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I don't expect the soft or matrix files will be useful for you, although it might be possible to pull metadata out of those files if absolutely necessary. I usually only see those for microarray data, so it might be worth double-checking that you do actually have RNASeq data. I've found the best "quick start" explanation on how to carry out ...

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A metadata file isn't that large or complicated. If you want to do DE, you must have two treatments? Or two timepoints? Two or more something, and you are comparing samples of one type to another? It's just a data table where the rownames are the samples, and then a column for, say, treatment. If you have some samples treated, some not, some at day 0, ...

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You can try a hierarchical clustering tool HGC (hierarchical graph clustering). You may get cell population at different hierarchy by choosing multiple cutting heights. See the paper for more details.

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The DESeq2 function collapseReplicates sums the counts for the technical replicates. Here is the code reference: github.com/mikelove/DESeq2/blob/master/R/helper.R#L186 OPs actual confusion was with the DESeq2 function plotCounts which by default normalizes count data and adds a pseudocount of 0.5 for plotting on log2 scale.

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sample is a character vector, so levels(dds$sample) is returning null. Presumably you mean to use dds$sample[1] here instead, which will likely solve the problem.

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I understand that this post is rather old but there has been some development in the field and this 2019 paper might be a good resource for interested individuals.

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The problem is probably that the tar process creates a folder with the genome files but the input for -x is not the folder with the index files but the basename of the index files itself.

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