Dear all,

I am using edgeR to analyse one of my RNAseq data.

In this experiment, we have three tumour samples from three different
patients.

Named: Patient_1, Patient_2, Patient_3


We did RNASeq on Tumour-cells under two different conditions:  treated and
untreated, and wanted to find differential expressed genes after treatment.

In the end, we got following 6 RNASeq data (100bp, paired-end and
HiSeq2500):


Patient_1_treated,   Patient_1_untreated,

Patient_2_treated,   Patient_2_untreated,

Patient_3_treated,   Patient_3_untreated,


So for each condition (tread V.S untreated), we have three biological
replicates.


I followed <4.4 RNA-Seq of oral carcinomas vs matched normal tissue> in
edgeR User’s Guide to analysis these data, for this case study is very
similar to what we did.

However, as you can see, from the MDS-plot, Patient_1_treated is very close
to Patient_1_untreated on first two dimensions, which is same for Patient_2
or Patient_3.



plotMDS:  http://i.imgur.com/6AQVNhi.png


plotBCV : http://i.imgur.com/srqwuJC.png


So, without surprise, I end up with finding 0 differential expressed genes
(FDR<0.1)

My questions are:

1.Could I say that, especially basing on the result of MDS-plot, the
biological replicates are not consistent in my case, or the quality
(fitness for purpose) is very low.


2. Should we add more replicates to increased statistic power or trying
other statistic models, or you have another suggestion to deal with this
kind of data ?

Thank you for your suggesting in advances.


Regards,

Sheng

=======================

R code:

library(  "edgeR"  )

files <- dir( pattern=".read_cnt", full.names = FALSE)

RG <- readDGE( files )

colnames( RG$samples )

d <- DGEList( counts = RG )

y <- d

colnames(y)<-c( "Patient_1_treated",  "Patient_1_untreated",
"Patient_2_treated",  "Patient_2_untreated","Patient_3_treated",
 "Patient_3_untreated")

##==Filtering

keep <- rowSums(cpm(y) > 10 ) >= 3

y <- y[keep,]

dim(y)

##Re-compute the library sizes:

y$samples$lib.size <- colSums(y$counts)

y$samples


##Normalizing

y <- calcNormFactors(y)

y$samples


plotMDS(y, top=500, main ="Multi-Dimensional Scaling Plot for Count Data")

###The design matrix

Patient <- factor( c( "Patient_1", "Patient_1","Patient_2",
"Patient_2","Patient_3", "Patient_3" ) )

Treat <- factor( c( "Treated", "Untread", "Treated", "Untread","Treated",
"Untread") )

data.frame( Sample = colnames(y), Patient,Treat)

design <- model.matrix( ~Patient + Treat )

rownames( design ) <- colnames( y )

y <- estimateGLMCommonDisp( y, design, verbose = TRUE )

y <- estimateGLMTrendedDisp( y, design )

y <- estimateGLMTagwiseDisp( y, design )


fit <- glmFit(y, design)


lrt <- glmLRT( fit )


top <- topTags( lrt, n = 50 )

q_value = 0.05

summary( de <- decideTestsDGE( lrt, p.value = q_value ) )


q_value = 0.1

summary( de <- decideTestsDGE( lrt, p.value = q_value ) )

plotBCV( y, cex = 0.8)


#sessionInfo(  )

#R version 3.0.1 (2013-05-16)

#Platform: x86_64-apple-darwin10.8.0 (64-bit)

#

#locale:

#[1] C

#

#attached base packages:

#[1] splines   stats     graphics  grDevices utils     datasets  methods
base

#

#other attached packages:

#[1] ggplot2_0.9.3.1 biomaRt_2.16.0  edgeR_3.2.3     limma_3.16.5

#

#loaded via a namespace (and not attached):

# [1] MASS_7.3-27        RColorBrewer_1.0-5 RCurl_1.95-4.1     XML_3.95-0.2
      colorspace_1.2-2

# [6] dichromat_2.0-0    digest_0.6.3       grid_3.0.1         gtable_0.1.2
      labeling_0.2

#[11] munsell_0.4        plyr_1.8           proto_0.3-10
reshape2_1.2.2     scales_0.2.3

#[16] stringr_0.6.2      tools_3.0.1

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