getSignificance very memory intensive when fit = ANOVA

[ncborcherding]

getSignificance very memory intensive when fit = ANOVA

[Dragonmasterx87]

Update: I have subsetted each of the individual cell types, and am now running getSignificance for each file separately. So far it seems to be less computationally intensive. The trick will be to concatenate the data when I have it. Will update you later.

Update: Using some meta-data kung-fu, I was able to segregate the cell-types into individual Seurat objects and successfully run getSignificance on them, using simpler computational complexity I was able to reduce ram usage from >1.2TB to <20GB.

With regards to the interpretation, for the sake of argument in a cell-type X for the GO term 'increased carbohydrate synthesis' say I am comparing males to females and the p.val is <0.001 (the column name is malesvsfemales.pval), also say the median for males is 4000 and median for females is 2000, would that suggest that this specific GO is up-regulated in males, owing to a higher median in males and furthermore, in the males_vs_females direction and it is significant?

Cheers,

🐉

[ncborcherding]

Hey Fahd,

Apologies for the delay -

Thank for reaching out and giving an extensive summary (with follow up) of the problem. You are completely correct - there is a large computational requirement for a lot of the additional testing as getSignificance() is dependent on r::stats. I think this is a larger issue as data sets expand and will mark this as help wanted because the issue is persistent.

Please let me know if you have any suggestions from your experience and I will test some ideas I have in the mean time.

Nick

[Dragonmasterx87]

Cheers thanks legend!!