#COLORS 1.1.0 CODA 2 HOW TO#
#COLORS 1.1.0 CODA 2 MANUAL#

Loaded via a namespace (and not attached): parallel stats4 stats graphics grDevices utils datasets methods base LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

dds 10) >= 7 # keep genes with at least 7 samples with a count of 10 or higher Then do pre-filtering to keep only genes with >10 in at least 7 samples. Two binary variables are used to test for the effect of A and B: treatA, which value is 1 if the sample was treated with A or A+B, and 0 otherwise and treatB, which value is 1 if the sample was treated with B or A+B, and 0 otherwise.

#COLORS 1.1.0 CODA 2 MANUAL#

I did a manual inspection of the sample, looked at the Cook Distances and the original and replaced counts (plots below) and it looks like that sample has very high counts for most of the 437 genes outliers detected by DESeq2 (I include 4 examples but I had checked all the values).ДЇirst, I'm testing for the effect of each treatment and these are the steps I'm following:ДЎ) Create DESeq object. When looking at glmPCA and a heatmap using Poisson distances the outlier is very different from any other sample.

If leaving it, how can I make sure the outlier is not skewing my results?Дў) Am I missing other checks that could explain why this sample is so different and perhaps help justify removing it from the analysis? Or would the visual checks shown below be enough to justify discarding the sample from the analysis? If removing it, the dilemma is that I can't yet justify removing the sample: I have looked at the QC for the raw reads of that sample, as well as the stats from the alignment and can't seem to find any evident issue with it.

#COLORS 1.1.0 CODA 2 HOW TO#

My question is on how to handle this outlier:ДЎ) I could either remove it from the analysis (leaving me with 6 biological replicates left which should be enough) or I could leave it and use the replacement from DESeq2. I have 7 biological replicates in each group.ДЄfter performing exploratory and differential expression analyses, one sample is detected as a possible outlier. I have an RNAseq dataset from human cells for an experiment with a 2-factorial design for four groups: control, treatment A, treatment B, and treatmentA+treatmentB. I was wondering if I could have your advice regarding investigating possible outliers.

Colors 1.1.0 coda 2