Hi ,
I am trying to convert a bioinformatics R script to use spark R. It uses
external bioconductor package (DESeq2) so the only conversion really I have
made is to change the way it reads the input file.
When I call my external R library function in DESeq2 I get error cannot
coerce class "data.frame" to a DataFrame .
I am listing my old R code and new spark R code below and the line giving
problem is in RED.
ORIGINAL R -
library(plyr)
library(dplyr)
library(DESeq2)
library(pheatmap)
library(gplots)
library(RColorBrewer)
library(matrixStats)
library(pheatmap)
library(ggplot2)
library(hexbin)
library(corrplot)
sampleDictFile <- "/160208.txt"
sampleDict <- read.table(sampleDictFile)
peaks <- read.table("/Atlas.txt")
countMat <- read.table("/cntMatrix.txt", header = TRUE, sep = "\t")
colnames(countMat) <- sampleDict$sample
rownames(peaks) <- rownames(countMat) <- paste0(peaks$seqnames, ":",
peaks$start, "-", peaks$end, " ", peaks$symbol)
peaks$id <- rownames(peaks)
############
#
SPARK R CODE
peaks <- (read.csv("/Atlas.txt",header = TRUE, sep = "\t")))
sampleDict<- (read.csv("/160208.txt",header = TRUE, sep = "\t",
stringsAsFactors = FALSE))
countMat<- (read.csv("/cntMatrix.txt",header = TRUE, sep = "\t"))
-------------------------------------------------------------------
COMMON CODE for both -
countMat <- countMat[, sampleDict$sample]
colData <- sampleDict[,"group", drop = FALSE]
design <- ~ group
* dds <- DESeqDataSetFromMatrix(countData = countMat, colData = colData,
design = design)*
This line gives error - dds <- DESeqDataSetFromMatrix(countData = countMat,
colData = (colData), design = design)
Error in DataFrame(colData, row.names = rownames(colData)) :
cannot coerce class "data.frame" to a DataFrame
I tried as.data.frame or using DataFrame to wrap the defs , but no luck.
What Can I do differently?
Thanks
Roni
