User: jianxing.tongji

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Posts by jianxing.tongji

<prev • 6 results • page 1 of 1 • next >
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Answer: A: GFOLD value cutoff
... The GFOLD values are symmetric around 0. For up-regulated genes, you can take GFOLD>x and for down-regulated ones, you can take GFOLD<-x. ...
written 3.9 years ago by jianxing.tongji30
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Answer: A: high fold changes due to the low counts
... A reasonable model like DESeq, GFOLD or edgeR should not be cursed by low read counts. The calculation is not related to fold change. ...
written 4.1 years ago by jianxing.tongji30
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Answer: A: high fold changes due to the low counts
... A reasonable model like DESeq, GFOLD or edgeR should not be cursed by low read counts. The calculation is not related to fold change. ...
written 4.1 years ago by jianxing.tongji30
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Answer: A: GFOLD diff input
... Yes, different normalization gives different result. However, the top ranked genes should be similar. ...
written 4.1 years ago by jianxing.tongji30
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Answer: A: GFOLD value cutoff
... There is no gold standard cutoff. To understand this, just consider gfold as a reliable log2 fold change. The only thing hold is that large absolute gfold value corresponds to large express changes.For enrichment analysis, just take the top genes, 1000, 500, 2000, etc. The biological conclusion shou ...
written 4.1 years ago by jianxing.tongji30
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Answer: A: GFOLD posterior distribution
... Just consider lambda as the 'true' expression level and the read count from RNAseq as an observation. Therefore the value for lambda is ranging from 0 to infinity. For example, if a gene has expression 890 (=absolute molecular counts * a scalar constant), the observed read count is a random variable ...
written 4.1 years ago by jianxing.tongji30 • updated 7 weeks ago by RamRS25k

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