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Merge pull request #295 from mvfki/master
Release 1.0.1
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^LICENSE$ | ||
^\.lintr$ | ||
^\.vscode | ||
^.*\.Rproj$ | ||
^\.Rproj\.user$ | ||
^\.travis\.yml$ | ||
^travis_setup\.sh$ | ||
^_config\.yml$ | ||
^vignettes/walkthrough_pbmc\.Rmd$ | ||
^docs$ | ||
^appveyor\.yml$ | ||
^vignettes/.*html$ | ||
^vignettes/articles | ||
^vignettes/Integrating_multi_scRNA_data\.rmd$ | ||
^vignettes/Integrating_scRNA_and_scATAC_data\.Rmd$ | ||
^vignettes/Integrating_multi_scRNA_data\.Rmd$ | ||
^vignettes/Parameter_selection\.Rmd$ | ||
^vignettes/SNAREseq_walkthrough\.Rmd$ | ||
^vignettes/STARmap_dropviz_vig\.Rmd$ | ||
^vignettes/UINMF_vignette\.Rmd$ | ||
^vignettes/online_iNMF_tutorial\.Rmd$ | ||
^vignettes/pbmc_alignment\.zip$ | ||
^vignettes/walkthrough_pbmc\.Rmd$ | ||
^vignettes/walkthrough_pbmc\.pdf$ | ||
^vignettes/cross_species_vig\.Rmd$ | ||
^docs | ||
^devdata |
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Package: rliger | ||
Version: 1.0.0 | ||
Date: 2021-03-09 | ||
Version: 1.0.1 | ||
Date: 2023-11-08 | ||
Type: Package | ||
Title: Linked Inference of Genomic Experimental Relationships | ||
Description: Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details. | ||
Authors@R: c( | ||
person(given = 'Joshua', family = 'Welch', email = '[email protected]', role = c('aut', 'ctb')), | ||
person(given = 'Chao', family = 'Gao', email = '[email protected]', role = c('aut', 'ctb', 'cre')), | ||
person(given = 'Chao', family = 'Gao', email = '[email protected]', role = c('aut', 'ctb')), | ||
person(given = 'Jialin', family = 'Liu', email = '[email protected]', role = c('aut', 'ctb')), | ||
person(given = 'Joshua', family = 'Sodicoff', email = '[email protected]', role = c('aut', 'ctb')), | ||
person(given = 'Velina', family = 'Kozareva', role = c('aut', 'ctb')), | ||
person(given = 'Evan', family = 'Macosko', role = c('aut', 'ctb')), | ||
person(given = 'Yichen', family = 'Wang', email = '[email protected]', role = c('aut', 'ctb', 'cre')), | ||
person(given = 'Paul', family = 'Hoffman', role = 'ctb'), | ||
person(given = 'Ilya', family = 'Korsunsky', role = 'ctb'), | ||
person(given = 'Robert', family = 'Lee', role = 'ctb') | ||
) | ||
Author: Joshua Welch [aut, ctb], | ||
Chao Gao [aut, ctb, cre], | ||
Chao Gao [aut, ctb], | ||
Jialin Liu [aut, ctb], | ||
Joshua Sodicoff [aut, ctb], | ||
Velina Kozareva [aut, ctb], | ||
Evan Macosko [aut, ctb], | ||
Yichen Wang [aut, ctb, cre], | ||
Paul Hoffman [ctb], | ||
Ilya Korsunsky [ctb], | ||
Robert Lee [ctb] | ||
Maintainer: Chao Gao <gchao@umich.edu> | ||
Maintainer: Yichen Wang <wayichen@umich.edu> | ||
BugReports: https://github.com/welch-lab/liger/issues | ||
URL: https://github.com/welch-lab/liger | ||
License: GPL-3 | file LICENSE | ||
License: GPL-3 | ||
Imports: Rcpp (>= 0.12.10), | ||
plyr, | ||
FNN, | ||
|
@@ -38,31 +40,32 @@ Imports: Rcpp (>= 0.12.10), | |
ica, | ||
Rtsne, | ||
ggplot2, | ||
riverplot, | ||
foreach, | ||
parallel, | ||
doParallel, | ||
mclust, | ||
stats, | ||
psych, | ||
RANN, | ||
uwot, | ||
rlang, | ||
utils, | ||
hdf5r, | ||
scattermore (>= 0.7) | ||
scattermore (>= 0.7), | ||
patchwork, | ||
cowplot | ||
biocViews: | ||
LazyData: true | ||
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppProgress | ||
Depends: | ||
cowplot, | ||
R (>= 3.4), | ||
Matrix, | ||
methods, | ||
patchwork | ||
RoxygenNote: 7.1.1 | ||
stats, | ||
utils | ||
RoxygenNote: 7.2.3 | ||
Encoding: UTF-8 | ||
Suggests: | ||
Seurat, | ||
SeuratObject, | ||
knitr, | ||
reticulate, | ||
rmarkdown, | ||
|
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## rliger 1.0.1 | ||
|
||
- Allow setting mito pattern in `getMitoProportion()` #271 | ||
- Fix efficiency issue when taking the log of norm.data (e.g. `runWilcoxon`) | ||
- Add runable examples to all exported functions when possible | ||
- Fix typo in online_iNMF matrix initialization | ||
- Adapt to Seurat5 | ||
- Other minor fixes | ||
|
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# Generated by using Rcpp::compileAttributes() -> do not edit by hand | ||
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 | ||
|
||
RunModularityClusteringCpp <- function(SNN, modularityFunction, resolution, algorithm, nRandomStarts, nIterations, randomSeed, printOutput, edgefilename) { | ||
.Call('_rliger_RunModularityClusteringCpp', PACKAGE = 'rliger', SNN, modularityFunction, resolution, algorithm, nRandomStarts, nIterations, randomSeed, printOutput, edgefilename) | ||
} | ||
|
||
scaleNotCenterFast <- function(x) { | ||
.Call('_rliger_scaleNotCenterFast', PACKAGE = 'rliger', x) | ||
} | ||
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rowMeansFast <- function(x) { | ||
.Call('_rliger_rowMeansFast', PACKAGE = 'rliger', x) | ||
} | ||
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rowVarsFast <- function(x, means) { | ||
.Call('_rliger_rowVarsFast', PACKAGE = 'rliger', x, means) | ||
} | ||
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sumSquaredDeviations <- function(x, means) { | ||
.Call('_rliger_sumSquaredDeviations', PACKAGE = 'rliger', x, means) | ||
} | ||
|
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cpp_sumGroups_dgc <- function(x, p, i, ncol, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dgc', PACKAGE = 'rliger', x, p, i, ncol, groups, ngroups) | ||
} | ||
|
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cpp_sumGroups_dgc_T <- function(x, p, i, ncol, nrow, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dgc_T', PACKAGE = 'rliger', x, p, i, ncol, nrow, groups, ngroups) | ||
} | ||
|
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cpp_sumGroups_dense <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dense', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
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cpp_sumGroups_dense_T <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dense_T', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
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cpp_nnzeroGroups_dense <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dense', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
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cpp_nnzeroGroups_dense_T <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dense_T', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
||
cpp_nnzeroGroups_dgc <- function(p, i, ncol, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dgc', PACKAGE = 'rliger', p, i, ncol, groups, ngroups) | ||
} | ||
|
||
cpp_in_place_rank_mean <- function(v_temp, idx_begin, idx_end) { | ||
.Call('_rliger_cpp_in_place_rank_mean', PACKAGE = 'rliger', v_temp, idx_begin, idx_end) | ||
} | ||
|
||
cpp_rank_matrix_dgc <- function(x, p, nrow, ncol) { | ||
.Call('_rliger_cpp_rank_matrix_dgc', PACKAGE = 'rliger', x, p, nrow, ncol) | ||
} | ||
|
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cpp_rank_matrix_dense <- function(X) { | ||
.Call('_rliger_cpp_rank_matrix_dense', PACKAGE = 'rliger', X) | ||
} | ||
|
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cpp_nnzeroGroups_dgc_T <- function(p, i, ncol, nrow, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dgc_T', PACKAGE = 'rliger', p, i, ncol, nrow, groups, ngroups) | ||
} | ||
|
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#' Fast calculation of feature count matrix | ||
#' | ||
#' @param bedmat A feature count list generated by bedmap | ||
#' @param barcodes A list of barcodes | ||
#' | ||
#' @return A feature count matrix with features as rows and barcodes as | ||
#' columns | ||
#' @export | ||
#' @examples | ||
#' \dontrun{ | ||
#' gene.counts <- makeFeatureMatrix(genes.bc, barcodes) | ||
#' promoter.counts <- makeFeatureMatrix(promoters.bc, barcodes) | ||
#' samnple <- gene.counts + promoter.counts | ||
#' } | ||
makeFeatureMatrix <- function(bedmat, barcodes) { | ||
.Call('_rliger_makeFeatureMatrix', PACKAGE = 'rliger', bedmat, barcodes) | ||
} | ||
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cluster_vote <- function(nn_ranked, clusts) { | ||
.Call('_rliger_cluster_vote', PACKAGE = 'rliger', nn_ranked, clusts) | ||
} | ||
|
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scale_columns_fast <- function(mat, scale = TRUE, center = TRUE) { | ||
.Call('_rliger_scale_columns_fast', PACKAGE = 'rliger', mat, scale, center) | ||
} | ||
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max_factor <- function(H, dims_use, center_cols = FALSE) { | ||
.Call('_rliger_max_factor', PACKAGE = 'rliger', H, dims_use, center_cols) | ||
} | ||
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solveNNLS <- function(C, B) { | ||
.Call('_rliger_solveNNLS', PACKAGE = 'rliger', C, B) | ||
} | ||
|
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ComputeSNN <- function(nn_ranked, prune) { | ||
.Call('_rliger_ComputeSNN', PACKAGE = 'rliger', nn_ranked, prune) | ||
} | ||
|
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WriteEdgeFile <- function(snn, filename, display_progress) { | ||
invisible(.Call('_rliger_WriteEdgeFile', PACKAGE = 'rliger', snn, filename, display_progress)) | ||
} | ||
|
||
DirectSNNToFile <- function(nn_ranked, prune, display_progress, filename) { | ||
.Call('_rliger_DirectSNNToFile', PACKAGE = 'rliger', nn_ranked, prune, display_progress, filename) | ||
} | ||
|
||
# Generated by using Rcpp::compileAttributes() -> do not edit by hand | ||
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 | ||
|
||
RunModularityClusteringCpp <- function(SNN, modularityFunction, resolution, algorithm, nRandomStarts, nIterations, randomSeed, printOutput, edgefilename) { | ||
.Call('_rliger_RunModularityClusteringCpp', PACKAGE = 'rliger', SNN, modularityFunction, resolution, algorithm, nRandomStarts, nIterations, randomSeed, printOutput, edgefilename) | ||
} | ||
|
||
scaleNotCenterFast <- function(x) { | ||
.Call('_rliger_scaleNotCenterFast', PACKAGE = 'rliger', x) | ||
} | ||
|
||
rowMeansFast <- function(x) { | ||
.Call('_rliger_rowMeansFast', PACKAGE = 'rliger', x) | ||
} | ||
|
||
rowVarsFast <- function(x, means) { | ||
.Call('_rliger_rowVarsFast', PACKAGE = 'rliger', x, means) | ||
} | ||
|
||
sumSquaredDeviations <- function(x, means) { | ||
.Call('_rliger_sumSquaredDeviations', PACKAGE = 'rliger', x, means) | ||
} | ||
|
||
cpp_sumGroups_dgc <- function(x, p, i, ncol, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dgc', PACKAGE = 'rliger', x, p, i, ncol, groups, ngroups) | ||
} | ||
|
||
cpp_sumGroups_dgc_T <- function(x, p, i, ncol, nrow, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dgc_T', PACKAGE = 'rliger', x, p, i, ncol, nrow, groups, ngroups) | ||
} | ||
|
||
cpp_sumGroups_dense <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dense', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
||
cpp_sumGroups_dense_T <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_sumGroups_dense_T', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
||
cpp_nnzeroGroups_dense <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dense', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
||
cpp_nnzeroGroups_dense_T <- function(X, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dense_T', PACKAGE = 'rliger', X, groups, ngroups) | ||
} | ||
|
||
cpp_nnzeroGroups_dgc <- function(p, i, ncol, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dgc', PACKAGE = 'rliger', p, i, ncol, groups, ngroups) | ||
} | ||
|
||
cpp_in_place_rank_mean <- function(v_temp, idx_begin, idx_end) { | ||
.Call('_rliger_cpp_in_place_rank_mean', PACKAGE = 'rliger', v_temp, idx_begin, idx_end) | ||
} | ||
|
||
cpp_rank_matrix_dgc <- function(x, p, nrow, ncol) { | ||
.Call('_rliger_cpp_rank_matrix_dgc', PACKAGE = 'rliger', x, p, nrow, ncol) | ||
} | ||
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cpp_rank_matrix_dense <- function(X) { | ||
.Call('_rliger_cpp_rank_matrix_dense', PACKAGE = 'rliger', X) | ||
} | ||
|
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cpp_nnzeroGroups_dgc_T <- function(p, i, ncol, nrow, groups, ngroups) { | ||
.Call('_rliger_cpp_nnzeroGroups_dgc_T', PACKAGE = 'rliger', p, i, ncol, nrow, groups, ngroups) | ||
} | ||
|
||
#' Fast calculation of feature count matrix | ||
#' | ||
#' @param bedmat A feature count list generated by bedmap | ||
#' @param barcodes A list of barcodes | ||
#' | ||
#' @return A feature count matrix with features as rows and barcodes as | ||
#' columns | ||
#' @export | ||
#' @examples | ||
#' \dontrun{ | ||
#' gene.counts <- makeFeatureMatrix(genes.bc, barcodes) | ||
#' promoter.counts <- makeFeatureMatrix(promoters.bc, barcodes) | ||
#' samnple <- gene.counts + promoter.counts | ||
#' } | ||
makeFeatureMatrix <- function(bedmat, barcodes) { | ||
.Call('_rliger_makeFeatureMatrix', PACKAGE = 'rliger', bedmat, barcodes) | ||
} | ||
|
||
cluster_vote <- function(nn_ranked, clusts) { | ||
.Call('_rliger_cluster_vote', PACKAGE = 'rliger', nn_ranked, clusts) | ||
} | ||
|
||
scale_columns_fast <- function(mat, scale = TRUE, center = TRUE) { | ||
.Call('_rliger_scale_columns_fast', PACKAGE = 'rliger', mat, scale, center) | ||
} | ||
|
||
max_factor <- function(H, dims_use, center_cols = FALSE) { | ||
.Call('_rliger_max_factor', PACKAGE = 'rliger', H, dims_use, center_cols) | ||
} | ||
|
||
solveNNLS <- function(C, B) { | ||
.Call('_rliger_solveNNLS', PACKAGE = 'rliger', C, B) | ||
} | ||
|
||
ComputeSNN <- function(nn_ranked, prune) { | ||
.Call('_rliger_ComputeSNN', PACKAGE = 'rliger', nn_ranked, prune) | ||
} | ||
|
||
WriteEdgeFile <- function(snn, filename, display_progress) { | ||
invisible(.Call('_rliger_WriteEdgeFile', PACKAGE = 'rliger', snn, filename, display_progress)) | ||
} | ||
|
||
DirectSNNToFile <- function(nn_ranked, prune, display_progress, filename) { | ||
.Call('_rliger_DirectSNNToFile', PACKAGE = 'rliger', nn_ranked, prune, display_progress, filename) | ||
} | ||
|
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#' dgCMatrix object of PBMC subsample data with Control and Stimulated datasets | ||
#' @format \code{dgCMatrix} object of gene expression matrix from PBMC study, | ||
#' named by "ctrl" and "stim". | ||
#' @source https://www.nature.com/articles/nbt.4042 | ||
#' @references Hyun Min Kang and et. al., Nature Biotechnology, 2018 | ||
#' @rdname liger-demodata | ||
"ctrl" | ||
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||
#' @rdname liger-demodata | ||
"stim" |
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