1#ifndef SCRAN_PCA_SIMPLE_PCA_HPP
2#define SCRAN_PCA_SIMPLE_PCA_HPP
9#include "tatami_stats/tatami_stats.hpp"
13#include "sanisizer/sanisizer.hpp"
82template<
bool sparse_,
typename Value_,
typename Index_,
class EigenVector_>
83void compute_row_means_and_variances(
const tatami::Matrix<Value_, Index_>& mat,
const int num_threads, EigenVector_& center_v, EigenVector_& scale_v) {
84 const auto ngenes = mat.
nrow();
93 const auto ncells = mat.
ncol();
96 for (Index_ g = start, end = start + length; g < end; ++g) {
97 const auto results = [&]{
98 if constexpr(sparse_) {
99 auto range = ext->fetch(vbuffer.data(), NULL);
100 return tatami_stats::variances::direct(range.value, range.number, ncells,
false);
102 auto ptr = ext->fetch(vbuffer.data());
103 return tatami_stats::variances::direct(ptr, ncells,
false);
106 center_v.coeffRef(g) = results.first;
107 scale_v.coeffRef(g) = results.second;
109 }, ngenes, num_threads);
113 const auto ncells = mat.
ncol();
116 typedef typename EigenVector_::Scalar Scalar;
117 tatami_stats::LocalOutputBuffer<Scalar> cbuffer(t, start, length, center_v.data());
118 tatami_stats::LocalOutputBuffer<Scalar> sbuffer(t, start, length, scale_v.data());
121 if constexpr(sparse_) {
122 return tatami_stats::variances::RunningSparse<Scalar, Value_, Index_>(length, cbuffer.data(), sbuffer.data(),
false, start);
124 return tatami_stats::variances::RunningDense<Scalar, Value_, Index_>(length, cbuffer.data(), sbuffer.data(),
false);
130 if constexpr(sparse_) {
137 for (Index_ c = 0; c < ncells; ++c) {
138 if constexpr(sparse_) {
139 const auto range = ext->fetch(vbuffer.data(), ibuffer.data());
140 running.add(range.value, range.index, range.number);
142 const auto ptr = ext->fetch(vbuffer.data());
150 }, ngenes, num_threads);
154template<
class IrlbaMatrix_,
class EigenMatrix_,
class EigenVector_>
155auto run_irlba_deferred(
156 const IrlbaMatrix_& mat,
157 const SimplePcaOptions& options,
158 EigenMatrix_& components,
159 EigenMatrix_& rotation,
160 EigenVector_& variance_explained,
161 EigenVector_& center_v,
162 EigenVector_& scale_v)
166 irlba::Scaled<
true,
decltype(I(centered)), EigenVector_> scaled(centered, scale_v,
true);
167 return irlba::compute(scaled, options.number, components, rotation, variance_explained, options.irlba_options);
169 return irlba::compute(centered, options.number, components, rotation, variance_explained, options.irlba_options);
173template<
typename Value_,
typename Index_,
class EigenMatrix_,
class EigenVector_>
176 const SimplePcaOptions& options,
177 EigenMatrix_& components,
178 EigenMatrix_& rotation,
179 EigenVector_& variance_explained,
180 EigenVector_& center_v,
181 EigenVector_& scale_v,
182 typename EigenVector_::Scalar& total_var,
185 const auto ngenes = mat.
nrow();
186 sanisizer::resize(center_v, ngenes);
187 sanisizer::resize(scale_v, ngenes);
189 if (options.realize_matrix) {
203 const Index_ ncells = mat.
ncol();
207 std::move(extracted.value),
208 std::move(extracted.index),
209 std::move(extracted.pointers),
215 const auto& pointers = emat.get_pointers();
216 const auto& values = emat.get_values();
217 for (Index_ g = start, end = start + length; g < end; ++g) {
218 const auto offset = pointers[g];
219 const auto next_offset = pointers[g + 1];
220 const Index_ num_nonzero = next_offset - offset;
221 const auto results = tatami_stats::variances::direct(values.data() + offset, num_nonzero, ncells,
false);
222 center_v.coeffRef(g) = results.first;
223 scale_v.coeffRef(g) = results.second;
225 }, ngenes, options.num_threads);
227 total_var = internal::process_scale_vector(options.scale, scale_v);
228 const auto out = run_irlba_deferred(emat, options, components, rotation, variance_explained, center_v, scale_v);
229 converged = out.first;
232 compute_row_means_and_variances<true>(mat, options.num_threads, center_v, scale_v);
233 total_var = internal::process_scale_vector(options.scale, scale_v);
234 const auto out = run_irlba_deferred(
235 internal::TransposedTatamiWrapper<EigenVector_, Value_, Index_>(mat, options.num_threads),
243 converged = out.first;
247template<
typename Value_,
typename Index_,
class EigenMatrix_,
class EigenVector_>
250 const SimplePcaOptions& options,
251 EigenMatrix_& components,
252 EigenMatrix_& rotation,
253 EigenVector_& variance_explained,
254 EigenVector_& center_v,
255 EigenVector_& scale_v,
256 typename EigenVector_::Scalar& total_var,
259 const Index_ ngenes = mat.
nrow();
260 sanisizer::resize(center_v, ngenes);
261 sanisizer::resize(scale_v, ngenes);
263 if (options.realize_matrix) {
265 const Index_ ncells = mat.
ncol();
267 sanisizer::cast<
decltype(I(std::declval<EigenMatrix_>().rows()))>(ncells),
268 sanisizer::cast<
decltype(I(std::declval<EigenMatrix_>().cols()))>(ngenes)
278 tatami::ConvertToDenseOptions opt;
279 opt.num_threads = options.num_threads;
284 center_v.array() = emat.array().colwise().sum();
288 std::fill(center_v.begin(), center_v.end(), std::numeric_limits<typename EigenVector_::Scalar>::quiet_NaN());
290 emat.array().rowwise() -= center_v.adjoint().array();
292 scale_v.array() = emat.array().colwise().squaredNorm();
294 scale_v /= ncells - 1;
296 std::fill(scale_v.begin(), scale_v.end(), std::numeric_limits<typename EigenVector_::Scalar>::quiet_NaN());
299 total_var = internal::process_scale_vector(options.scale, scale_v);
301 emat.array().rowwise() /= scale_v.adjoint().array();
304 const auto out =
irlba::compute(emat, options.number, components, rotation, variance_explained, options.irlba_options);
305 converged = out.first;
308 compute_row_means_and_variances<false>(mat, options.num_threads, center_v, scale_v);
309 total_var = internal::process_scale_vector(options.scale, scale_v);
310 const auto out = run_irlba_deferred(
311 internal::TransposedTatamiWrapper<EigenVector_, Value_, Index_>(mat, options.num_threads),
319 converged = out.first;
333template<
typename EigenMatrix_,
typename EigenVector_>
403template<
typename Value_,
typename Index_,
typename EigenMatrix_,
class EigenVector_>
418 if (!options.
scale) {
419 output.
scale = EigenVector_();
438template<
typename EigenMatrix_ = Eigen::MatrixXd,
class EigenVector_ = Eigen::VectorXd,
typename Value_,
typename Index_>
virtual Index_ ncol() const=0
virtual Index_ nrow() const=0
virtual bool prefer_rows() const=0
virtual std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > > sparse(bool row, const Options &opt) const=0
std::pair< bool, int > compute(const Matrix_ &matrix, Eigen::Index number, EigenMatrix_ &outU, EigenMatrix_ &outV, EigenVector_ &outD, const Options &options)
Principal component analysis on single-cell data.
void simple_pca(const tatami::Matrix< Value_, Index_ > &mat, const SimplePcaOptions &options, SimplePcaResults< EigenMatrix_, EigenVector_ > &output)
Definition simple_pca.hpp:404
CompressedSparseContents< StoredValue_, StoredIndex_, StoredPointer_ > retrieve_compressed_sparse_contents(const Matrix< InputValue_, InputIndex_ > &matrix, bool row, const RetrieveCompressedSparseContentsOptions &options)
void parallelize(Function_ fun, Index_ tasks, int threads)
Container_ create_container_of_Index_size(Index_ x, Args_ &&... args)
void convert_to_dense(const Matrix< InputValue_, InputIndex_ > &matrix, bool row_major, StoredValue_ *store, const ConvertToDenseOptions &options)
auto consecutive_extractor(const Matrix< Value_, Index_ > &matrix, bool row, Index_ iter_start, Index_ iter_length, Args_ &&... args)
Options for simple_pca().
Definition simple_pca.hpp:27
bool realize_matrix
Definition simple_pca.hpp:63
bool transpose
Definition simple_pca.hpp:57
int num_threads
Definition simple_pca.hpp:69
irlba::Options irlba_options
Definition simple_pca.hpp:74
bool scale
Definition simple_pca.hpp:51
int number
Definition simple_pca.hpp:44
Results of simple_pca().
Definition simple_pca.hpp:334
EigenMatrix_ components
Definition simple_pca.hpp:341
EigenMatrix_ rotation
Definition simple_pca.hpp:360
bool converged
Definition simple_pca.hpp:377
EigenVector_ center
Definition simple_pca.hpp:366
EigenVector_ variance_explained
Definition simple_pca.hpp:347
EigenVector_ scale
Definition simple_pca.hpp:372
EigenVector_::Scalar total_variance
Definition simple_pca.hpp:353
bool sparse_extract_index