I’m following the protocol from the KRAS vignette ([https://hectorrdb.github.io/condimentsPaper/articles/KRAS.html])
My dataset consists of one shared lineage (Lineage1) across two conditions, with approximately 200k nuclei per condition.
I fitted the model using nknots = 6, and ran:
condRes <- conditionTest(kras, l2fc = log2(2), lineages = TRUE)
condRes$padj <- p.adjust(condRes$pvalue, "fdr")
All parameters are left as default. However, I noticed that my resulting p-values are mostly either 0 or 1. There’s no smooth or approximately normal distribution.
Is this expected behavior, or could it indicate a model fitting or design issue?
I wonder whether this could be due to overfitting with nknots = 6, unbalanced pseudotime coverage between 2 conditions, or some other parameter choice.
Any insights on how to interpret this p-value distribution, or diagnostics I could check would be very helpful.
Thank you!
Best,
Yixian Qin
I’m following the protocol from the KRAS vignette ([https://hectorrdb.github.io/condimentsPaper/articles/KRAS.html])
My dataset consists of one shared lineage (Lineage1) across two conditions, with approximately 200k nuclei per condition.
I fitted the model using nknots = 6, and ran:
condRes <- conditionTest(kras, l2fc = log2(2), lineages = TRUE)
condRes$padj <- p.adjust(condRes$pvalue, "fdr")
All parameters are left as default. However, I noticed that my resulting p-values are mostly either 0 or 1. There’s no smooth or approximately normal distribution.
Is this expected behavior, or could it indicate a model fitting or design issue?
I wonder whether this could be due to overfitting with nknots = 6, unbalanced pseudotime coverage between 2 conditions, or some other parameter choice.
Any insights on how to interpret this p-value distribution, or diagnostics I could check would be very helpful.
Thank you!
Best,
Yixian Qin