Changes in version 1.1.3 Major Changes - 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'. Minor Changes - 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.