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I want to extract differential expressed genes using LIMMA from RNA seq data for three cancer types viz breast, lung and prostate. These data should have tumor and normal samples. I have read some papers which have used data from TCGA. BUt now TCGA has linked to Genomics Data Commons and all data are not open access. All BAM files are under controlled access. Also, LIMMA requires raw read counts for analysis. I an new to RNA seq data and analysis. Can anyone help, where should I get these data and what format should it be, as I have read that TPM, FPKM normalized values cannot be input to LIMMA.
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For reference cross-posted: https://www.biostars.org/p/250946/ -
I'm not sure what you'll have to do to get your data, but I can give you some advice on the RNA-seq part.
If you're starting with BAM files that means your reads are already aligned, if you start with reads (.fastq) you can map them yourself using any splice aware mapping tool. Assuming you have bam files though, you'll want to use a tool that extracts raw counts from the bam files. You'll need an annotation file (.gtf or gff format probably) and a counting tool. htseq-counts or featureCounts (in the RSubread package) are both good and widely used tools for extracting raw counts from BAM files, so start there and extract your counts.
Once you have gotten that far, then there are alot of tools like limma, deseq2, or edgeR that can use that data for analysis.Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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