Nanostring data differential expression analysis: limma or DESeq2?
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pg45863 ▴ 10
Last seen 9 months ago

Hello everyone. I have obtained nCounter data on tumor samples of 4 distinct subgroups of one tumor type. I want to perform differential expression analysis between these 4 subgroups and get a heatmap that can differentiate between them.

My search tells me that because nCounter data are read counts (like in RNA-seq) I could use the DESeq2 package in R to do it. However, some of them also refer different types of normalization methods for the data. I am very new to bioinformatics analysis and have a hard time understanding these nuances. I have both the raw data and also the normalized data that was normalized in nSolver using their pre-defined method.

What shoul I be doing to perform the differential expression analysis? Use the raw counts data directly into DESeq2? Use the normalized data? Use a different package than DESeq2? Is it possible to perform this analysis between 4 different groups?

Hope you can help me and thank you in advance!

DESeq2 NanoStringDiff limma • 934 views
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ATpoint ★ 4.2k
Last seen 23 hours ago

Please see the vignette which instructs to us raw counts. Nothing else will do for DESeq2. limma can either use raw counts with limma-voom or normalized data (typically logCPM-like) via limma-trend for count data. Again, please see its manual. There is also many previous questions on DESeq2/limma on Nanostring, please find them via a google search.

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Ok, thank you!


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