Sandra Fernandez Moya | 31 Jul 00:44 2014
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Question about median of replicates

Hello, I have a very important question, cause we are going to submit a paper in a few hours and now we realize
that maybe we have an error, so I want to check with experts beforehand. The thing is I used EdgeR for
comparison of 2 groups, Control and Group1, and 3 samples, Control:1 and Group1:2; I followed the basic
protocol, because I dont know so much about this analyses and I get a final logFC, that it makes sense and
also we checked in the lab. But, now, the referees asked if the logFC was from the data Group1 normalized and
with the mean of both and we realize that it was not the mean but seems to be the summ of the counts from each
sample of Group1 the ones that the software take for the analysis. Maybe I did something wrong, but can you
confirm me this? It shouldnt be, but does Edg
 eR summ the counts of each replicate and uses it for the analysis?Thanks a lot, and I wait for the
answer!Sandra  		 	   		  
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Rao,Xiayu | 31 Jul 00:01 2014

multi-level design - a simplified question - corrected table

Hello all,

I do need some help on analyzing such unorganized data. Please help me out. Thank you so much!
I basically followed the analysis of multi-level experiments in limma user guide. But I do not feel right
about the code below. Please give me some suggestions.

# I want to compare Normal vs. Tumor negative,  and Normal vs Tumor positive. There are partial pairing
(subject) and batch effect (chip).
Treat <- factor(paste(targets$chip,targets$type,sep="."))
design <- model.matrix(~0+Treat)
colnames(design) <- levels(Treat)

corfit <- duplicateCorrelation(y,design,block=targets$subject)
corfit$consensus
fit <- lmFit(y,design,block=targets$subject,correlation=corfit$consensus)
cm <- makeContrasts(TposvsN=(a1.Tpos+a2.Tpos+a3.Tpos)/3-(a1.N+a2.N)/2,
TnegvsN=(a1.Tneg+a3.Tneg)/2-(a1.N+a2.N)/2, levels=design)        ????
fit2 <- contrasts.fit(fit, cm)
fit2 <- eBayes(fit2)
topTable(fit2, coef=1, sort.by="p")

sample	type	subject	chip 
s1	Tneg	1	a1
s2	N	1	a1
s3	Tpos	2	a1
s4	N	2	a1
s5	Tneg	3	a1
s6	N	3	a1
s7	Tpos	4	a1
s8	N	4	a1
(Continue reading)

Rao,Xiayu | 30 Jul 23:51 2014

multi-level design - a simplified question

Hello all,

I do need some help on analyzing such unorganized data. Please help me out. Thank you so much!
I basically followed the analysis of multi-level experiments in limma user guide. But I do not feel right
about the code below. Please give me some suggestions.

# I want to compare Normal vs. Tumor negative,  and Normal vs Tumor positive. There are partial pairing
(subject) and batch effect (chip).
Treat <- factor(paste(targets$chip,targets$type,sep="."))
design <- model.matrix(~0+Treat)
colnames(design) <- levels(Treat)

corfit <- duplicateCorrelation(y,design,block=targets$subject)
corfit$consensus
fit <- lmFit(y,design,block=targets$subject,correlation=corfit$consensus)
cm <- makeContrasts(TposvsN=(a1.Tpos+a2.Tpos+a3.Tpos)/3-(a1.N+a2.N)/2,
TnegvsN=(a1.Tneg+a3.Tneg)/2-(a1.N+a2.N)/2, levels=design)        ????
fit2 <- contrasts.fit(fit, cm)
fit2 <- eBayes(fit2)
topTable(fit2, coef=1, sort.by="p")

sample

type

subject

chip

s1
(Continue reading)

rob yang | 30 Jul 22:53 2014
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DESEQ2 Contrasting Interaction Terms

Hi List,
I am having a bit trouble pinning down the interaction terms in DESEQ2. I have followed the user manual, read
previous q&a's, and thought I understood. But upon actual application, I realized that I am still
confused. 

For simplicity reasons, I will just ask my questions based off the example in user manual 1.5, under section 3.3.

	
		
		
	
	
		
			
				
					

>The main effect of OHT treatment over control for all patients can be extracted with the following:

				
			
			
				
					
						>resOHT <- results(ddsX, contrast = c("treatment","OHT","Control"))

					
				
			
		
(Continue reading)

Nikolas Balanis | 30 Jul 17:43 2014

DNA Copy CNA function , what do you pass to maploc argument?

This is somewhat related to this question, I just want some more
clarification on using the CNA function. This is probably very elementary,
I apologize in advance

https://stat.ethz.ch/pipermail/bioconductor/2007-September/019138.html

>From an agilent data set the SystematicName column is composed of elements
of the form chr3:175483690-175485000. So for example for this element the
chromosome # is 3, so the vector of these #s for all elements is passed as
chrom in the CNA function. But what is the position numeric passed to
maploc in the CNA function? From the documentation the maploc argument is
"the locations of marker on the genome. Vector of length same as the number
of rows of genomdat. This has to be numeric."  So it has to be numeric from
the DNAcopy documentation. Is it the first value 175483690, the second
value 175485000 , or the average of the two 175484345, or something else
that I am missing completely? I cant seem to find any clarification on this
anywhere.

Nikolas Balanis

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Asma rabe | 30 Jul 17:06 2014
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Error in getting exons sequence

Hi,

When I tried to get the exons sequence, I got an error..

library("BSgenome.Hsapiens.UCSC.hg19")

txdb<-TxDb.Hsapiens.UCSC.hg19.knownGene

tx_Exons<-exonsBy(txdb)

 tx1<-tx_Exons[1:3]

(seq<-getSeq(Hsapiens,tx1))

Error in .toGRanges(names, strand) : invalid 'names'

Any help is apreciated
Thank you.

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Asma rabe | 30 Jul 17:03 2014
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deepSNV error

Hi Dario,

Thank you for help last time.

I wonder if deepSNV and shearwater were designed to call variants from
exome-seq (genome wide) data or it is designed just for calling variants
from small scale data (like few genes...)

When i try to run deepSNV  for single exon i got  an error

library("TxDb.Hsapiens.UCSC.hg19.knownGene")

txdb<-TxDb.Hsapiens.UCSC.hg19.knownGene

tx_Exons<-exonsBy(txdb)

gr<-tx_Exons[1:3]

dpSNV1<-deepSNV(test="test.sorted.bam",control="control.sorted.bam",regions=gr[1],combine.method="fisher")

Error: class(regions) %in% c("data.frame", "GRanges") is not TRUE

Thank you very much.

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(Continue reading)

Rao,Xiayu | 30 Jul 16:43 2014

multi-level experiments - limma

Hello,

Sorry that I bring up this again, but I do want to know if my logic is correct or not, with regard to subset a big
data set according to different research questions and start from normalization to set up different
design matrixes and contrasts. I am particularly unsure about the 2nd question of comparing normal and
tumor type I (AR+), and how pairing and batch effect should be addressed? Any comments or suggestions
would be very appreciated. Thank you very much in advance.

Thanks,
Xiayu

-----Original Message-----
From: bioconductor-bounces@...
[mailto:bioconductor-bounces@...] On Behalf Of Rao,Xiayu
Sent: Monday, July 28, 2014 4:55 PM
To: bioconductor@...
Subject: [BioC] multi-level design and contrasts problem - limma

Hello,

I read the limma user guide on the topics of multi-level experiments and found the information very useful.
But my design is a little more complicated, and I would like to consult for a solution.
I was asked to solve the following questions regarding the data structure below (targets.txt). I guess I
need to set up different design matrixes according to different questions?

1)      Normal vs tumor:    Do I subset the data into paired samples (subject) only and then used the paired design
since some samples do not have their normal samples? There is only 1 or 2 patients with Tumor and normal
samples in different chips. Can I just do pairing and ignore the batch effect (chip), as I read in the forum
that doing both does no good since most pairs are within the same chip.

(Continue reading)

Ole Rogeberg | 30 Jul 12:01 2014
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Help installing 'rhdf5' in an "offline" environment

I would like to install rhdf5 in a secured, Windows-based server
environment that (for security reasons) cannot access outside servers or
websites. As the environment is "offline" in this sense, I cannot use the
recommended procedure involving biocLite.R and the bioconductor repository
directly for installing the rhdf5 package.

I can upload specific files to the offline environment, but not install
software directly myself (lack write-access to the relevant folders on the
server where software is located). However, the system administrator has
placed a mirror of CRAN in the "offline" environment, and installing new
CRAN packages in R is unproblematic.

For rhdf5 I have tried:

1. Install rhdf5 directly: Uploading the rhdf5_2.8.0.tar.gz file and
installing it from the package manager in RStudio. This gives the message
that "ERROR: dependency 'zlibbioc' is not available for package 'rhdf5'"

2. Installing required packages that rhdf5 relies on: Uploading and
installing zlibbioc in the same manner. This gives me a warning that "This
package has a configure script. It probably needs manual configuration" -
followed by a compilation error (see full R-message here
<http://screencast.com/t/2bTXvwuZ>).

I am unable to figure out what the "status 127" error message refers to, or
if this is even a sensible way to proceed.

Has anyone got a simple solution that I can implement or (if needed) a more
technical solution I can forward to my system administrator?

(Continue reading)

Eduardo Andres Leon | 30 Jul 09:38 2014
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Re: Goseq datasets

Thanks, But I'm not talking about using GSEA. I mean to use other datasets (GOseq comes just with GO and KEGG)
into GOSeq
Sorry if I didn't explain myself

On 30 Jul 2014, at 05:10, Yang,Jie <JYang32@...> wrote:

> Hi,
> 
> You can create the dataset by yourself and do the analysis. But the format of the dataset should be one of the
required. Just create the dataset, load it and choose it in "run gsea".
> 
> ________________________________________
> From: bioconductor-bounces@...
[bioconductor-bounces@...] on behalf of Eduardo Andres Leon [eleon-ibis@...]
> Sent: Tuesday, July 29, 2014 10:29 AM
> To: list list
> Subject: [BioC] Goseq datasets
> 
> Dear R helpers,
> I've been successfully using Goseq to measure GO and KEGG enrichment in diverse RNASeq experiment. I was
wondering if is there any way to use different datasets :
>        * Positional enrichment, for instance cytoband enrichment ....
>        * Custom datasets ( In GSEA I used to create my own dataset to search for enrichment in customised networks
... for example special TF ... and others)
> 
> Many thanks in advance
> 
> ================================================
> 
> Computational Biology and Bioinformatics
(Continue reading)

Gourja Bansal | 30 Jul 08:03 2014
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Query regarding DESeq

Hi Simon,

I have a query regarding count based method to find differential expression.

As in seqanswers you stated that HTSeq is not for isoform level count as
most of the reads are overlapping, so a major portion of data will be
reagrded as ambigous.

But if instead of rejecting overlapping reads if I assign them to all
isoforms in which they are falling i.e. by using featurecounts and then
find differential expression using DESeq. Do you think the results which I
got after it will be significant?

Please reply.

Regards,
Gourja

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