Thanks. I did use fold-change and adjusted p-value for analysis by edgeR. However, some of my results were different from those in cBioPortal database using expression Z-scores. For example,in TCGA lung adenocarcinoma dataset, CAV1 was downregulated in tumors with a logFC -3.2 and p-value E-104, but CAV1 was identified to overexpress in 6% tumors using Z-score absolute value > 2. I was wondering about what leads the opposite results and which method should be used for differential analysis.
Differential gene expression analysis
Hello,every one.I have been analyzing differentially expressed genes between tumors and normals(not paired) with RNAseq data and confused about the methods. Applying a fold-change cut-off is commonly used while a threshold for expression Z scores is also used. I would like to know which method is better for unpaired RNAseq data. Thanks so much!
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It is important to use both statistical significance and effect size while filtering the deferentially expressed genes. Statistical significance level (for example: a p-value or better adjusted p-value) will tell you whether the observed difference is due to the data in hand or it appeared just by chance, while the effect size (for example: fold-change) will tell you the magnitude of difference between the two groups. You can have a look at this published article (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/) which talks about "Using Effect Size—or Why the P Value Is Not Enough".
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This paragraph from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156192/ might help you to understand your findings. "A positive correlation exists between the upregulation of Cav-1 and the clinical features of primary lung cancer. Although Cav-1 levels in lung tumor tissues are significantly lower than in tumor-free lung tissues (91,92,100–102), the expression of Cav-1 in lung tumor tissues is markedly higher in patients with lymph node metastasis (92,93,100) and advanced tumor stage (93,100,103)." You have considered only 3 samples for your analysis. I am not sure about the tumor stages of these samples.
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Hi!
Which software did you use so far? (for mapping, counting and then calling differentially expressed genes)?
I downloaded TCGA level 3 RNAseqV2 RSEM data for lung cancer,removed genes with low expression and analyzed by edgeR. Then I searched cBioPortal for further validation and found that expression Z-scores were used for expression analysis in the database, which gave the different result.
Usually the cut off is +/- 2 log2(rpkm). I'm not familiar with using Z scores.