Package index • clustifyr

Main functions

Classify cell clusters by transcriptome profiles or gene lists, and building references

clustify(): Compare scRNA-seq data to reference data.

clustify_lists(): 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

clustifyr_methods: Correlation functions available in clustifyr

average_clusters(): Average expression values per cluster

matrixize_markers(): Convert candidate genes list into matrix

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

call_to_metadata(): Insert called ident results into metadata

call_consensus(): get concensus calls for a list of cor calls

Object wrappers

Retrieving metadata and reference building from supported object types

sce_pbmc(): An example SingleCellExperiment object

so_pbmc(): An example Seurat object

seurat_meta(): Function to convert labelled seurat object to fully prepared metadata

seurat_ref(): Function to convert labelled seurat object to avg expression matrix

object_ref(): Function to convert labelled object to avg expression matrix

object_loc_lookup(): lookup table for single cell object structures

Plotting

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

Data sets

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

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.

Shiny

run_clustifyr_app(): Launch Shiny app version of clustifyr, may need to run install_clustifyr_app() at first time to install packages

Utilities

run_gsea(): Run GSEA to compare a gene list(s) to per cell or per cluster expression data

calculate_pathway_gsea(): Convert expression matrix to GSEA pathway scores (would take a similar place in workflow before average_clusters/binarize)

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

assess_rank_bias(): Find rank bias

binarize_expr(): Binarize scRNAseq data

downsample_matrix(): downsample matrix by cluster or completely random

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

gmt_to_list(): convert gmt format of pathways to list of vectors

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

overcluster_test(): compare clustering parameters and classification outcomes

parse_loc_object(): more flexible parsing of single cell objects

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

query_rank_bias(): Query rank bias results

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

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.

assign_ident(): manually change idents as needed

build_atlas(): Function to combine records into single atlas

calc_similarity(): compute similarity

calc_distance(): Distance calculations for spatial coord

check_raw_counts(): Given a count matrix, determine if the matrix has been either log-normalized, normalized, or contains raw counts

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

cosine(): Cosine distance

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

insert_meta_object(): more flexible metadata update of single cell objects

kl_divergence(): KL divergence

not_pretty_palette: black and white palette for plotting continous variables

object_data(): Function to access object data

overcluster(): Overcluster by kmeans per cluster

percent_clusters(): Percentage detected per cluster

permute_similarity(): Compute a p-value for similarity using permutation

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

vector_similarity(): Compute similarity between two vectors

write_meta(): Function to write metadata to object