Package: SignacX 2.2.0

SignacX: Cell Type Identification and Discovery from Single Cell Gene Expression Data

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.

Authors:Mathew Chamberlain [aut, cre], Virginia Savova [aut], Richa Hanamsagar [aut], Frank Nestle [aut], Emanuele de Rinaldis [aut], Sanofi US [fnd]

SignacX_2.2.0.tar.gz
SignacX_2.2.0.zip(r-4.7)SignacX_2.2.0.zip(r-4.6)SignacX_2.2.0.zip(r-4.5)
SignacX_2.2.0.tgz(r-4.6-any)SignacX_2.2.0.tgz(r-4.5-any)
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SignacX_2.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SignacX/json (API)

# Install 'SignacX' in R:

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

Bug tracker:https://github.com/mathewchamberlain/signacx/issues

Datasets:

Genes_Of_Interest - Genes of interest for drug discovery / disease biology research

On CRAN:

cellular-phenotypes seurat single-cell-rna-seq

6.65 score 27 stars 47 scripts 376 downloads 20 exports 153 dependencies

Last updated from:11e872c292. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	189
source / vignettes	OK	265
linux-release-x86_64	WARNING	189
macos-release-arm64	WARNING	165
macos-oldrel-arm64	WARNING	154
windows-devel	WARNING	112
windows-release	WARNING	141
windows-oldrel	WARNING	127
wasm-release	OK	200

Exports:CID.entropy CID.GetDistMat CID.IsUnique CID.LoadData CID.LoadEdges CID.Louvain CID.Normalize CID.smooth CID.writeJSON GenerateLabels get_colors GetModels_HPCA GetTrainingData_HPCA KSoftImpute MASC ModelGenerator SaveCountsToH5 Signac SignacBoot SignacFast

Dependencies:abind askpass base64enc BH bitops boot bslib cachem caTools cli cluster codetools commonmark cowplot cpp11 crosstalk curl data.table deldir Deriv digest dotCall64 dplyr dqrng evaluate farver fastDummies fastmap fitdistrplus FNN fontawesome fs future future.apply generics ggplot2 ggrepel ggridges globals glue goftest gplots gridExtra gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph irlba isoband jquerylib jsonlite KernSmooth knitr labeling later lattice lazyeval lifecycle listenv lme4 lmtest magrittr MASS Matrix matrixStats memoise mime miniUI minqa neuralnet nlme nloptr openssl otel parallelly patchwork pbapply pbmcapply pillar pkgconfig plotly plyr png polyclip progressr promises purrr R6 RANN rappdirs rbibutils RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML Rdpack reformulas reshape2 reticulate RJSONIO rlang rmarkdown ROCR rprojroot RSpectra Rtsne S7 sass scales scattermore sctransform Seurat SeuratObject shiny sitmo sourcetools sp spam spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stringi stringr survival sys tensor tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite withr xfun xtable yaml zoo

Cell type classification with SignacX: CITE-seq PBMCs from 10X Genomics

Seurat | SignacX | Visualizations

Rendered fromsignac-Seurat_CITE-seq.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-25

SignacX, Seurat and MASC: Analysis of kidney cells from AMP

Load data | Seurat | SignacX | Visualizations | Run MASC

Rendered fromsignac-Seurat_AMP.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-04

Cell type classification with SignacX: 1k PBMCs from 10X Genomics

Seurat | SignacX | Visualizations

Rendered fromsignac-Seurat_pbmcs.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-25

Benchmarking SignacFast with single cell flow-sorted data

Load data | Seurat | SignacX

Rendered fromSignacFast-Seurat_AMP_RA.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-18

Benchmarking SignacX and SingleR with synovial flow cytometry data

Load data | SingleR | Seurat | SignacX | Compare SignacX and SingleR with FACs labels

Rendered fromsignac-Seurat_AMP_RA.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-11

Learning CD56 NK cells from CITE-seq PBMCs

Load data | SignacX | Classify a new data set with the model

Rendered fromsignac-SPRING_Learning.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-11

Mapping homologous gene symbols

Load the essential packages | Load Macaca fascicularis genes | Map homologous genes

Rendered fromCrabeating_vignette.Rmdusingknitr::rmarkdown_notangle

Last update: 2021-03-01
Started: 2021-02-10

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Normalized Shannon entropy-based "unclassified" assignment	CID.entropy
Computes distance matrix from edge list	CID.GetDistMat
Extracts unique elements	CID.IsUnique
Load data file from directory	CID.LoadData
Load edges from edge list for single cell network	CID.LoadEdges
Detects community substructure by Louvain community detection	CID.Louvain
Library size normalize	CID.Normalize
Smoothing function	CID.smooth
Writes JSON file for SPRING integration	CID.writeJSON
Generates cellular phenotype labels	GenerateLabels
Genes of interest for drug discovery / disease biology research	Genes_Of_Interest
Loads neural network models from GitHub	GetModels_HPCA
Loads bootstrapped HPCA training data from GitHub	GetTrainingData_HPCA
KNN-based imputation	KSoftImpute
Mixed effect modeling	MASC
Generates an ensemble of neural network models.	ModelGenerator
Save count_matrix.h5 files for SPRING integration	SaveCountsToH5
Classification of cellular phenotypes in single cell data	Signac
Generates bootstrapped single cell data	SignacBoot
Fast classification of cellular phenotypes	SignacFast