You could do str(readCounts) to see the structure of the object. You could post the output of that which may help. Once you identify which part of readCounts contains each sample you should be able to isobarPlot(readCounts$<sample>)
[ensemblVEP][1] had a great function called [parseCSQToGRanges][2] that you could use to get the consequence information in a nice data.frame, e.g. vcf <- readVcf(tempFile, genome="grc38") …
Dear all, referring to the batch correction methods for scRNA-seq, would you have any preference and/or comments ? among possible choices : -- MNNCorrect, as …
# NB - please post questions on Bioconductor Support: https://support.bioconductor.org/ [ direct link to create a post: https://support.bioconductor.org/p/new/post/?tag_val=PCAtools ] ------------- # PCAtools [![ex9-1][1]][2] [![ex11b-1][3]][4] [![ex5-1][5]][6] …
# NB - please post questions on Bioconductor Support: https://support.bioconductor.org/ [ direct link to create a post: https://support.bioconductor.org/p/new/post/?tag_val=EnhancedVolcano ] ---------- # EnhancedVolcano [![Ecv9vi-XX0-AE4q3-A][1]][2] [![download-1][3]][4] [![ex7-1][5]][6] …
I'm using trying to use [this tutorial][1] to map certain protein positions to genomic positions. The whole tutorial is focused on using the `Proteins` class …
You could do
str(readCounts)to see the structure of the object. You could post the output of that which may help. Once you identify which part ofreadCountscontains each sample you should be able toisobarPlot(readCounts$<sample>)Thanks, but the structure is not so simple and I'm not sure which are the parts that are required by the function to create the plot
P.S. I ended up simply creating a separate object for each sample :)