I have read that the data for DESeq2 and EDGE should follow a Negative Binomial distribution while the data for Baggerley´s should follow a Beta-Binomial. From DESeq2 there are around 1500 differential expressed genes, while from EDGE there are around 2000 and finally from Baggerley I got around 3000. What I got seems not very consistent to me. Then I tried to find coherences among them, so I filtered the adjusted p-values (with the same threshold) from each test and compare the filtered genes lists to see how similar they are. The first axis (94 of the variance) separated the basidial stage from the aecial and the pycnial stages, whereas the second axis (3 of the variance) separated the aecial and the pycnial stage. CLC Genomics Workbench is software for analyzing and visualizing next generation sequencing data. counts and DESeq processed relative gene expression level between the wt and the kctd10. Principal component analysis of DESeq2 rlog-transformed normalized counts showed homogeneous biological replicates (Supplementary Fig. So now I have 3 different outcomes from 3 statistical approaches, the one from DESeq2, EDGE and Baggerley´s test from CLC Genomics. s test (Z-test) using CLC Genomics Workbench v4.8Genomebuild. DESeq2 (ref.155) and limma+voom156, and a list of differentially. The data was analized by two pipelines in parallel: Tophat/Bowtie->HTSeq count->DESeq2 and in the CLC Genomics Workbench. Enrichment Analysis Deferentially Expressed Genes CLC Genomics Work Bench Ingenuity. I´m a beginner in the RNA-Seq world who recently got some results to analyse and process.
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