Package: scBFA 1.27.0

Ruoxin Li

scBFA: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.

Authors:Ruoxin Li [aut, cre], Gerald Quon [aut]

scBFA_1.27.0.tar.gz
scBFA_1.27.0.zip(r-4.7)scBFA_1.27.0.zip(r-4.6)scBFA_1.27.0.zip(r-4.5)
scBFA_1.27.0.tgz(r-4.6-any)scBFA_1.27.0.tgz(r-4.5-any)
scBFA_1.27.0.tar.gz(r-4.7-any)scBFA_1.27.0.tar.gz(r-4.6-any)
scBFA_1.27.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scBFA/json (API)

# Install 'scBFA' in R:

install.packages('scBFA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

celltype - Cell types as labels of example scRNA-seq dataset
celltype_toy - Toy cell type vector with 3 cell types generated for 5 cells in toy dataset
disperPlot - Reference dataset
exprdata - ScRNA-seq dataset
zinb_toy - Example zinb object after fitting a toy dataset with 5 cells and 10 genes

On BioConductor:scBFA-1.27.0(bioc 3.24)scBFA-1.26.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

singlecell transcriptomics dimensionreduction geneexpression atacseq batcheffect kegg qualitycontrol

4.30 score 6 scripts 2 mentions 7 exports 189 dependencies

Last updated from:2d0371865f. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	261
linux-devel-x86_64	NOTE	472
source / vignettes	OK	355
linux-release-x86_64	NOTE	511
macos-release-arm64	NOTE	236
macos-oldrel-arm64	NOTE	211
windows-devel	NOTE	359
windows-release	NOTE	343
windows-oldrel	NOTE	366
wasm-release	OK	222

Exports:BinaryPCA diagnose getGeneExpr getLoading getScore scBFA scNoiseSim

Dependencies:abind ADGofTest annotate AnnotationDbi askpass base64enc BH Biobase BiocGenerics BiocParallel Biostrings bit bit64 bitops blob bslib cachem caTools cli cluster codetools colorspace commonmark copula cowplot cpp11 crayon crosstalk curl data.table DBI DelayedArray deldir DESeq2 digest dotCall64 dplyr dqrng edgeR evaluate farver fastDummies fastmap fitdistrplus FNN fontawesome formatR fs futile.logger futile.options future future.apply genefilter generics GenomicRanges ggplot2 ggrepel ggridges globals glue goftest gplots gridExtra gsl gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph IRanges irlba isoband jquerylib jsonlite KEGGREST KernSmooth knitr labeling lambda.r later lattice lazyeval lifecycle limma listenv lmtest locfit magrittr MASS Matrix MatrixGenerics matrixStats memoise mime miniUI mvtnorm nlme numDeriv openssl otel parallelly patchwork pbapply pcaPP pillar pkgconfig plotly plyr png polyclip progressr promises pspline purrr R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML reshape2 reticulate rlang rmarkdown ROCR rprojroot RSpectra RSQLite Rtsne S4Arrays S4Vectors S7 sass scales scattermore sctransform Seqinfo Seurat SeuratObject shiny SingleCellExperiment sitmo snow softImpute sourcetools sp spam SparseArray spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stabledist statmod stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite withr xfun XML xtable XVector yaml zinbwave zoo

Gene Detection Analysis for scRNA-seq

Rendered fromvignette.Rmdusingknitr::rmarkdown

Last update: 2019-08-07
Started: 2019-03-21

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Performs Binary PCA (as outlined in our paper). This function take the input of gene expression profile and perform PCA on gene detection pattern	BinaryPCA
Cell types as labels of example scRNA-seq dataset(exprdata)	celltype
toy cell type vector with 3 cell types generated for 5 cells in toy dataset	celltype_toy
Perform diagnoisis of dispersion on the expression profile to check whether scBFA works on specific dataset	diagnose
Reference dataset(disperPlot)	disperPlot
scRNA-seq dataset(exprdata)	exprdata
Function to extract gene expression matrix from input observation matrix	getGeneExpr
Function to get low dimensional loading matrix	getLoading
Function to get low dimensional embedding matrix	getScore
Calculate gradient of the negative log likelihood, used for calls to the optim() function.	gradient
Calculate gradient of the negative log likelihood, used for calls to the optim() function.	gradient_chunk
Perform BFA model on the expression profile	scBFA
simulation to generate scRNA-seq data with varying level of gene detection noise versus gene count noise	scNoiseSim
example zinb object after fitting a toy dataset with 5 cells and 10 genes	zinb_toy