Thanks for pointing that out, yes it's a good idea to run QC. There is documentation about QC assessment in the DESeq vignette: bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#data-quality-assessment-by-sample-clustering-and-visualization For example, you can run PCA with plotPCA(vst(dds), intgroup="genotype") - we use the "genotype" column from the "metadata" data frame in the video. -- Robert
Hi I wanted to ask a question about the normalization of row counts. If I want to compare the 2 groups with z score. Can I do it with deseq2? 2. If not? When calculating the mean, I'm calculating the mean of all samples or mean of the group (mutant And control seppartly)?
For the most part RNAseq is RNAseq despite which organism. What may change could be the technique used. Say sc, bulk, microrna etc but the basic molecule is the same. So I would assume the procedures are the same. While this is biased especially if you have to factor in rates of transcription in your study , then you won’t have to worry much. If needed you just have to apply the statistical adjustments in the pipeline
Hi thanks for your time but what about qc? Isn't the qc step necessary? Like pca or hierarchical clustering?
Thanks for pointing that out, yes it's a good idea to run QC. There is documentation about QC assessment in the DESeq vignette: bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#data-quality-assessment-by-sample-clustering-and-visualization
For example, you can run PCA with plotPCA(vst(dds), intgroup="genotype") - we use the "genotype" column from the "metadata" data frame in the video.
-- Robert
Hi I wanted to ask a question about the normalization of row counts.
If I want to compare the 2 groups with z score.
Can I do it with deseq2?
2. If not? When calculating the mean, I'm calculating the mean of all samples or mean of the group (mutant And control seppartly)?
Robert, you are a great teacher. Can you also do a tutorial on single cell rna seq analysis using seurat?
Thanks
Excellent !!! , for bacteria RNAseq operate by similar way?
For the most part RNAseq is RNAseq despite which organism. What may change could be the technique used. Say sc, bulk, microrna etc but the basic molecule is the same. So I would assume the procedures are the same.
While this is biased especially if you have to factor in rates of transcription in your study , then you won’t have to worry much. If needed you just have to apply the statistical adjustments in the pipeline
Thanks for the tutorial, very well done. However, when running the dds
Thank you! That is an odd error, does countData=DataFrame(counts), colData=DataFrame(metadata) help? -- Robert
Hi I am below average student can i also learn bioinformatics and can make career in it