results_summary: Summary of the results Create a data.frame that displays...

In spell098/rnaseq_functions2: Contain various functions for rnaseq analysis

Description

Summary of the results Create a data.frame that displays every gene significant in any comparison and the p-value for each comparison.

Usage

results_summary(results, topTable3, adjust = "no")

Arguments

expr.matrix	A matrix of standardized data. Columns = samples, rows = genes,transcripts,CpG...
resultsSummary	A matrix of data from every gene that is significanlty different

Value

The significant rows of the initial expression matrix between at least two groups. Columns = samples, rows = genes,transcripts,CpG...

Author(s)

Simon J Pelletier

See Also

topTable

Examples

#Example with expression matrix
expr.matrix <- readRDS("data/expr_matrix_LGVD.rds")
type = "ensembl_gene_id"
design <- design_contrasts(get_names(colnames(expr.matrix)))
lm2 <- lm2Contrast(expr.matrix,design)
lm2.contrast = lm2[[1]]
contrasts=lm2[[2]]
contrast.matrix=lm2[[3]]
names <- get_names(expr.matrix)[[1]]
specie = "rnorvegicus"
attribute <- "ensembl_gene_id"
externalSymbol <- "rgd_symbol"
annotationFile <- paste0("annotations/databases/",specie,"_ensembl_",attribute,".csv")
#ensemblTable <- annotation_biomart(rownames(expr.matrix),specie,attribute)
ensemblTable <- read.csv(annotationFile)
genes <- ensemblTable[as.character(ensemblTable[,attribute]) != "",]
annotation <- annotate_ensembl(genes[,1],type)
annotations <- annotation[[1]]
go <- annotation[[2]]
genes_annotation_unique <- annotation[[3]]
design <- design_contrasts(get_names(colnames(expr.matrix)))
lm2 <- lm2Contrast(expr.matrix,design)
lm2.contrast = lm2[[1]]
contrasts=lm2[[2]]
contrast.matrix=lm2[[3]]
names <- get_names(expr.matrix)[[1]]
comparisons <- get_comparisons(names)
pvalue = 0.05
logFC = c(-1.3,1.3)
type <- "ensembl_gene_id"
results_list <- results_topTable(lm2.contrast,expr.toBind,pvalue,logFC,type,genes_annotation_unique,annotations,"BH",ensemblTable)
results = results_list[[1]]
topTable3 = results_list[[2]]
resultsSummary <- results_summary(results,topTable3,adjust="BH")

#Example with online dataset
gset <- getGEO("GSE54839", GSEMatrix =TRUE)
exprset <- gset[[1]]
type <- "ensembl_gene_id"
expr.matrix <- exprs(exprset)
names1 <- get_names(sampleNames(exprset))
comparisons <- comparisonsPheno(exprset)[[1]]
comparisonsTable <- comparisonsPheno(exprset)[[2]]
specie = "hsapiens"
attribute <- "illumina_humanht_12_v3"
externalSymbol <- "hgnc_symbol"
annotationFile <- paste0("annotations/databases/",specie,"_ensembl_",attribute,".csv")
#ensemblTable <- annotation_biomart(rownames(expr.matrix),specie,attribute)
ensemblTable <- read.csv(annotationFile)
genes <- ensemblTable[as.character(ensemblTable[,attribute]) != "",]
annotation <- annotate_ensembl(genes[,1],type)
annotations <- annotation[[1]]
go <- annotation[[2]]
genes_annotation_unique <- annotation[[3]]
pvalue = 0.05
expr.toBind <- NULL
logFC = c(-1,1)
selectedVariables = names(comparisons)[1]
namesTable <- comparisonsTable
names2 <- namesSelectedComparisons(as.data.frame(as.character(namesTable[,2])))
design = design_contrasts(names2)
bnet = readRDS('data/modules_characteristics_ch1.rds')
expr.toBind = exprToBind(bnet,voom.matrix)
lm2=lm2Contrast(expr.matrix,design)
lm2.contrast = lm2[[1]]
contrasts=lm2[[2]]
contrast.matrix=lm2[[3]]
results_list = results_topTable(lm2.contrast,expr.toBind,pvalue,logFC,type,genes_annotation_unique,annotations,adjust="none",ensemblTable)
results = results_list[[1]]
topTable3 = results_list[[2]]
resultsSummary = results_summary(results,topTable3,"no")

spell098/rnaseq_functions2 documentation built on May 30, 2019, 7:57 a.m.