DESeq2 - Outliers influencing sig DEGs, filter counts per group samples, not by all samples
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Entering edit mode
smac_head • 0
@8eea9978
Last seen 3 months ago
United Kingdom

Hi there,

So, I'm running DESeq2 and have a number of different of different groups (>10) for comparisons. When I reach the prefiltering step, I filter for genes with a mean count above 1. Then, I also try to keep only the genes that have a normalised count greater than or above 10, in at least 10 samples.

countdata <- subset(countdata,apply(countdata, 1, mean) >= 1)
...
dds = estimateSizeFactors(dds)
keep = rowSums(counts(dds, normalized = T) >= 10) >= 10
dds = dds[keep,]

But because the filtering works for a gene in all samples at once, when I extract results for certain comparisons that exclude samples, some genes are retained that will have counts of 0 in one group (n = 12), and majority 0 in another group (n = 10) except for one or more samples that could be e.g. 3. Sort of like the example below:

ID  W1  W1  W1  W1  W1  W1 W1  W1  W1  M2  M2  M2 M2  M2  M2  M2  M2  M2 
Gene1   0   0   0   0   0   0   0   0   0   0   0   0   3   0   0   0   0

Due to these outliers, these genes are determined significantly differentially expressed between comparisons, and you can see it seems to be the result of these outliers.

boxplot showing issue

The only similar issue I could find was this post - I cleaned the code into a working format and switched the values to filter by median rather than mean:

#Get assay data containing only the conditions of interest
assay_of_conditions =data.frame(assay(dds, "counts"))[colData(dds)$condition %in% c("Condition1", "Condition2")]

#Make a logical vector that finds the median of each row, and returns True or False based on if the median is > 3
filter_assay = apply(assay_of_conditions, 1, median, na.rm = T) > 3

#Get the results of the dds by this condition, and filter by the logical vector
res = results(dds, c("condition", "Condition1", "Condition2"))[filter_assay,]

My only question is, does this post-DESeq2 filtering seem appropriate? I worry the significance value is affected by running without pre-filtering removing the problematic outliers in the first place.

Thank you!

filtering DESeq2 outliers • 526 views
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Entering edit mode
@mikelove
Last seen 1 day ago
United States

You may want to filter per comparison, or require a higher number of samples across the entire dataset to be expressed.

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