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Since a part of OUTRIDER's vignette was designed with RNA-seq data in mind, it seems that not all functions are meant to be used for proteomics data. For instance, there is the filterExpression step, but it seems that is based on fpkm values and thus should be irrelevant with proteomics data? Similarly, I was unable to use the plotCountGeneSampleHeatmap function. Are there alternatives for those for proteomics data?
Found it in the vignette:
• plotExpectedVsObservedCounts -> plotExpectedVsObserved,
• plotCountCorHeatmap -> plotSampleCorHeatmap,
• plotCountGeneSampleHeatmap -> plotFeatureSampleHeatmap.
Since a part of OUTRIDER's vignette was designed with RNA-seq data in mind, it seems that not all functions are meant to be used for proteomics data. For instance, there is thefilterExpression
step, but it seems that is based on fpkm values and thus should be irrelevant with proteomics data? Similarly, I was unable to use theplotCountGeneSampleHeatmap
function. Are there alternatives for those for proteomics data?Found it in the vignette:
• plotExpectedVsObservedCounts -> plotExpectedVsObserved,
• plotCountCorHeatmap -> plotSampleCorHeatmap,
• plotCountGeneSampleHeatmap -> plotFeatureSampleHeatmap.
This issue (sample swaps: OutriderDataSet(countData=cts, colData=anno) makes undocumented(?) assumption that "cts" columns have same order as "anno" rows, leading to results with incorrect sample labels when this is not met. #32) mentions that the order of the samples in the annotations table should be the same as the order of the rows in the inputData. Is that true?
Edit: moved to: Error in findEncodingDim: deprecated pandas.DataFrame.append pandas>=2.0 #60
Should input protein intensities be raw values or is it ok if they are already log-transformed?
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