User: kentfung

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kentfung0
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Posts by kentfung

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Comment: C: Finding genes in a gene set detected by ROAST (or FRY) in limma
... Hi Gordon, Thank you for suggesting the use of camera. I am quite new to the field and saw a review saying that self-contained gene set analysis is more specific and sensitive, so I just followed without thinking much. I will have a look at the paper and vignette again. Thanks a lot for your help. ...
written 11 days ago by kentfung0
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Finding genes in a gene set detected by ROAST (or FRY) in limma
... I used fry() in limma package and it gave me this as results NGenes Direction PValue FDR PValue.Mixed FDR.Mixed KEGG_APOPTOSIS 81 Down 2.853559e-24 5.307619 ...
limma roast fry written 11 days ago by kentfung0 • updated 11 days ago by Gordon Smyth38k
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Including sva results in SeqGSEA with data with batch effect
... I have been using voom+limma+sva to analyse my RNA-Seq data of leukaemia. After some clustering analysis and prior knowledge to the data set, I now am trying to compare some of the samples in a group with the remaining data set. I used fgseaMultilevel() from fgsea package and did a pre-ranked GSEA u ...
limma deseq sva deseq2 seqgsea written 13 days ago by kentfung0
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Comment: C: Batch effect correction for class detection
... svaseq requires a priori knowledge of the biological factor in all samples, which is not suitable for my case. And even if it doesn't, I have tried pSVA, which would eventually lead to the same results. I realise there is a lack of tools for batch effect correction on samples with unknown biologica ...
written 11 months ago by kentfung0
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Comment: C: Looking for Batch effects in PCA
... This may be a bit late, but if you still tackling the problem you could try guided PCA (a link to the vignette: https://cran.r-project.org/web/packages/gPCA/vignettes/gPCA.pdf) for identifying batch effect. However, you must know which batch each sample is from to make it work.   ...
written 11 months ago by kentfung0
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Batch effect correction for class detection
... Hi, I have two set of RNA-Seq data of leukaemia, one from our lab and one from a published data set. The aim of the analysis is to further subgrouping the cases by certain traits (e.g. translocation). The batch effect is very heavy and my current approach is: filtering by TPM > voom+quantile nor ...
batch effect unsupervised written 11 months ago by kentfung0 • updated 11 months ago by James W. MacDonald50k

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