GenomeInfoDb, GenomicRanges, IRanges, Matrix, Rsamtools,
S4Vectors, Seurat (>= 4.0.6), SeuratObject (>= 4.0.0),
data.table, dplyr (>= 1.0.0), future, future.apply, ggplot2,
ggseqlogo, irlba, pbapply, tidyr, patchwork, stats, utils,
BiocGenerics, ggrepel, stringi, fastmatch, lsa, RcppRoll,
scales, Rcpp, ggforce, qlcMatrix, grid, tidyselect
'RcppExports.R' 'data.R' 'differential_accessibility.R'
'generics.R' 'dimension_reduction.R' 'footprinting.R'
'fragments.R' 'genomeinfodb-methods.R' 'granges-methods.R'
'heatmaps.R' 'iranges-methods.R' 'links.R' 'mito.R' 'motifs.R'
'objects.R' 'peaks.R' 'preprocessing.R' 'quantification.R'
'region-enrichment.R' 'utilities.R' 'visualization.R' 'zzz.R'

GenomeInfoDb (>= 1.29.3), GenomicRanges, IRanges, Matrix,
Rsamtools, S4Vectors, SeuratObject (>= 4.0.0), data.table,
dplyr (>= 1.0.0), future, future.apply, ggplot2, irlba,
pbapply, tidyr, patchwork, stats, utils, BiocGenerics, stringi,
fastmatch, RcppRoll, scales, Rcpp, grid, tidyselect, vctrs
Seurat (>= 4.0.6), ggforce, ggrepel, ggseqlogo, testthat (>=
2.1.0), chromVAR, SummarizedExperiment, TFBSTools, motifmatchr,
BSgenome, shiny, miniUI, rtracklayer, biovizBase, Biostrings,
lsa, qlcMatrix

Cell Type Identification and Discovery from Single Cell Gene
Expression Data
c(
person(given = "Mathew", family = "Chamberlain", email = "mathew.chamberlain@sanofi.com", role = c( "aut","cre")),
person(given = "Virginia", family = "Savova", email = "virginia.savova@sanofi.com", role = c( "aut")),
person(given = "Richa", family = "Hanamsagar", email = "richa.hanamsagar@sanofi.com", role = c( "aut")),
person(given = "Frank", family = "Nestle", email = "frank.nestle@sanofi.com", role = c( "aut")),
person(given = "Emanuele", family = "de Rinaldis", email = "Emanuele.deRinaldis@sanofi.com", role = c( "aut")),
person("Sanofi US", role = "fnd"))
neuralnet, lme4, methods, Matrix, pbmcapply, Seurat(>= 3.2.0),
RJSONIO, igraph (>= 1.2.1), jsonlite (>= 1.5), RColorBrewer (>=
1.1.2), stats
An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
Mathew Chamberlain [aut, cre],
Virginia Savova [aut],
Richa Hanamsagar [aut],
Frank Nestle [aut],
Emanuele de Rinaldis [aut],
Sanofi US [fnd]