Bimodality Assessment of Null Distributions

To better assess the bimodality of the null distributions, for each link we drew 1000 GC and log(ATACseq peak sum of counts + 1) matched trans-peaks using Signac’s function MatchRegionStats() (instead of the default 200), computed their Pearson R and scaled them. To establish if the resulting null distributions were multimodal, we used the mixtools package expectation–maximization function normalmixEM() with k = 2 and epsilon = 1e−03 as described in Ameijeiras-Alonso et al., 2019^{22 (link)}. Null distributions with nominal p-values < 0.05 were categorised as multimodal.

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Leblanc F.J, & Lettre G. (2023). Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes. Scientific Reports, 13, 3924.

Publication 2023

Multimodal

Corresponding Organization :

Other organizations : Université de Montréal

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Variable analysis

independent variables

Number of GC and log(ATACseq peak sum of counts + 1) matched trans-peaks drawn (1000 instead of the default 200)

dependent variables

Pearson R between the GC and log(ATACseq peak sum of counts + 1) matched trans-peaks

control variables

Signac's function MatchRegionStats() used to draw the GC and log(ATACseq peak sum of counts + 1) matched trans-peaks
Normalmixem() function from the mixtools package used with k = 2 and epsilon = 1e-03 to establish if the resulting null distributions were multimodal
Null distributions with nominal p-values < 0.05 categorised as multimodal

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