User: eldronzhou
eldronzhou • 350
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Posts by eldronzhou
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... It is hard to say because increasing n does not necessarily lead to decrease of moderate t-statistics. ...
written 2.3 years ago by
eldronzhou • 350
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... You have mentioned some important points. The rank does not change much but the effective size has changed. That's because by pooling all of the data you increase the degree of freedoms for fitting linear model and make variance estimation for shrinkage more accurate. ...
written 2.3 years ago by
eldronzhou • 350
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... Here is a paper [here][1] using log2(FPKM+1) as input for Combat with some discussion. I think there is no need to worry about below zero values, and I will recommend keep using log-transformed values. The real problem with Combat may be whether the prior distribution fits RNA-seq data well (check t ...
written 2.3 years ago by
eldronzhou • 350
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... Can you specifify whether you are using counts or FPKM ? You did not mention FPKM after first sentence. ...
written 2.3 years ago by
eldronzhou • 350
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... Usually no need to worry about uneven group sizes. ...
written 2.3 years ago by
eldronzhou • 350
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Comment:
C: Gene function KEGG and GSEA
... Test your set of genes with GO/KEGG/GSEA. If there is an enrichment of your set of genes in specific GO/KEGG terms, then just look at DEGs presented in that category. ...
written 2.3 years ago by
eldronzhou • 350
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Comment:
C: Gene function KEGG and GSEA
... MSigDB has the list of KEGG and GO terms. Personally I prefer GSEA than conventional GO and KEGG fisher test because it is less sensitive to arbitrarily defined cutoff for DGE, but usually two methods would not differ too much. I don't quite understand your second question. What do you mean by class ...
written 2.3 years ago by
eldronzhou • 350
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... Multimapping is one of issues for count-based differential gene expression analysis. Personally I discard all of the multimapping reads by STAR (for human RNA-seq typically should be less than 10%) when counting. Another choice is switching to transcript quantification like [kallisto][1], Salmon, et ...
written 2.3 years ago by
eldronzhou • 350
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... Your plot is not bad. PCA works with linear combinations of gene expression value whose variances are largest (PC1), second largest (PC2), ... across all samples. You will not know the variables names in each component. You need to visualize your PCA plot (say PC1 vs PC2), usually labeled your point ...
written 2.3 years ago by
eldronzhou • 350
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... I suggest using `lmFit` from limma for multiple linear regression and `plotSA` for assumption check. Usually autocorrelation is not a problem and limma has way to deal with heteroskedasticity with `arrayweights`. But I'm cufused when you say **one continuous dependent variable (i.e. gene expression ...
written 2.3 years ago by
eldronzhou • 350
Latest awards to eldronzhou
Teacher
14 months ago,
created an answer with at least 3 up-votes.
For A: RNA-seq normalization using housekeeping genes in EdgeR
Appreciated
2.3 years ago,
created a post with more than 5 votes.
For C: pca analysis for differential gene expression data of microarray samples of simi
Commentator
2.4 years ago,
created a comment with at least 3 up-votes.
For C: TrimGalore! on multiple paired fastq files
Commentator
2.4 years ago,
created a comment with at least 3 up-votes.
For C: TrimGalore! on multiple paired fastq files
Autobiographer
2.4 years ago,
has more than 80 characters in the information field of the user's profile.
Scholar
2.8 years ago,
created an answer that has been accepted.
For A: HT_HG-U133A_EA probe tab annotation file
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