On the identification of differentially-active transcription factors from ATAC-seq data
Fig 2
Further benchmark results, and improvements over native chromVAR.
A. Sensitivity and specificity (considering interactors and members of the same archetypes as positives) of the top alternative per approach. The methods are coloured by family (sample-wise methods in pink, logFC-based methods in blue). B. Mean (and standard error of the mean) running time (elapsed, as well as total CPU time when multithreading) across datasets of the top alternative per approach (the x axis is squared-root-transformed for visibility). Note that because it was done separately for reasons of standardization, the running time does not include the generation of the peak-count matrix, nor, except for monaLisa, the motif scanning. monaLisa is therefore disadvantaged in this comparison, and these times should be interpreted as rough estimates. C. Comparison of the rank of the true motifs obtained by a limma analysis on the chromVAR deviations, versus using chromVAR’s native differentialDeviations. D. Comparison of the rank of the true motifs obtained by a limma analysis on the normalized chromVAR z-scores, versus on the chromVAR deviations.