NEWS

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BPCells 1.0

• ~~Parallelization~~ (basic support complete. See below)
• Native python library (re-using C++ backend)
• Peak-gene correlations
• MACS peak calling

Contributions welcome :)

BPCells 0.2.1

Features

• apply_by_col() and apply_by_row() allow providing custom R functions to compute per row/col summaries. In initial tests calculating row/col means using R functions is ~2x slower than the C++-based implementation but memory usage remains low.
• Add rowMaxs() and colMaxs() functions, which return the maximum value in each row or column of a matrix. If matrixStats or MatrixGenerics packages are installed, BPCells::rowMaxs() will fall back to their implementations for non-BPCells objects. Thanks to @immanuelazn for their first contribution as a new lab hire!
• Add regress_out() to allow removing unwanted sources of variation via least squares linear regression models. Thanks to @ycli1995 for pull request #110
• Add trackplot_genome_annotation() for plotting peaks, with options for directional arrows, colors, labels, and peak widths. (pull request #113)

Improvements

• trackplot_loop() now accepts discrete color scales

Bug-fixes

• Fixed error message when a matrix is too large to be converted to dgCMatrix. (Thanks to @RookieA1 for reporting issue #95)
• Fixed forgetting dimnames when subsetting after certain sets of operations. (Thanks to @Yunuuuu for reporting issues #97 and #100)
• Fixed plotting crashes when running trackplot_coverage with fragments from a single cluster. (Thanks to @sjessa for directly reporting this bug and coming up with a fix)

BPCells 0.2.0

We are finally declaring a new release version, covering a large amount of changes and improvements over the past year. Among the major features here are parallelization options for svds() and matrix_stats(), improved genomic track plots, and runtime CPU feature detection for SIMD code (enables higher performance, more portable builds). Full details of changes below.

This version also comes with a new installation path, which is done in preparation for a future Python package release. (So we can have one folder for R and one for Python, rather than having all the R files sit in the root folder). This is a breaking change and requires a slightly modified installation command.

Thanks to @brgew, @ycli1995, and @Yunuuuu for pull requests that contributed to this release, as well as all users who submitted github issues to help identify and fix bugs.

Breaking changes

• Installation location has changed, to make room for a future python package release. New installs will have to use remotes::install_github("bnprks/BPCells/r") (note the additional /r)
  • r-universe mirrors will have to add "subdir": "r" to their packages.json config.
• New slots have been added to 10x matrix objects, so any saved RDS files may need to have their 10x matrix inputs re-opened and replaced by calling all_matrix_inputs(). Outside of loading old RDS files no changes should be needed.
• trackplot_gene() now returns a plot with a facet label to match the new trackplot system. This label can be removed by by calling trackplot_gene(...) + ggplot2::facet_null() to be equivalent to the old function's output.

Deprecations

• draw_trackplot_grid() deprecated, replaced by trackplot_combine() with simplified arguments
• trackplot_bulk() has been deprecated, replaced by trackplot_coverage() with equivalent functionality
• The old function names will output deprecation warnings, but otherwise work as before.

Features

• New svds() function, based on the excellent Spectra C++ library (used in RSpectra) by Yixuan Qiu. This should ensure lower memory usage compared to irlba, while achieving similar speed + accuracy.
• Limited parallelization is now supported. This is easiest to use via the threads argument to matrix_stats() and svds().
  • All normalizations are supported, but a few operations like marker_features() and writing a matrix to disk remain single-threaded.
  • Running svds() with many threads on gene-major matrices can result in high memory usage for now. This problem is not present for cell-major matrices.
• Reading text-based MatrixMarket inputs (e.g. from 10x or Parse) is now supported via import_matrix_market() and the convenience function import_matrix_market_10x(). Our implementation uses disk-backed sorting to allow importing large files with low memory usage.
• Added binarize() function and associated generics <, <=, >, and >=. This only supports comparison with non-negative numbers currently. (Thanks to contribution from @brgew)
• Added round() matrix transformation (Thanks to contributions from @brgew)
• Add getter/setter function all_matrix_inputs() to help enable relocating the underlying storage for BPCells matrix transform objects.
• All hdf5-writing functions now support a gzip_level parameter, which will enable a shuffle + gzip filter for compression. This is generally much slower than bitpacking compression, but it adds improved storage options for files that must be read by outside programs. Thanks to @ycli1995 for submitting this improvement in pull #42.
• AnnData export now supported via write_matrix_anndata_hdf5() (issue #49)
• Re-licensed code base to use dual-licensed Apache V2 or MIT instead of GPLv3
• Assigning to a subset is now supported (e.g. m1[i,j] <- m2). Note that this does not modify data on disk. Instead, it uses a series of subsetting and concatenation operations to provide the appearance of overwriting the appropriate entries.
• Added knn_to_geodesic_graph(), which matches the Scanpy default construction for graph-based clustering
• Add checksum(), which allows for calculating an MD5 checksum of a matrix contents. Thanks to @brgrew for submitting this improvement in pull request #83
• write_insertion_bedgraph() allows exporting pseudobulk insertion data to bedgraph format

Improvements

• Merging fragments with c() now handles inputs with mismatched chromosome names.
• Merging fragments is now 2-3.5x faster
• SNN graph construction in knn_to_snn_graph() should work more smoothly on large datasets due to C++ implementation
• Reduced memory usage in marker_features() for samples with millions of cells and a large number of clusters to compare.
• On Windows, increased the maximum number of files that can be simultaneously open. Previously, opening >63 compressed counts matrices simultaneously would hit the limit. Now at least 1,000 simultaneous matrices should be possible.
• Subsetting peak or tile matrices with [ now propagates through so we always avoid computing parts of the peak/tile matrix that have been discarded by our subset. Subsetting a tile matrix will automatically convert into a peak matrix when possible for improved efficiency.
• Subsetting RowBindMatrices and ColBindMatrices now propagates through so we avoid touching matrices with no selected indices
• Added logic to help reduce cases where subsetting causes BPCells to fall back to a less efficient matrix-vector multiply algorithm. This affects most math transforms. As part of this, the filtering part of a subset will propagate to earlier transformation steps, while the reordering will not. Thanks to @nimanouri-nm for raising issue #65 to fix a bug in the initial implementation.
• Additional C++17 filesystem backwards compatibility that should allow slightly older compilers such as GCC 7.5 to build BPCells.
• as.matrix() will produce integer matrices when appropriate (Thanks to @Yunuuuu in pull #77)
• 10x HDF5 matrices can now read and write non-integer types when requested (Thanks to @ycli1995 in pull #75)
• Old-style 10x files from cellranger v2 can now read multi-genome files, which are returned as a list (Thanks to @ycli1995 in pull #75)
• Trackplots have received several improvements
  • Trackplots now use faceting to provide per-plot labels, leading to an easier-to-use trackplot_combine()
  • trackplot_gene() now draws arrows for the direction of transcription
  • trackplot_loop() is a new track type allows plotting interactions between genomic regions, for instance peak-gene correlations or loop calls from Hi-C
  • trackplot_scalebar() is added to show genomic scale
  • All trackplot functions now return ggplot objects with additional metadata stored for the plotting height of each track
  • Labels and heights for trackplots can be adjusted using set_trackplot_label() and set_trackplot_height()
  • The getting started pbmc 3k vignette now includes the updated trackplot APIs in its final example
• Add rowVars() and colVars() functions, as convenience wrappers around matrix_stats(). If matrixStats or MatrixGenerics packages are installed, BPCells::rowVars() will fall back to their implementations for non-BPCells objects. Unfortunately, matrixStats::rowVars() is not generic, so either BPCells::rowVars() or BPCells::colVars()
• Optimize mean and variance calculations for matrices added to a per-row or per-column constant.
• Migrate SIMD code to use highway.
  • Adds run-time detection of CPU features to eliminate architecture-specific compilation
  • For now, the Pow SIMD implementation is removed, but Square gets a new SIMD implementation
  • Empirically, most operations using SIMD math instructions are about 2x faster. This includes log1p(), and sctransform_pearson()
  • Minor speedups on dense-sparse matrix multiply functions (1.1-1.5x faster)

Bug-fixes

• Fixed a few fragment transforms where using chrNames(frags) <- val or cellNames(frags) <- val could cause downstream errors.
• Fixed errors in transpose_storage_order() for matrices with >4 billion non-zero entries.
• Fixed error in transpose_storage_order() for matrices with no non-zero entries.
• Fixed bug writing fragment files with >512 chromosomes.
• Fixed bug when reading fragment files with >4 billion fragments.
• Fixed file permissions errors when using read-only hdf5 files (Issue #26 reported thanks to @ttumkaya)
• Renaming rownames() or colnames() is now propagated when saving matrices (Issue #29 reported thanks to @realzehuali, with an additional fix after report thanks to @Dario-Rocha)
• Fixed 64-bit integer overflow (!) that could cause incorrect p-value calculations in marker_features() for features with more than 2.6 million zeros.
• Improved robustness of the Windows installation process for setups that do not need the -lsz linker flag to compile hdf5
• Fixed possible memory safety bug where wrapped R objects (such as dgCMatrix) could be potentially garbage collected while C++ was still trying to access the data in rare circumstances.
• Fixed case when dimnames were not preserved when calling convert_matrix_type() twice in a row such that it cancels out (e.g. double -> uint32_t -> double). Thanks to @brgrew reporting issue #43
• Caused and fixed issue resulting in unusably slow performance reading matrices from HDF5 files. Broken versions range from commit 21f8dcf until the fix in 3711a40 (October 18-November 3, 2023). Thanks to @abhiachoudhary for reporting this in issue #53
• Fixed error with svds() not handling row-major matrices correctly. Thanks to @ycli1995 for reporting this in issue #55
• Fixed error with row/col name handling for AnnData matrices. Thanks to @lisch7 for reporting this in issue #57
• Fixed error with merging matrices of different data types. Thanks to @Yunuuuu for identifying the issue and providing a fix (#68 and #70)
• Fixed issue with losing dimnames on subset assignment [<-. Thanks to @Yunuuuu for identifying the issue #67
• Fixed incorrect results with some cases of scaling matrix after shifting. Thanks to @Yunuuuu for identifying the issue #72
• Fixed infinite loop bug when calling transpose_storage_order() on a densely-transformed matrix. Thanks to @Yunuuuu for reporting this in issue #71
• h5ad outputs will now subset properly when loaded by the Python anndata package (Thanks to issue described by @ggruenhagen3 in issue #49 and fixed by @ycli1995 in pull #81)
• Disk-backed fragment objects now load via absolute path, matching the behavior of matrices and making it so objects loaded via readRDS() can be used from different working directories.
• footprints() now respects user interrupts via Ctrl-C

BPCells 0.1.0

Features

• ATAC-seq Analysis
  • Reading/writing 10x fragment files on disk
  • Reading/writing compressed fragments on disk (in folder or hdf5 group)
  • Interconversion of fragments objects with GRanges / data.frame
  • Merging of multiple source fragment files transparently at run time
  • Calculation of Cell x Peak matrices, and Cell x Tile matrices
  • ArchR-compatible QC calculations
  • ArchR-compatible gene activity score calculations
  • Filtering fragments by chromosmes, cells, lengths, or genomic region
  • Fast peak calling approximation via overlapping tiles
• Single cell matrices
  • Conversion to/from R sparse matrices
  • Read-write access to 10x hdf5 feature matrices, and read-only access to AnnData files
  • Reading/writing of compressed matrices on disk (in folder or hdf5 group)
  • Support for integer or single/double-precision floating point matrices on disk
  • Fast transposition of storage order, to switch between indexing by cell or by gene/feature.
  • Concatenation of multiple source matrix files transparently at run time
  • Single-pass calculation of row/column mean and variance
  • Wilcoxon marker feature calculation
  • Transparent handling of vector +, -, *, /, and log1p for streaming normalization, along with other less common operations. This allows implementation of ATAC-seq LSI and Seurat default normalization, along with most published log-based normalizations.
  • SCTransform pearson residual calculation
  • Multiplication of sparse matrices
• Single cell plotting utilities
  • Read count knee cutoffs
  • UMAP embeddings
  • Dot plots
  • Transcription factor footprinting / TSS profile plotting
  • Fragments vs. TSS Enrichment ATAC-seq QC plot
  • Pseudobulk genome track plots, with gene annotation plots
• Additional utility functions
  • Matching gene symbols/IDs to canonical symbols
  • Download transcript annotations from Gencode or GTF files
  • Download + parse UCSC chromosome sizes
  • Parse peak files BED format; Download ENCODE blacklist region
  • Wrappers for knn graph calculation + clustering

Note: All operations interoperate with all storage formats. For example, all matrix operations can be applied directly to an AnnData or 10x matrix file. In many cases the bitpacking-compressed formats will provide performance/space advantages, but are not required to use the computations.