User: Jakub

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Jakub30
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United Kingdom
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1 year ago
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2 years ago
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j***********@ndcn.ox.ac.uk

Posts by Jakub

<prev • 15 results • page 1 of 2 • next >
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Comment: C: is it necessary to check batch effect in this case? and how to?
... Glad to help. I assume you used default trimmomatic settings, which are reasonable. The result looks fine. You will have to decide how much QC you do on 'lanes', and whether you want to know per lane GC content etc... Trimming is only one part of QC. There are many ways to explore biases from seque ...
written 13 months ago by Jakub30
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Answer: A: Change the FDR in DEXSeq
... You can subset the results class with any cuttoff you like, as the results object has a "padj" column, which contains the the adjusted p value (also known as the FDR adjusted p-value), by some multiple testing adjustment, here using the Benjamini-Hochberg method. At the end of chapter 4 of the vign ...
written 13 months ago by Jakub30
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Answer: A: is it necessary to check batch effect in this case? and how to?
... If I understand correctly, you have run the same 88 samples in each of the flow cell lanes? In that case that's an optimal experimental design and goes a long way in preventing the sort of batch effects you might be worried about. I would argue that an optimal block design is the main answer to your ...
written 13 months ago by Jakub30
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Comment: A: DESeq2 normFactors method: Error in fitBetaWrapper
... It does indeed, sorry for not being explicit about this! Thanks again. ...
written 19 months ago by Jakub30
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Comment: C: Accounting for 5'/3' Bias in DESeq 2
... Thanks! I've done exactly this and computed TINs for each gene, and performed the loess regression. I now have the raw logcounts and corrected logcounts. I guess I am not clear in my head which value is best to use in a normFactor offset matrix, before normalising each row to a geometric mean of 1 ...
written 19 months ago by Jakub30
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Comment: C: DESeq2 normFactors method: Error in fitBetaWrapper
... Many thanks! Indeed, not all values were finite (lots of dividing by zero). I have added the line: normFactors[!is.finite(normFactors)] <- 1  A typical column now looks like this: Min. :0.1656 1st Qu.:0.9380 Median :1.0000 Mean :1.0208 3rd Qu.:1.1189 Max. :4.2724 ...
written 19 months ago by Jakub30
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DESeq2 normFactors method: Error in fitBetaWrapper
... Dear all, this is a follow up from my last question. When my run my DESeq2 analysis using the usual estimateSizeFactors, it runs fine, however if I provide a normalisation matrix, I get the following error on estimateDispersions: Error in fitBetaWrapper(ySEXP = counts(object), xSEXP = modelMatrix ...
normalization deseq2 written 19 months ago by Jakub30
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Answer: A: Issue with SampleSheet.csv during illumina methylatiom microarrays analysis by R
... Hi, I think there are indeed two issues. I would rename the columns to the standard column names (see below). I have added the Tissue column, but you can add any number of columns to the right the Sentrix_Position column. Sample_Name,Sample_Well,Sample_Plate,Sample_Group,Pool_ID,Sentrix_ID,Sentri ...
written 19 months ago by Jakub30
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Accounting for 5'/3' Bias in DESeq 2
... I didn't find an answer to this searching the forums. I have RNASeq samples with 5'/3' biases that are unevenly distributed amongst the samples. Some of my conditions have more samples with the bias, some less - the reason for these biases is almost certainly different levels of RNA sample fragmenta ...
deseq2 written 20 months ago by Jakub30 • updated 20 months ago by Ryan C. Thompson6.1k
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Comment: C: Question: GoSeq: opposite pwf plot for up- and downregulated genes
... I have thought about this very hard, and have run goseq in a variety of ways. Similar to what goseq package suggests: genes=as.integer(p.adjust(res$padj[(res$log2FoldChange!=0) & (!is.na(res$log2FoldChange))], method="BH")<.05) names(genes)= row.names(res[(res$log2FoldChange!=0) & ( ...
written 2.1 years ago by Jakub30

Latest awards to Jakub

Scholar 19 months ago, created an answer that has been accepted. For A: Issue with SampleSheet.csv during illumina methylatiom microarrays analysis by R

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