Changes in version 2.0.0 * New loadFry() function, written by Dongze He with contributions from Steve Lianoglou and Wes Wilson. loadFry() helps users to import and process alevin-fry quantification results. Can process spliced, unspliced and ambiguous counts separately and flexibly. Has specific output formats designed for downstream use with scVelo or velocity analysis. See ?loadFry for more details. * Adding correlation tests: Spearman or Pearson correlations of a numeric covariate with the log counts, or with the log fold changes across pairs. The Spearman correlation test with counts was already implemented in the original SAMseq method as response type = "Quantitative". For new functionality see 'cor' argument in the ?swish man page. * Adding importAllelicCounts() to facilitate importing Salmon quantification data against a diploid transcriptome. Can import either as a 'wide' format or as 'assays'. Leverages tximeta(). For gene-level summarization, importAllelicCounts() can create an appropriate tx2gene table with the necessary a1 and a2 suffices, and it will automatically set txOut=FALSE, see ?importAllelicCounts for more details. * Added a 'q' argument to plotInfReps to change the intervals when making point and line plots. * Switched the legend of plotInfReps so that reference levels will now be on the bottom, and non-reference (e.g. treatment) on top. Changes in version 1.99.18 * Added helper functionality to importAllelicCounts, so it will create an appropriate tx2gene table with the necessary a1 and a2 suffices, and it will automatically set txOut=FALSE. * Added a 'q' argument to plotInfReps to change the intervals when making point and line plots. * Switched the legend of plotInfReps so that reference levels will now be on the bottom, and non-reference (e.g. treatment) on top. * Added loadFry() to process alevin-fry quantification result. Can process spliced, unspliced and ambiguous counts separately and flexibly. Changes in version 1.99.15 * Adding correlation tests: Spearman or Pearson correlations of a numeric covariate with the log counts, or with the log fold changes across pairs. The Spearman correlation test with counts was already implemented in the original SAMseq method as response type = "Quantitative". For new functionality see 'cor' argument in the ?swish man page. * Adding importAllelicCounts() to facilitate importing Salmon quantification data against a diploid transcriptome. Can import either as a 'wide' format or as 'assays'. Leverages tximeta(). Changes in version 1.9.6 * Specifying ties.method in matrixStats::rowRanks. Changes in version 1.9.1 * Added importAllelicCounts() with options for importing Salmon quantification on diploid transcriptomes. Changes in version 1.8.0 * Added note in vignette about how to deal with estimated batch factors, e.g. from RUVSeq or SVA. Two strategies are outlined: either discretizing the estimate batch factors and performing stratified analysis, or regressing out the batch-associated variation using limma's removeBatchEffect. Demonstation code is included. Changes in version 1.6.0 * Added makeInfReps() to create pseudo-inferential replicates via negative binomial simulation from mean and variance matrices. Note: the mean and the variance provide the _inferential_ distribution per element of the count matrix. See preprint for details, doi: 10.1101/2020.07.06.189639. * Added splitSwish() and addStatsFromCSV(), which can be used to distribute running of Swish across a number of jobs managed by `Snakemake`. See vignette for description of a suggested workflow. For a large single-cell dataset with mean and variance summaries of inferential uncertainty, splitSwish() avoids generating the inferential replicate counts until the data has been split into smaller pieces and sent to different jobs, then only the necessary summary statistics are gathered and q-values computed by addStatsFromCSV(). * plotInfReps() gains many new features to facilitate plotting of inferential count distributions for single cells, as quantified with alevin and imported with tximport. E.g. allow for numeric `x` argument plus grouping with `cov` for showing counts over pseudotime across groups of cells. Also added `applySF` argument which can be used to divide out a size factor, and the `reorder` argument which will re-order the samples/cells within groups by the count. plotInfReps() will draw boxplots with progressively thinner visual features as the number of cells grows to make the plots still legible. Changes in version 1.5.2 * First version of makeInfReps(), to create pseudo-infReps via negative binomial simulation from set of mean and variance matrices in the assays of the SummarizedExperiment. Changes in version 1.4.0 * Added isoformProportions(), which can be run after scaleInfReps() and optionally after filtering out transcripts using labelKeep(). Running swish() after isoformProportions() will produce differential transcript usage (DTU) results, instead of differential transcript expression (DTE) results. Example in vignette. * Default number of permutations increased from 30 to 100. It was observed that there was too much fluctuation in the DE called set for nperms=30 across different seeds, and setting to 100 helped to stabilize results across seeds, without increasing running time too much. For further reduced dependence on the seed, even higher values of nperms (e.g. 200, 300) can be used. Changes in version 1.3.8 * Added isoformProportions(), which can be run after scaleInfReps() and optionally after filtering out transcripts using labelKeep(). Running swish() after isoformProportions() will produce differential transcript usage (DTU) results, instead of differential transcript expression (DTE) results. Example in vignette. Changes in version 1.3.4 * Default number of permutations increased from 30 to 100. It was observed that there was too much fluctuation in the DE called set for nperms=30 across different seeds, and setting to 100 helped to stabilize results across seeds, without increasing running time too much. For further reduced dependence on the seed, even higher values of nperms (e.g. 200, 300) can be used. Changes in version 1.2.0 * Switching to a faster version of Swish which only computes the ranks of the data once, and then re-uses this for the permutation distribution. This bypasses the addition of uniform noise per permutation and is 10x faster. Two designs which still require re-computation of ranks per permutation are the paired analysis and the general interaction analysis. Two-group, stratified two-group, and the paired interaction analysis now default to the new fast method, but the original, slower method can be used by setting fast=0 in the call to swish(). * Adding Rcpp-based function readEDS() written by Avi Srivastava which imports the sparse counts stored in Alevin's Efficient Data Storage (EDS) format. * Changed the vignette so that it (will) use a linkedTxome, as sometime the build would break if the Bioc build machine couldn't access ftp.ebi.ac.uk. * Add 'computeInfRV' function. InfRV is not used in the Swish methods, only for visualization purposes in the Swish paper. * removed 'samr' from Imports, as it required source installation, moved to Suggests, for optional qvalue calculation Changes in version 0.99.30 * added two interaction tests, described in ?swish * incorporate qvalue package for pvalue, locfdr and qvalue * added plotMASwish() to facilitate plotting * wilcoxP is removed, and the mean is used instead Changes in version 0.99.0 * fishpond getting ready for submission to Bioc