They are two versions of the same. We completely overhauled the vignette this summer, reqrote parts of it, and used different example data. Also, we took out the explanation how to read in the data and moved it to the pasilla vignette. Maybe we should change this back.
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hello every one i am also new to this area. i have a file containing something like this eg:
name celltype1 celltype2
mirna1 23 45
in the below example the name represents name of mirna and the no of reads in each celltype are also mentioned how and wat will be the best way to normalize this kind of data. can i use DEseq?Last edited by anurupa; 03-04-2012, 10:58 PM.Comment
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Sure, you can use DESeq. However, you seem to only have only a single sample per cell type. You won't get far with that.Comment
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hi,
thank you for the reply. i just have shown an example that is not mine actual data. kindly explain what u meant and why? if u are talking about the duplicates we have 2 duplicates for each eg (celltype1a celltype1b) which are duplicatesComment
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Dear all,
I am working on short RNA sequencing already for some time. I can agree with Simon Anders, that RNA-Seq is linear and DEseq or TMM are good solutions. On the other hand our experience is RPM normalization correlates well with microarray data and qPCR. Of course in the range of medium and high expression. I bet none of previously mentioned methods will deal well with low expression of RNA, since there are quantization effects and simply noise. My personal opinion is - if you can do things in many ways you can validate, choose the simplest one.Tomasz Stokowy
www.sequencing.io.gliwice.plComment
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Hi everyone,
I understand that RPM is the simplest and most used normalization method for small RNA data, however I am wondering is there any publication for the same which could be cited along with the publication, like we have for other normalization methods.
- RPKM (Reads per kilobase per million mapped) : Mortazi et al, Nat. Methods, 2008
- Trimmed mean of M-values : Robinson, Oshlack, Genome Biology 2010
- upper-quantile : http://www.ncbi.nlm.nih.gov/pubmed/20167110Comment
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Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.
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