# Classifier for Single-cell RNA-seq Using Cell Clusters

## Documentation for package ‘clustifyr’ version 1.16.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D F G H I K M N O P Q R S V W

## -- 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