappingAnno(featureDf,
dbName = 'org.Hs.egREFSEQ2EG',
interName = "GB_LIST")
y = limma::backgroundCorrect(x, method = "normexp", normexp.method = "mle", offset = 16)
y$E = log2(y$E)
y$E = normalizeBetweenArrays(y$E, method = "quantile")
rownames(y$E) = y$genes$Name
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
sum(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
if (any(is.na(exprsByGene))) {
warning(sprintf('Imputing missing expression values for study %s.', studyName))
resultImputed = impute.knn(exprsByGene)
exprsByGene = resultImputed$data
}
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE3698.rds")
## ====
studyName = "GSE47726"
q = read.ilmn(files = "GSE47726_non-normalized.txt.gz", path = paste0(parentFolderPath),
probeid = "ID_REF", expr = "SAMPLE", other.columns = "Detection")
esetGEO = getGEO("GSE47726")
# pl = getGEO("GPL6947")
y = neqc(q)
colnames(y) = esetGEO$GSE47726_series_matrix.txt.gz$geo_accession
# HumanWG6_V2
# options(error = rlang::entrace)
mapping = getBM(attributes = c("illumina_humanwg_6_v3", "hgnc_symbol"),
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGEO("GPL8234")
mapping = mapping@dataTable@table[,1:2]
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "SYMBOL",
probeColname = "ID")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
sum(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
# if (any(is.na(exprsByGene))) {
# warning(sprintf('Imputing missing expression values for study %s.', studyName))
# resultImputed = impute.knn(exprsByGene)
# exprsByGene = resultImputed$data
# }
# colnames(exprsByGene) = fixGeoSampleNames(colnames(exprsByGene))
eset <- new("ExpressionSet", exprs = exprsByGene)
saveRDS(eset, "./Data_in/GEO/GSE47726.rds")
## ====
# GSE47727
studyName = "GSE47727"
q = read.ilmn(files = "GSE47727_non_normalized.txt.gz", path = paste0(parentFolderPath),
probeid = "ID_REF", expr = "Sample")
esetGEO = getGEO("GSE47727")
# pl = getGEO("GPL6947")
q$E = q$E[complete.cases(q$E),]
y = limma::backgroundCorrect(q, method = "normexp", normexp.method = "mle", offset = 0)
y$E = log2(y$E)
y$E = normalizeBetweenArrays(y$E, method = "quantile")
colnames(y$E) = esetGEO$GSE47727_series_matrix.txt.gz$geo_accession
# plotDensity(y$E)
# plot(prcomp(t(y$E))$x)
# HumanHT-12 V3.0
mapping = getBM(attributes = c("illumina_humanht_12_v3", "entrezgene_id"),
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene_id",
probeColname = "illumina_humanht_12_v3")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
sum(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
meta = pData(esetGEO$GSE47727_series_matrix.txt.gz)
meta = meta[, c("geo_accession", "title", "source_name_ch1", "characteristics_ch1", "characteristics_ch1.1",
"platform_id", "data_row_count", "age (yrs):ch1", "gender:ch1")]
meta$study = "GSE47727"
eset <- new("ExpressionSet", exprs = exprsByGene)
pData(eset) = meta
saveRDS(eset, "./Data_in/GEO/GSE47727.rds")
## ====
# GSE47728
studyName = "GSE47728"
q = read.ilmn(files = "GSE47728_non_normalized.txt.gz", path = paste0(parentFolderPath),
probeid = "ID_REF", expr = "Sample")
esetGEO = getGEO("GSE47728")
# pl = getGEO("GPL6947")
unique(which(is.na(q$E), arr.ind = T)[,1])
q$E = q$E[complete.cases(q$E),]
y = limma::backgroundCorrect(q, method = "normexp", normexp.method = "mle", offset = 0)
y$E = log2(y$E)
y$E = normalizeBetweenArrays(y$E, method = "quantile")
colnames(y$E) = esetGEO$GSE47728_series_matrix.txt.gz$geo_accession
# plotDensity(y$E)
# plot(prcomp(t(y$E))$x)
# HumanHT-12 V3.0
mapping = getBM(attributes = c("illumina_humanht_12_v4", "entrezgene_id"),
mart = ensembl,
verbose = F,
uniqueRows = T)
mapping = getGeneProbeMappingDirect(featureDf = mapping,
geneColname = "entrezgene_id",
probeColname = "illumina_humanht_12_v4")
exprsByGene = calcExprsByGeneEmat(y$E, mapping)
sum(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
meta = pData(esetGEO$GSE47728_series_matrix.txt.gz)
# meta = meta[, c("geo_accession", "title", "source_name_ch1", "characteristics_ch1", "characteristics_ch1.1", "platform_id", "data_row_count", "age (yrs):ch1", "gender:ch1")]
meta$study = "GSE47728"
eset <- new("ExpressionSet", exprs = exprsByGene)
pData(eset) = meta
saveRDS(eset, "./Data_in/GEO/GSE47728.rds")
###############
####### Others
###############
##### GSE29746
eset = getGEO("GSE29746")
emat = exprs(eset[[1]])
meta = pData(eset[[1]])
meta$class = case_when(
meta$`disease status:ch1` == "RASF (rheumatoid arthritis synovial fibroblasts)" ~ "RA",
meta$`disease status:ch1` == "HSF (healthy synovial fibroblasts)" ~ "Healthy",
meta$`disease status:ch1` == "OASF (osteoarthritis arthritis synovial fibroblasts)" ~ "OA"
)
colnames(meta)[!colnames(meta) %in% colnames(anno)][1] = "sample"
meta[,colnames(anno) %in% colnames(meta)]
meta$study = "GSE29746"
featureDf = eset[[1]]@featureData@data
mapping = getGeneProbeMappingDirect(featureDf,
geneColname = "GENE",
probeColname = "ID")
y = neqc(emat, status = featureDf$CONTROL_TYPE, negctrl = "neg", regular = "FALSE", offset = 0)
exprsByGene = calcExprsByGeneEmat(y, mapping)
sum(is.na(exprsByGene))
exprsByGene = exprsByGene[complete.cases(exprsByGene),]
emat = exprsByGene
save(emat, meta, file = "./Data_in/GEO/GSE29746.Rdata")
eset <- new("ExpressionSet", exprs = exprsByGene)
pData(eset) = meta
saveRDS(eset, "./Data_in/GEO/GSE29746.rds")
# GSE19821
studyName = "GSE19821"
q = getGEO("GSE19821")
GSE19821_A = exprs(q[[1]])
# # GSE2053
#
# studyName = "GSE2053"
# q = getGEO(filename = "Data_in/GSE2053_family.soft.gz")
#
# meta = foreach(sample = names(q@gsms)) %do% {
# q@gsms[[sample]]@header$characteristics_ch1
# }
#
# int = foreach(sample = names(q@gsms)) %do% {
# m = q@gsms[[sample]]@dataTable@table[,"SIGNAL_RAW"]
# names(m) = q@gsms[[sample]]@dataTable@table[,"ID_REF"]
# m
# }
# intB = foreach(sample = names(q@gsms)) %do% {
# m = q@gsms[[sample]]@dataTable@table[,"BKD_RAW"]
# names(m) = q@gsms[[sample]]@dataTable@table[,"ID_REF"]
# m
# }
# names(int) = names(intB) = names(meta) = gsm
#
# meta = do.call(rbind, meta)
# int = do.call(cbind, int)
# intB = do.call(cbind, intB)
#
# gpl = q@gpls$GPL1740
# mapping = gpl@dataTable@table[, c("ID", "GB_ACC")]
# colnames(mapping) = c('probeSet', 'geneId')
# class(mapping[,1]) = class(mapping[,2]) = "character"
# mapping[mapping == ""] = NA
# mapping = mapping[complete.cases(mapping),]
# rownames(int) = rownames(intB) = NULL
#
# mapping1 = getGeneProbeMappingAnno(mapping, dbName = 'org.Hs.egACCNUM2EG', interName = 'GB_ACC')
#
#
# targets = data.frame(colnames(int))
# rownames(targets) = targets[,1]
# x = new("EListRaw", list(E = int,
# #Eb = intB,
# targets = targets,
# genes = gpl@dataTable@table,
# source = "SMD Homo sapiens 37.6K Print_843"))
#
# y = limma::backgroundCorrect(x, method = "normexp", normexp.method = "mle", offset = 16)
# y$E = log2(y$E)
# y = normalizeBetweenArrays(y, method = "quantile")
#
# rownames(y$E) = y$genes$ID
# cat("First 5 probe ids: ", rownames(y$E)[1:5], "\n")
# exprsByGene = calcExprsByGeneEmat(y$E, mapping)
# cat("Number of missing values =", sum(is.na(exprsByGene)), "\n")
# eset <- new("ExpressionSet", exprs = exprsByGene)
# message("Correction is done!")
# eset = fixMissingData(eset)