Classifier for Single-cell RNA-seq Using Cell Clusters

Documentation for package ‘clustifyr’ version 1.16.0

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-- A --

append_genes	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.
assess_rank_bias	Find rank bias
assign_ident	manually change idents as needed
average_clusters	Average expression values per cluster

-- B --

binarize_expr	Binarize scRNAseq data
build_atlas	Function to combine records into single atlas

-- C --

calculate_pathway_gsea	Convert expression matrix to GSEA pathway scores (would take a similar place in workflow before average_clusters/binarize)
calc_distance	Distance calculations for spatial coord
calc_similarity	compute similarity
call_consensus	get concensus calls for a list of cor calls
call_to_metadata	Insert called ident results into metadata
cbmc_m	reference marker matrix from seurat citeseq CBMC tutorial
cbmc_ref	reference matrix from seurat citeseq CBMC tutorial
check_raw_counts	Given a count matrix, determine if the matrix has been either log-normalized, normalized, or contains raw counts
clustify	Compare scRNA-seq data to reference data.
clustify.default	Compare scRNA-seq data to reference data.
clustify.Seurat	Compare scRNA-seq data to reference data.
clustify.SingleCellExperiment	Compare scRNA-seq data to reference data.
clustifyr_methods	Correlation functions available in clustifyr
clustify_lists	Main function to compare scRNA-seq data to gene lists.
clustify_lists.default	Main function to compare scRNA-seq data to gene lists.
clustify_lists.Seurat	Main function to compare scRNA-seq data to gene lists.
clustify_lists.SingleCellExperiment	Main function to compare scRNA-seq data to gene lists.
clustify_nudge	Combined function to compare scRNA-seq data to bulk RNA-seq data and marker list
clustify_nudge.default	Combined function to compare scRNA-seq data to bulk RNA-seq data and marker list
clustify_nudge.Seurat	Combined function to compare scRNA-seq data to bulk RNA-seq data and marker list
collapse_to_cluster	From per-cell calls, take highest freq call in each cluster
compare_lists	Calculate adjusted p-values for hypergeometric test of gene lists or jaccard index
cor_to_call	get best calls for each cluster
cor_to_call_rank	get ranked calls for each cluster
cor_to_call_topn	get top calls for each cluster
cosine	Cosine distance

-- D --

downrefs	table of references stored in clustifyrdata
downsample_matrix	downsample matrix by cluster or completely random

-- F --

feature_select_PCA	Returns a list of variable genes based on PCA
file_marker_parse	takes files with positive and negative markers, as described in garnett, and returns list of markers
find_rank_bias	Find rank bias

-- G --

gene_pct	pct of cells in each cluster that express genelist
gene_pct_markerm	pct of cells in every cluster that express a series of genelists
get_best_match_matrix	Function to make best call from correlation matrix
get_best_str	Function to make call and attach score
get_common_elements	Find entries shared in all vectors
get_similarity	Compute similarity of matrices
get_ucsc_reference	Build reference atlases from external UCSC cellbrowsers
get_unique_column	Generate a unique column id for a dataframe
get_vargenes	Generate variable gene list from marker matrix
gmt_to_list	convert gmt format of pathways to list of vectors

-- H --

human_genes_10x

Vector of human genes for 10x cellranger pipeline

-- I --

insert_meta_object

more flexible metadata update of single cell objects

-- K --

kl_divergence

KL divergence

-- M --

make_comb_ref	make combination ref matrix to assess intermixing
marker_select	decide for one gene whether it is a marker for a certain cell type
matrixize_markers	Convert candidate genes list into matrix
mouse_genes_10x	Vector of mouse genes for 10x cellranger pipeline

-- N --

not_pretty_palette

black and white palette for plotting continous variables

-- O --

object_data	Function to access object data
object_data.Seurat	Function to access object data
object_data.SingleCellExperiment	Function to access object data
object_loc_lookup	lookup table for single cell object structures
object_ref	Function to convert labelled object to avg expression matrix
object_ref.default	Function to convert labelled object to avg expression matrix
object_ref.Seurat	Function to convert labelled object to avg expression matrix
object_ref.SingleCellExperiment	Function to convert labelled object to avg expression matrix
overcluster	Overcluster by kmeans per cluster
overcluster_test	compare clustering parameters and classification outcomes

-- P --

parse_loc_object	more flexible parsing of single cell objects
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.
percent_clusters	Percentage detected per cluster
permute_similarity	Compute a p-value for similarity using permutation
plot_best_call	Plot best calls for each cluster on a tSNE or umap
plot_call	Plot called clusters on a tSNE or umap, for each reference cluster given
plot_cor	Plot similarity measures on a tSNE or umap
plot_cor_heatmap	Plot similarity measures on heatmap
plot_dims	Plot a tSNE or umap colored by feature.
plot_gene	Plot gene expression on to tSNE or umap
plot_pathway_gsea	plot GSEA pathway scores as heatmap, returns a list containing results and plot.
plot_rank_bias	Query rank bias results
pos_neg_marker	generate pos and negative marker expression matrix from a list/dataframe of positive markers
pos_neg_select	adapt clustify to tweak score for pos and neg markers
pretty_palette	Color palette for plotting continous variables
pretty_palette2	Color palette for plotting continous variables, starting at gray
pretty_palette_ramp_d	Expanded color palette ramp for plotting discrete variables

-- Q --

query_rank_bias

Query rank bias results

-- R --

ref_feature_select	feature select from reference matrix
ref_marker_select	marker selection from reference matrix
reverse_marker_matrix	generate negative markers from a list of exclusive positive markers
run_clustifyr_app	Launch Shiny app version of clustifyr, may need to run install_clustifyr_app() at first time to install packages
run_gsea	Run GSEA to compare a gene list(s) to per cell or per cluster expression data

-- S --

sce_pbmc	An example SingleCellExperiment object
seurat_meta	Function to convert labelled seurat object to fully prepared metadata
seurat_meta.Seurat	Function to convert labelled seurat object to fully prepared metadata
seurat_ref	Function to convert labelled seurat object to avg expression matrix
seurat_ref.Seurat	Function to convert labelled seurat object to avg expression matrix
so_pbmc	An example Seurat object

-- V --

vector_similarity

Compute similarity between two vectors

-- W --

write_meta	Function to write metadata to object
write_meta.Seurat	Function to write metadata to object
write_meta.SingleCellExperiment	Function to write metadata to object