scran_ component libraries in the libscran organization instead.
The libscran library takes core parts of the scran Bioconductor package (as well as other useful bits from other packages) and implements them in C++. The idea is to provide a light-weight library that can be easily embedded into other applications without including the entire R/Bioconductor runtime. For example, we can compile libscran to WebAssembly to perform single-cell analyses in the browser; or we can wrap libscran into an R package for a minimal-dependency version of the basic Bioconductor single-cell analysis stack. The library itself is compatible with any CMake-based build system and can be turned into a fully header-only library for easy deployment.
The example below demonstrates how to use libscran to run a standard analysis of single-cell RNA-seq data.
#include "scran/scran.hpp"
// Loading the data from an unzipped MatrixMarket file.
auto mat = tatami_mtx::load_matrix_from_file<false, double, int>(argv[1]);
// Filtering out low-quality cells.
auto qc_res = scran::PerCellRnaQcMetrics().run(mat.get(), { /* mito subset definitions go here */ });
auto qc_filters = scran::SuggestRnaQcFilters().run(qc_res);
auto low_quality = qc_filters.filter(qc_res);
auto filtered = scran::FilterCells().run(mat, low_quality.data());
// Computing log-normalized expression values, re-using the total count from the QC step.
auto size_factors = scran::subset_vector<false>(qc_res.sums, low_quality.data());
auto normalized = scran::LogNormCounts().run(filtered, std::move(size_factors));
// Identifying highly variable genes.
auto var_res = scran::ModelGeneVariances().run(normalized.get());
auto keep = scran::ChooseHvgs().run(var_res.residuals.size(), var_res.residuals.data());
// Performing a PCA on the HVGs.
int npcs = 20;
auto pca_res = scran::SimplePca().set_rank(npcs).run(normalized.get(), keep.data());
// Performing clustering.
auto graph = scran::BuildSnnGraph().run(npcs, pca_res.pcs.cols(), pca_res.pcs.data());
auto clust_res = scran::ClusterSnnGraphMultiLevel().run(graph);
const auto& best_clustering = clust_res.membership[clust_res.max];
// Throw in some marker detection.
auto marker_res = scran::ScoreMarkers().run(normalized.get(), best_clustering.data());Each class represents a step in the analysis and has tunable parameters, e.g., RunPCA::set_rank to set the number of PCs.
See the reference documentation for more details.
Most of the functions are motivated by the theory in the Orchestrating single-cell analysis with Bioconductor book.
Identification and filtering of low-quality cells are performed using an outlier-based approach.
The PerCellQCMetrics class will compute common QC metrics,
the PerCellQCFilters class will identify filtering thresholds from the distribution of such metrics,
and the FilterCells class will apply those filters to the count matrix.
Log-transformed normalized expression values are computed from the count matrix,
using size factors derived from the library size.
This is performed using the LogNormCounts class.
Variance modelling and selection of highly variable genes is performed on the log-expression values.
The ModelGeneVar class will fit a mean-dependent trend to the variances across genes,
while the ChooseHVGs class will choose the top set of HVGs based on the residuals from the trend.
Principal component analysis is used to compress and denoise the data based on the first few PCs.
The RunPCA class will use an approximate PCA algorithm to efficiently compute the top PCs from the HVG-subsetted matrix.
Alternatively, the BlockedPCA and MultiBatchPCA classes can be used when dealing with multiple batches.
Clustering of cells is performed using the per-cell PC scores.
We provide several flavors of graph-based clustering from a shared-nearest neighbor graph,
using community detection algorithms such as multi-level (ClusterSnnGraphMultiLevel), Leiden (ClusterSnnGraphLeiden) or Walktrap clustering (ClusterSnnGraphWalktrap).
Developers can also easily apply other algorithms, e.g., k-means.
Per-cluster marker detection is performed based on pairwise comparisons between clusters.
The ScoreMarkers class will aggregate the set of pairwise comparisons into a single suite of summary statistics for each cluster.
Users can then rank by a statistic of interest to obtain a marker listing for each cluster.
The output of PCA is also directly compatible with UMAP and t-SNE C++ implementations. Readers are referred to the documentation for those libraries for more details.
Compile the minimal.cpp example by running the following commands at the root of the libscran directory:
cmake -S . -B example -DBUILD_TESTING=OFF -DBUILD_GALLERY=ON
cmake --build example --target minimalDownload and decompress a Matrix Market file containing a scRNA-seq count matrix:
mkdir example/data
wget https://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc4k/pbmc4k_filtered_gene_bc_matrices.tar.gz -P example/data
tar -xvf example/data/pbmc4k_filtered_gene_bc_matrices.tar.gz --directory example/dataRun the minimal pipeline:
time example/gallery/minimal example/data/filtered_gene_bc_matrices/GRCh38/matrix.mtx
## Detected 11 clusters in 'example/data/filtered_gene_bc_matrices/GRCh38/matrix.mtx'
## Sizes are 937, 534, 1018, 37, 135, 384, 537, 225, 190, 123, 191
##
## real 0m2.340s
## user 0m9.613s
## sys 0m0.104sIf you're using CMake, you just need to add something like this to your CMakeLists.txt:
include(FetchContent)
FetchContent_Declare(
libscran
GIT_REPOSITORY https://github.com/LTLA/libscran
GIT_TAG master # or any version of interest
)
FetchContent_MakeAvailable(libscran)Then you can link to libscran to make the headers available during compilation:
# For executables:
target_link_libraries(myexe libscran)
# For libaries
target_link_libraries(mylib INTERFACE libscran)Developers are responsible for linking to the igraph C library themselves, either with find_package() or FetchContent.
We expect igraph versions from the 0.10 series - see tests/CMakeLists.txt for the specific version being tested.