Package: cellity 1.35.0

Tomislav Ilicic

cellity: Quality Control for Single-Cell RNA-seq Data

A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets.

Authors:Tomislav Illicic, Davis McCarthy

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NEWS

# Install 'cellity' in R:
install.packages('cellity', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • extra_human_genes - Additional human genes that are used in feature extraction
  • extra_mouse_genes - Additional mouse genes that are used in feature extraction
  • feature_info - Information which genes and GO categories should be included as features. Also defines which features are cell-type independent
  • mES1_features - Real test dataset containing all and common features from the paper
  • mES1_labels - Real test dataset containing annotation of cells
  • param_mES_all - Parameters used for SVM classification
  • param_mES_common - Parameters used for SVM classification
  • sample_counts - Sample gene expression data containing 40 cells
  • sample_stats - Sample read statistics data containing 40 cells
  • training_mES_features - Original training dataset containing all and common features from the paper
  • training_mES_labels - Original training dataset containing annotation of cells

On BioConductor:cellity-1.35.0(bioc 3.21)cellity-1.34.0(bioc 3.20)

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

immunooncologyrnaseqqualitycontrolpreprocessingnormalizationvisualizationdimensionreductiontranscriptomicsgeneexpressionsequencingsoftwaresupportvectormachine

4.00 score 9 scripts 202 downloads 4 exports 74 dependencies

Last updated 26 days agofrom:614b66cb40. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 24 2024
R-4.5-winOKNov 24 2024
R-4.5-linuxOKNov 24 2024
R-4.4-winOKNov 24 2024
R-4.4-macOKNov 24 2024
R-4.3-winOKNov 24 2024
R-4.3-macOKNov 24 2024

Exports:assess_cell_quality_PCAassess_cell_quality_SVMextract_featuresnormalise_by_factor

Dependencies:AnnotationDbiaskpassBiobaseBiocGenericsBiostringsbitbit64blobcachemclassclicolorspacecpp11crayoncurlDBIDEoptimRe1071fansifarverfastmapgenericsGenomeInfoDbGenomeInfoDbDataggplot2glueGO.dbgraphgtablehttrIRangesisobandjsonliteKEGGRESTlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemunsellmvoutliernlmeopensslorg.Hs.eg.dborg.Mm.eg.dbpillarpkgconfigplogrpngproxyR6RColorBrewerrlangrobustbaseRSQLiteS4VectorsscalessgeostatSparseMsystibbletopGOUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

Introduction to cellity: Classification of low quality cells in scRNA-seq data using R

Tomislav Ilicic & Davis McCarthy

Rendered fromcellity_vignette.Rmdusingknitr::rmarkdownon Nov 24 2024.

Last update: 2018-09-06
Started: 2016-02-25

Readme and manuals

Help Manual

Help pageTopics
Quality Control for Single-Cell RNA-seq Datacellity-package
ASSESS CELL QUALITY USING PCA AND OUTLIER DETECTIONassess_cell_quality_PCA
Assess quality of a cell - SVM versionassess_cell_quality_SVM
Additional human genes that are used in feature extractionextra_human_genes
Additional mouse genes that are used in feature extractionextra_mouse_genes
Extracts biological and technical features for given datasetextract_features
Helper Function to create all featuresfeature_generation
Information which genes and GO categories should be included as features. Also defines which features are cell-type independent (common features)feature_info
Real test dataset containing all and common features from the paper (mES1)mES1_features
Real test dataset containing annotation of cellsmES1_labels
Internal multiplot function to combine plots onto a gridmultiplot
Internal function to normalize by library sizenormalise_by_factor
Parameters used for SVM classificationparam_mES_all
Parameters used for SVM classificationparam_mES_common
Plots PCA of all features. Colors high and low quality cells based on outlier detection.plot_pca
Sample gene expression data containing 40 cellssample_counts
Sample read statistics data containing 40 cellssample_stats
Converts all first letters to capital letterssimple_cap
Sums up normalised values of genes to groups.sum_prop
Original training dataset containing all and common features from the paper (training mES)training_mES_features
Original training dataset containing annotation of cellstraining_mES_labels
Internal function to detect outliers from the mvoultier pacakge Modified slightly so that plots are not printeduni.plot