Comments (2)
My understanding is that minimal effect = 0.2 by default. In general, a celltype whose CI is far away from 0 is considered as significant. That means, if a cell type has c_effect < 0, it will be signficant (c_FDR<0.05) if its c_upper < -0.2 ; if a cell type has c_effect > 0, it will be signficant (c_FDR<0.05) if its c_lower > 0.2.
Although this is roughly what happens intuitively, is not 1 to 1 link.
- probability: The probability of an effect occurring is calculated as the quantile of the posterior distribution that is left or right (exclusively) of the minimal effect threshold (e.g 0.2). Here, if the CI is outside the threshold range, it is significant.
- false-discovery rate: as explained in the paper, it is the cumulative mean of the probabilities (so FDR always < than the probability for each single cell type). So, the CI and FDR do not match perfectly. FDR is a cumulative statistic, and it is not about a single cell type.
Let me know if that was clear; with your help, we could add an explanation in the README to help users understand.
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