getwd
setwd
setwd(Volumes/web/scaphapoda/Hannah/CLC_Data)
setwwd(/Volumes/web/scaphapoda/Hannah/CLC_Data)
setwd("Volumes/web/scaphapoda/Hannah/CLC_Data")
source("http://bioconductor.org/biocLite.R")
biocLite("DESeq")
library( "DESeq" )
??read.genepop
??read.genepop.gen
CountData<- read.table("FemalevMale_ExpressionValues.txt")
head(CountData)
Treatment <- factor( c("Lean","Siscowet") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
source("http://bioconductor.org/biocLite.R")
biocLite("DESeq")
library( "DESeq" )
??read.table
CountData <- read.table("MalevFemale_RPKM.txt",row.names=1, header=TRUE,)
source("http://bioconductor.org/biocLite.R")
biocLite("DESeq")
library( "DESeq" )
CountData<- read.table("FemalevMale_RPKM.txt",row.names=1, header=TRUE,)
CountData<- read.table("FemalevMale_RPKM.txt")
source("http://bioconductor.org/biocLite.R")
biocLite("DESeq")
library( "DESeq" )
CountData<- read.table("Female_vs_Male_UniqGeneRead.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Lean","Siscowet") )
cds <- newCountDataSet( CountData, Treatment )
cds <- newCountDataSet( 3, Treatment )
cds <- newCountDataSet( CountData, Treatment )
??CountData
cds <- newCountDataSet(  , Treatment )
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
read.table("Female_vs_Male_UniqGeneRead.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
??asinteger
??as.character
as.integer(as.character(DATA))
as.integer(as.character(CountData))
source("http://bioconductor.org/biocLite.R")#
biocLite("DESeq")
library( "DESeq" )
CountData<- read.table("Female_vs_Male_TotalGeneRead.txt",row.names=1, header=TRUE,)
CountData<- read.table("106Male_vs_Female.txt",row.names=1, header=TRUE,)
CountData<- read.table("FvM_UniqGRead.rtf",row.names=1, header=TRUE,)
head(FvM_UniqGRead.txt)
CountData<- read.table("FvM_UniqGRead.txt",row.names=1, header=TRUE,)
CountData<- read.table("106Male_vs_Female_4.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("FvM_5.txt",row.names=1, header=TRUE,)
head(CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
CountData<- read.table("106Male_vs_Female_4.txt",row.names=1, header=TRUE, colClasses=integer)
class(countData)
class(CountData)
source("http://bioconductor.org/biocLite.R")
biocLite("DESeq")
library( "DESeq"
library( "DESeq" )
CountData<- read.table("108Female_vs_Male_UniqueGeneRead_4.txt",row.names=1, header=TRUE,)
getwd()
source("http://bioconductor.org/biocLite.R")
biocLite("DESeq")
library( "DESeq" )
CountData<- read.table("108Female_vs_Male_UniqueGeneRead_4.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
Dispersons <- estimateDispersions( LibrarySize, method="blind", sharingMode="fit-only" )
DE <- nbinomTest( Dispersons, "Female", "Male" )
head (DE)
plotDE <- function (DE)
#
	plot(
#
		DE$baseMean, 
#
		DE$log2FoldChange,
#
		log="x", pch=20, cex=.3,
#
		col = ifelse (DE$padj < .05, "red", "black"))
??estimateDispersions
Dispersons <- estimateDispersions( LibrarySize, method="blind", sharingMode="fit-only", fitType="local" )
head (DE)
plotDE <- function (DE)#
+ 	plot(#
+ 		DE$baseMean, #
+ 		DE$log2FoldChange,#
+ 		log="x", pch=20, cex=.3,#
+ 		col = ifelse (DE$padj < .05, "red", "black"))#

plotDE <- function (DE)
#
	plot(
#
		DE$baseMean, 
#
		DE$log2FoldChange,
#
		log="x", pch=20, cex=.3,
#
		col = ifelse (DE$padj < .05, "red", "black"))
plotDE(DE)
hist(DE$pval, breaks=100, col="skyblue", border="slateblue", main="")
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_4.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_5.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_6.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_7.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_8.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_9.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_10.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_11.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_12.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_13.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_14.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_15.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_16.txt",row.names=1, header=TRUE,)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
CountData<- read.table("106Female_vs_Male_UniqueGeneReads_4.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
Dispersons <- estimateDispersions( LibrarySize, method="blind", sharingMode="fit-only" )
DE <- nbinomTest( Dispersons, "Female", "Male" )
head (DE)
plotDE <- function (DE)
#
	plot(
#
		DE$baseMean, 
#
		DE$log2FoldChange,
#
		log="x", pch=20, cex=.3,
#
		col = ifelse (DE$padj < .05, "red", "black"))
plotDE(DE)
hist(DE$pval, breaks=100, col="skyblue", border="slateblue", main="")
write.table(DE, "OlyBroodstock_DESeq.txt", row.names = FALSE, sep="\t")
CountData<- read.table("108Female_vs_Male_UniqueGeneRead_4.txt",row.names=1, header=TRUE,)
head (CountData)
Treatment <- factor( c("Female","Male") )
cds <- newCountDataSet( CountData, Treatment )
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
Dispersons <- estimateDispersions( LibrarySize, method="blind", sharingMode="fit-only" )
DE <- nbinomTest( Dispersons, "Female", "Male" )
Dispersons <- estimateDispersions( LibrarySize, method="blind", sharingMode="fit-only", fitType="local" )
DE <- nbinomTest( Dispersons, "Female", "Male" )
head (DE)
plotDE <- function (DE)
#
	plot(
#
		DE$baseMean, 
#
		DE$log2FoldChange,
#
		log="x", pch=20, cex=.3,
#
		col = ifelse (DE$padj < .05, "red", "black"))
plotDE(DE)
hist(DE$pval, breaks=100, col="skyblue", border="slateblue", main="")
write.table(DE, "OlyBroodstock_108_DESeq.txt", row.names = FALSE, sep="\t")
load("/Users/srlab/Downloads/R_workspace_106and108_Broodstock_UniqueGeneRead (1)")
library( "DESeq" )
CountData<- read.table("Multiple_OlyO_Broodstock_MalevsFemale.txt",row.names=1, header=TRUE,)
head (CountData)
conds <- c(rep("Male",2), rep("Female",2))
conds
cds <- newCountDataSet( CountData, conds )
head (counts (cds))
CountData<- read.table("Multiple_OlyO_Broodstock_MalevsFemale.txt",row.names=1, header=TRUE,)
head (CountData)
conds <- c(rep("Male",2), rep("Female",2))
conds
cds <- newCountDataSet( CountData, conds )
head (counts (cds))
LibrarySize <- estimateSizeFactors( cds )
sizeFactors( LibrarySize )
head( counts( LibrarySize, normalized=TRUE ) )
Disp <- estimateDispersions( LibrarySize )
str( fitInfo(Disp) )
plotDispEsts <- function (Disp)
{#
	plot(#
		rowMeans( counts(Disp, normalized=TRUE)),#
		fitInfo(Disp)$perGeneDispEsts,#
		pch = '.', log="xy")#
		xg <-10^seq(-.5, 5, length.out=300)#
		lines (xg, fitInfo(Disp)$dispFun(xg), col="red")#
}
plotDispEsts(Disp)
head ( fData(Disp))
str(fitInfo(Disp))
DE <- nbinomTest( Disp, "Male", "Female" )
head (DE)
plotDE <- function (DE)
plot(#
		DE$baseMean, #
		DE$log2FoldChange,#
		log="x", pch=20, cex=.3,#
		col = ifelse (DE$padj < .05, "red", "black"))
plotDE(DE)
plotDE(DE)
hist(DE$pval, breaks=100, col="skyblue", border="slateblue", main="")
write.table(DE, "OlyO_MalevFemale_DESeq.txt", row.names = FALSE, sep="\t")
plotDE(DE)