Summarization by gene or exon or transcript
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Reema Singh ▴ 570
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Dear All, I would like to known in order to find out the differentially expressed genes between two condition - what is the idea feature for summarization? . I have choosen gene by summarization. Is this a good choice?. Any suggestions would be very helpful. Kind Regards -- Reema Singh PhD Scholar Computational Biology and Bioinformatics School of Computational and Integrative Sciences Jawaharlal Nehru University New Delhi-110067 INDIA [[alternative HTML version deleted]]
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Reema Singh ▴ 570
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Hi Reema, On Thu, Oct 31, 2013 at 10:04 AM, Reema Singh <reema28sep at="" gmail.com=""> wrote: > Dear All, > > I would like to known in order to find out the differentially expressed > genes between two condition - what is the idea feature for summarization? . > I have choosen gene by summarization. Is this a good choice?. > > Any suggestions would be very helpful. You're going to have to ask a more specific question, as I'm having a hard time understanding what you are after. If you are just curious how you could perform a differential expression analysis, there are workflows you can read through here: http://bioconductor.org/help/workflows/ Different packages that perform these analyses are also very well documented, including: * The limma user's guide: this can be applied to both microarray and rna-seq analysis * edgeR user's guide: differential rna-seq expression * DESeq2 vignette: differential rna-seq expression * DEXSeq vignette: differential exon usage using rna-seq data * oligo: different types of array analyses (including expression, tiling, and exon arrays) * xps: similar types of analyses to oligo (I think). Enjoy, -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech
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Hi, On Thu, Oct 31, 2013 at 1:04 PM, Reema Singh <reema28sep at="" gmail.com=""> wrote: > Hi Steve, > > Thank you for your reply, > > I just want to known what is the idea feature for summarizing read count > after alignment?. Gene,transcript,exons features from GFF/GTF files are > frequently used . If you are asking what the "ideal" format for storing summarized read counts is, I would have to say that in "the R world" that would be to use a SummarizedExperiment (it is a class defined in the GenomicRanges package). The rowData() of the SummarizedExperiment would contain the GRanges (or GRangesList) that define where the counts in each row of your assay are from, and the columns would tell you the counts for a given experiment. You could store your relevant sample data in `colData`, ie. phenotypic data for each experiment (column), like cell type, perturbation, whatever. See ?SummarizedExperiment for more info. If you were asking something else -- sorry, I'm still not getting what the question is and perhaps someone else can chime in. -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech
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Hi, On Thu, Oct 31, 2013 at 1:04 PM, Reema Singh <reema28sep at="" gmail.com=""> wrote: > Hi Steve, > > Thank you for your reply, > > I just want to known what is the idea feature for summarizing read count > after alignment?. Gene,transcript,exons features from GFF/GTF files are > frequently used . If you are asking what the "ideal" format for storing summarized read counts is, I would have to say that in "the R world" that would be to use a SummarizedExperiment (it is a class defined in the GenomicRanges package). The rowData() of the SummarizedExperiment would contain the GRanges (or GRangesList) that define where the counts in each row of your assay are from, and the columns would tell you the counts for a given experiment. You could store your relevant sample data in `colData`, ie. phenotypic data for each experiment (column), like cell type, perturbation, whatever. See ?SummarizedExperiment for more info. If you were asking something else -- sorry, I'm still not getting what the question is and perhaps someone else can chime in. -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech
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Hi Steve, Thank you for your reply, I just want to known what is the idea feature for summarizing read count after alignment?. Gene,transcript,exons features from GFF/GTF files are frequently used . Thanks On Thu, Oct 31, 2013 at 11:43 PM, Steve Lianoglou <lianoglou.steve@gene.com>wrote: > Hi Reema, > > > On Thu, Oct 31, 2013 at 10:04 AM, Reema Singh <reema28sep@gmail.com> > wrote: > > Dear All, > > > > I would like to known in order to find out the differentially expressed > > genes between two condition - what is the idea feature for > summarization? . > > I have choosen gene by summarization. Is this a good choice?. > > > > Any suggestions would be very helpful. > > You're going to have to ask a more specific question, as I'm having a > hard time understanding what you are after. > > If you are just curious how you could perform a differential > expression analysis, there are workflows you can read through here: > > http://bioconductor.org/help/workflows/ > > Different packages that perform these analyses are also very well > documented, including: > > * The limma user's guide: this can be applied to both microarray and > rna-seq analysis > * edgeR user's guide: differential rna-seq expression > * DESeq2 vignette: differential rna-seq expression > * DEXSeq vignette: differential exon usage using rna-seq data > * oligo: different types of array analyses (including expression, > tiling, and exon arrays) > * xps: similar types of analyses to oligo (I think). > > Enjoy, > -steve > > -- > Steve Lianoglou > Computational Biologist > Bioinformatics and Computational Biology > Genentech > -- Reema Singh PhD Scholar Computational Biology and Bioinformatics School of Computational and Integrative Sciences Jawaharlal Nehru University New Delhi-110067 INDIA [[alternative HTML version deleted]]
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Hi Reema, On Thu, Oct 31, 2013 at 10:04 AM, Reema Singh <reema28sep at="" gmail.com=""> wrote: > Dear All, > > I would like to known in order to find out the differentially expressed > genes between two condition - what is the idea feature for summarization? . > I have choosen gene by summarization. Is this a good choice?. > > Any suggestions would be very helpful. You're going to have to ask a more specific question, as I'm having a hard time understanding what you are after. If you are just curious how you could perform a differential expression analysis, there are workflows you can read through here: http://bioconductor.org/help/workflows/ Different packages that perform these analyses are also very well documented, including: * The limma user's guide: this can be applied to both microarray and rna-seq analysis * edgeR user's guide: differential rna-seq expression * DESeq2 vignette: differential rna-seq expression * DEXSeq vignette: differential exon usage using rna-seq data * oligo: different types of array analyses (including expression, tiling, and exon arrays) * xps: similar types of analyses to oligo (I think). Enjoy, -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech
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