Changelog • methylTFR

methylTFR 0.99.5

NEW FEATURES

Added run_methylTFR_RnBeads(), which runs the methylTFR workflow directly on a preprocessed RnBeads object instead of per-sample BED files. Samples are read one column at a time, so disk-backed RnBeads sets are never loaded into memory in full.
Added computeZScoreVariability(), which ranks TF motifs by how much their activity varies across samples and tests each motif against a chi-squared null. Deviation scores are first calibrated against a within-sample null estimated across motifs, so that a variability above 1 is interpretable as “more variable than background”.
methylTFRdeviations objects returned by run_methyltfr() and run_methylTFR_RnBeads() now carry a third assay, expected, holding the GC-derived expected deviations. These were previously computed and then discarded.

BUG FIXES

run_methyltfr() no longer rejects .csv sample annotation files. The .tsv branch’s else clause caught every .csv file and raised an error after the file had already been read.
differential_deviation_test() now falls back to colnames(deviations) when groups is NULL, instead of colnames(groups), which was always NULL. Group labels are also validated against the number of columns.
The invalid-input fallback for cov_threshold in run_methyltfr() now sets the documented default of 1 rather than 5.

DOCUMENTATION

The heatmap in the memory T cell vignette is now drawn with ComplexHeatmap instead of ggplot2, with columns split by cell type and rows clustered.
The compartment-agreement scatter plot in the same vignette now colours each motif by where it is differential: red for both compartments, green for CD4 only, blue for CD8 only and grey for neither.

INTERNAL

The per-sample deviation loop shared by both entry points was factored out into methyltfr_core(), so the file-based and RnBeads-based workflows are guaranteed to produce identical results for the same methylation calls.

NEW FEATURES