Support "multi-feature" analysis, e.g. parallel analysis of multiple features (bins, peaks or gene) on the same object.
New "Coverage" tab & functions generate_coverage_tracks() and plot_coverage_BigWig() to generate cluster coverage tracks and interactively visualise loci/genes of interest in the application.
New inter- and intra-correlation violin plots to vizualise cell correlation distribution between and within clusters.
New normalization method : TF-IDF combined with systematic removal of PC1 strongly correlated with library size.
Simple 'Copy Number Alteration' approximation & visualization using 'calculate_CNA' function for genetically re-arranged samples, provided one or more control samples.
New generate_analysis() & generate_report() functions to run a full-on ChromSCape analysis and/or generate an HTML interactive report of an existing analysis.
Supports 'custom' differential analysis to find differential loci between a subset of samples and/or clusters.
New pathway overlay on UMAP to visualize cumulative pathways signal directly on cells.
Now supports 'Fragment Files' input (e.g. from 10X cell ranger scATAC pipeline), using a wrapper around 'Signac' package FeatureMatrix() function.
New 'Contribution to PCA' plots showing most contributing features and chromosome to PCA.
Restructuration of the ChromSCape directory & faster reading/saving of S4 objects using package 'qs'.
RAM optimisation & faster pearson cell-to-cell correlations with 'coop' package, and use of 'Rcpp' for as_dist() RAM-efficient distance calculation.
Faster correlation filtering using multi-parallel processing.
plot_reduced_dim now supports gene input to color cells by gene signal.
All plots can now be saved in High Quality PDF files.
Changed 'geneTSS' to 'genebody' with promoter extension to better reflect the fact that mark spread in genebodies.
Possibility to rename samples in the application.
Downsampling of UMAPs & Heatmaps for fluider navigation.
Changed 'total cell percent based' feature selection to manual selection of top-covered features, as the previous was srongly dependent on the experiment size.
Faster sparse SVD calculation.
Faster differential analysis using pairWise Wilcoxon rank test from 'scran' package.