Package: clustifyr 1.19.0

Rui Fu

clustifyr: Classifier for Single-cell RNA-seq Using Cell Clusters

Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.

Authors:Rui Fu [cre, aut], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd, cph]

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clustifyr.pdf |clustifyr.html✨
clustifyr/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/rnabioco/clustifyr/issues

Datasets:
  • cbmc_m - Reference marker matrix from seurat citeseq CBMC tutorial
  • cbmc_ref - Reference matrix from seurat citeseq CBMC tutorial
  • downrefs - Table of references stored in clustifyrdata
  • human_genes_10x - Vector of human genes for 10x cellranger pipeline
  • mouse_genes_10x - Vector of mouse genes for 10x cellranger pipeline
  • object_loc_lookup - Lookup table for single cell object structures
  • pbmc_markers - Marker genes identified by Seurat from single-cell RNA-seq PBMCs.
  • pbmc_markers_M3Drop - Marker genes identified by M3Drop from single-cell RNA-seq PBMCs.
  • pbmc_matrix_small - Matrix of single-cell RNA-seq PBMCs.
  • pbmc_meta - Meta-data for single-cell RNA-seq PBMCs.
  • pbmc_vargenes - Variable genes identified by Seurat from single-cell RNA-seq PBMCs.

On BioConductor:clustifyr-1.17.2(bioc 3.20)clustifyr-1.16.2(bioc 3.19)

singlecellannotationsequencingmicroarraygeneexpressionassign-identitiesclustersmarker-genesrna-seqsingle-cell-rna-seq

9.35 score 113 stars 281 scripts 282 downloads 56 exports 89 dependencies

Last updated 23 days agofrom:01ae82e767. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winNOTEOct 30 2024
R-4.5-linuxNOTEOct 30 2024
R-4.4-winNOTEOct 30 2024
R-4.4-macNOTEOct 30 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 30 2024

Exports:append_genesassess_rank_biasaverage_clustersbinarize_exprbuild_atlascalc_distancecalculate_pathway_gseacall_consensuscall_to_metadatacheck_raw_countsclustifyclustify_listsclustify_nudgeclustifyr_methodscollapse_to_clustercompare_listscor_to_callcor_to_call_rankcor_to_call_topndownsample_matrixfeature_select_PCAfile_marker_parsefind_rank_biasgene_pct_markermget_ucsc_referenceget_vargenesgmt_to_listinsert_meta_objectmake_comb_refmarker_selectmatrixize_markersobject_dataobject_refoverclusterovercluster_testparse_loc_objectplot_best_callplot_corplot_cor_heatmapplot_dimsplot_geneplot_pathway_gseaplot_rank_biaspos_neg_markerpos_neg_selectquery_rank_biasref_feature_selectref_marker_selectreverse_marker_matrixrun_clustifyr_apprun_gseasce_pbmcseurat_metaseurat_refso_pbmcwrite_meta

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocParallelclicodetoolscolorspacecowplotcpp11crayoncurldata.tableDelayedArraydigestdotCall64dplyrentropyfansifarverfastmatchfgseaformatRfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2globalsgluegtablehttrIRangesisobandjsonlitelabelinglambda.rlatticelifecyclelistenvmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslparallellypillarpkgconfigprogressrproxypurrrR6RColorBrewerRcppRcppEigenrlangS4ArraysS4VectorsscalesSeuratObjectSingleCellExperimentsnowspspamSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

Improving NCBI GEO submissions of scRNA-seq data

Rendered fromgeo-annotations.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-08-23
Started: 2020-05-22

Introduction to clustifyr

Rui Fu, Austin Gillen, Ryan Sheridan, Chengzhe Tian, Michelle Daya, Yue Hao, Jay Hesselberth, Kent Riemondy

Rendered fromclustifyr.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-08-23
Started: 2022-10-04

Readme and manuals

Help Manual

Help pageTopics
Given a reference matrix and a list of genes, take the union of all genes in vector and genes in reference matrix and insert zero counts for all remaining genes.append_genes
Find rank biasassess_rank_bias
manually change idents as neededassign_ident
Average expression values per clusteraverage_clusters
Binarize scRNAseq databinarize_expr
Function to combine records into single atlasbuild_atlas
Distance calculations for spatial coordcalc_distance
compute similaritycalc_similarity
Convert expression matrix to GSEA pathway scores (would take a similar place in workflow before average_clusters/binarize)calculate_pathway_gsea
get concensus calls for a list of cor callscall_consensus
Insert called ident results into metadatacall_to_metadata
reference marker matrix from seurat citeseq CBMC tutorialcbmc_m
reference matrix from seurat citeseq CBMC tutorialcbmc_ref
Given a count matrix, determine if the matrix has been either log-normalized, normalized, or contains raw countscheck_raw_counts
Compare scRNA-seq data to reference data.clustify clustify.default clustify.Seurat clustify.SingleCellExperiment
Main function to compare scRNA-seq data to gene lists.clustify_lists clustify_lists.default clustify_lists.Seurat clustify_lists.SingleCellExperiment
Combined function to compare scRNA-seq data to bulk RNA-seq data and marker listclustify_nudge clustify_nudge.default clustify_nudge.Seurat
Correlation functions available in clustifyrclustifyr_methods
From per-cell calls, take highest freq call in each clustercollapse_to_cluster
Calculate adjusted p-values for hypergeometric test of gene lists or jaccard indexcompare_lists
get best calls for each clustercor_to_call
get ranked calls for each clustercor_to_call_rank
get top calls for each clustercor_to_call_topn
Cosine distancecosine
table of references stored in clustifyrdatadownrefs
downsample matrix by cluster or completely randomdownsample_matrix
Returns a list of variable genes based on PCAfeature_select_PCA
takes files with positive and negative markers, as described in garnett, and returns list of markersfile_marker_parse
Find rank biasfind_rank_bias
pct of cells in each cluster that express genelistgene_pct
pct of cells in every cluster that express a series of genelistsgene_pct_markerm
Function to make best call from correlation matrixget_best_match_matrix
Function to make call and attach scoreget_best_str
Find entries shared in all vectorsget_common_elements
Compute similarity of matricesget_similarity
Build reference atlases from external UCSC cellbrowsersget_ucsc_reference
Generate a unique column id for a dataframeget_unique_column
Generate variable gene list from marker matrixget_vargenes
convert gmt format of pathways to list of vectorsgmt_to_list
Vector of human genes for 10x cellranger pipelinehuman_genes_10x
more flexible metadata update of single cell objectsinsert_meta_object
KL divergencekl_divergence
make combination ref matrix to assess intermixingmake_comb_ref
decide for one gene whether it is a marker for a certain cell typemarker_select
Convert candidate genes list into matrixmatrixize_markers
Vector of mouse genes for 10x cellranger pipelinemouse_genes_10x
black and white palette for plotting continous variablesnot_pretty_palette
Function to access object dataobject_data object_data.Seurat object_data.SingleCellExperiment
lookup table for single cell object structuresobject_loc_lookup
Function to convert labelled object to avg expression matrixobject_ref object_ref.default object_ref.Seurat object_ref.SingleCellExperiment
Overcluster by kmeans per clusterovercluster
compare clustering parameters and classification outcomesovercluster_test
more flexible parsing of single cell objectsparse_loc_object
Marker genes identified by Seurat from single-cell RNA-seq PBMCs.pbmc_markers
Marker genes identified by M3Drop from single-cell RNA-seq PBMCs.pbmc_markers_M3Drop
Matrix of single-cell RNA-seq PBMCs.pbmc_matrix_small
Meta-data for single-cell RNA-seq PBMCs.pbmc_meta
Variable genes identified by Seurat from single-cell RNA-seq PBMCs.pbmc_vargenes
Percentage detected per clusterpercent_clusters
Compute a p-value for similarity using permutationpermute_similarity
Plot best calls for each cluster on a tSNE or umapplot_best_call
Plot called clusters on a tSNE or umap, for each reference cluster givenplot_call
Plot similarity measures on a tSNE or umapplot_cor
Plot similarity measures on heatmapplot_cor_heatmap
Plot a tSNE or umap colored by feature.plot_dims
Plot gene expression on to tSNE or umapplot_gene
plot GSEA pathway scores as heatmap, returns a list containing results and plot.plot_pathway_gsea
Query rank bias resultsplot_rank_bias
generate pos and negative marker expression matrix from a list/dataframe of positive markerspos_neg_marker
adapt clustify to tweak score for pos and neg markerspos_neg_select
Color palette for plotting continous variablespretty_palette
Expanded color palette ramp for plotting discrete variablespretty_palette_ramp_d
Color palette for plotting continous variables, starting at graypretty_palette2
Query rank bias resultsquery_rank_bias
feature select from reference matrixref_feature_select
marker selection from reference matrixref_marker_select
generate negative markers from a list of exclusive positive markersreverse_marker_matrix
Launch Shiny app version of clustifyr, may need to run install_clustifyr_app() at first time to install packagesrun_clustifyr_app
Run GSEA to compare a gene list(s) to per cell or per cluster expression datarun_gsea
An example SingleCellExperiment objectsce_pbmc
Function to convert labelled seurat object to fully prepared metadataseurat_meta seurat_meta.Seurat
Function to convert labelled seurat object to avg expression matrixseurat_ref seurat_ref.Seurat
An example Seurat objectso_pbmc
Compute similarity between two vectorsvector_similarity
Function to write metadata to objectwrite_meta write_meta.Seurat write_meta.SingleCellExperiment