Package 'clustifyr'

Title: Classifier for Single-cell RNA-seq Using Cell Clusters
Description: 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 [aut], Kent Riemondy [cre, aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd, cph]
Maintainer: Kent Riemondy <[email protected]>
License: MIT + file LICENSE
Version: 1.17.0
Built: 2024-06-30 05:44:36 UTC
Source: https://github.com/bioc/clustifyr

Help Index

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.

Description

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.

Usage

append_genes(gene_vector, ref_matrix)

Arguments

gene_vector

char vector with gene names
ref_matrix

Reference matrix containing cell types vs. gene expression values

Value

Reference matrix with union of all genes

Examples

mat <- append_genes(
    gene_vector = human_genes_10x,
    ref_matrix = cbmc_ref
)

Find rank bias

Description

Find rank bias

Usage

assess_rank_bias(
  avg_mat,
  ref_mat,
  query_genes = NULL,
  res,
  organism,
  plot_name = NULL,
  rds_name = NULL,
  expand_unassigned = FALSE
)

Arguments

avg_mat

average expression matrix
ref_mat

reference expression matrix
query_genes

original vector of genes used to clustify
res

dataframe of idents, such as output of cor_to_call
organism

for GO term analysis, organism name: human - 'hsapiens', mouse - 'mmusculus'
plot_name

name for saved pdf, if NULL then no file is written (default)
rds_name

name for saved rds of rank_diff, if NULL then no file is written (default)
expand_unassigned

test all ref clusters for unassigned results

Value

pdf of ggplot object

Examples

## Not run: 
avg <- average_clusters(
    pbmc_matrix_small, 
    pbmc_meta$seurat_clusters
)
res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "seurat_clusters"
)
top_call <- cor_to_call(
    res,
    metadata = pbmc_meta,
    cluster_col = "seurat_clusters",
    collapse_to_cluster = FALSE,
    threshold = 0.8
)
res_rank <- assess_rank_bias(
    avg, 
    cbmc_ref, 
    res = top_call
)

## End(Not run)

manually change idents as needed

Description

manually change idents as needed

Usage

assign_ident(
  metadata,
  cluster_col = "cluster",
  ident_col = "type",
  clusters,
  idents
)

Arguments

metadata

column of ident
cluster_col

column in metadata containing cluster info
ident_col

column in metadata containing identity assignment
clusters

names of clusters to change, string or vector of strings
idents

new idents to assign, must be length of 1 or same as clusters

Value

new dataframe of metadata

Average expression values per cluster

Description

Average expression values per cluster

Usage

average_clusters(
  mat,
  metadata,
  cluster_col = "cluster",
  if_log = TRUE,
  cell_col = NULL,
  low_threshold = 0,
  method = "mean",
  output_log = TRUE,
  subclusterpower = 0,
  cut_n = NULL
)

Arguments

mat

expression matrix
metadata

data.frame or vector containing cluster assignments per cell. Order must match column order in supplied matrix. If a data.frame provide the cluster_col parameters.
cluster_col

column in metadata with cluster number
if_log

input data is natural log, averaging will be done on unlogged data
cell_col

if provided, will reorder matrix first
low_threshold

option to remove clusters with too few cells
method

whether to take mean (default), median, 10% truncated mean, or trimean, max, min
output_log

whether to report log results
subclusterpower

whether to get multiple averages per original cluster
cut_n

set on a limit of genes as expressed, lower ranked genes are set to 0, considered unexpressed

Value

average expression matrix, with genes for row names, and clusters for column names

Examples

mat <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    if_log = FALSE
)
mat[1:3, 1:3]

Binarize scRNAseq data

Description

Binarize scRNAseq data

Usage

binarize_expr(mat, n = 1000, cut = 0)

Arguments

mat

single-cell expression matrix
n

number of top expressing genes to keep
cut

cut off to set to 0

Value

matrix of 1s and 0s

Examples

pbmc_avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified"
)

mat <- binarize_expr(pbmc_avg)
mat[1:3, 1:3]

Function to combine records into single atlas

Description

Function to combine records into single atlas

Usage

build_atlas(matrix_fns = NULL, genes_fn, matrix_objs = NULL, output_fn = NULL)

Arguments

matrix_fns

character vector of paths to study matrices stored as .rds files. If a named character vector, then the name will be added as a suffix to the cell type name in the final matrix. If it is not named, then the filename will be used (without .rds)
genes_fn

text file with a single column containing genes and the ordering desired in the output matrix
matrix_objs

Checks to see whether .rds files will be read or R objects in a local environment. A list of environmental objects can be passed to matrx_objs, and that names will be used, otherwise defaults to numbers
output_fn

output filename for .rds file. If NULL the matrix will be returned instead of saving

Value

Combined matrix with all genes given

Examples

pbmc_ref_matrix <- average_clusters(
mat = pbmc_matrix_small,
metadata = pbmc_meta,
cluster_col = "classified",
if_log = TRUE # whether the expression matrix is already log transformed
)
references_to_combine <- list(pbmc_ref_matrix, cbmc_ref)
atlas <- build_atlas(NULL, human_genes_10x, references_to_combine, NULL)

Distance calculations for spatial coord

Description

Distance calculations for spatial coord

Usage

calc_distance(
  coord,
  metadata,
  cluster_col = "cluster",
  collapse_to_cluster = FALSE
)

Arguments

coord

dataframe or matrix of spatial coordinates, cell barcode as rownames
metadata

data.frame or vector containing cluster assignments per cell. Order must match column order in supplied matrix. If a data.frame provide the cluster_col parameters.
cluster_col

column in metadata with cluster number
collapse_to_cluster

instead of reporting min distance to cluster per cell, summarize to cluster level

Value

min distance matrix

Examples

cbs <- paste0("cb_", 1:100)

spatial_coords <- data.frame(row.names = cbs,
                             X = runif(100),
                             Y = runif(100))
group_ids <- sample(c("A", "B"), 100, replace = TRUE)
dist_res <- calc_distance(
    spatial_coords, 
    group_ids
)

compute similarity

Description

compute similarity

Usage

calc_similarity(query_mat, ref_mat, compute_method, rm0 = FALSE, ...)

Arguments

query_mat

query data matrix
ref_mat

reference data matrix
compute_method

method(s) for computing similarity scores
rm0

consider 0 as missing data, recommended for per_cell
...

additional parameters

Value

matrix of numeric values

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

Description

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

Usage

calculate_pathway_gsea(
  mat,
  pathway_list,
  n_perm = 1000,
  scale = TRUE,
  no_warnings = TRUE
)

Arguments

mat

expression matrix
pathway_list

a list of vectors, each named for a specific pathway, or dataframe
n_perm

Number of permutation for fgsea function. Defaults to 1000.
scale

convert expr_mat into zscores prior to running GSEA?, default = FALSE
no_warnings

suppress warnings from gsea ties

Value

matrix of GSEA NES values, cell types as row names, pathways as column names

Examples

gl <- list(
    "n" = c("PPBP", "LYZ", "S100A9"),
    "a" = c("IGLL5", "GNLY", "FTL")
)

pbmc_avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified"
)

calculate_pathway_gsea(
    mat = pbmc_avg,
    pathway_list = gl
)

get concensus calls for a list of cor calls

Description

get concensus calls for a list of cor calls

Usage

call_consensus(list_of_res)

Arguments

list_of_res

list of call dataframes from cor_to_call_rank

Value

dataframe of cluster, new ident, and mean rank

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    ref_mat = cbmc_ref
)

res2 <- cor_to_call_rank(res, threshold = "auto")
res3 <- cor_to_call_rank(res)
call_consensus(list(res2, res3))

Insert called ident results into metadata

Description

Insert called ident results into metadata

Usage

call_to_metadata(
  res,
  metadata,
  cluster_col,
  per_cell = FALSE,
  rename_prefix = NULL
)

Arguments

res

dataframe of idents, such as output of cor_to_call
metadata

input metadata with tsne or umap coordinates and cluster ids
cluster_col

metadata column, can be cluster or cellid
per_cell

whether the res dataframe is listed per cell
rename_prefix

prefix to add to type and r column names

Value

new metadata with added columns

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    ref_mat = cbmc_ref
)

res2 <- cor_to_call(res, cluster_col = "classified")

call_to_metadata(
    res = res2,
    metadata = pbmc_meta,
    cluster_col = "classified",
    rename_prefix = "assigned"
)

reference marker matrix from seurat citeseq CBMC tutorial

Description

reference marker matrix from seurat citeseq CBMC tutorial

Usage

cbmc_m

Format

An object of class data.frame with 3 rows and 13 columns.

Source

https://satijalab.org/seurat/v3.0/multimodal_vignette.html#identify-differentially-expressed-proteins-between-clusters

See Also

Other data: cbmc_ref, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

reference matrix from seurat citeseq CBMC tutorial

Description

reference matrix from seurat citeseq CBMC tutorial

Usage

cbmc_ref

Format

An object of class matrix (inherits from array) with 2000 rows and 13 columns.

Source

https://satijalab.org/seurat/v3.0/multimodal_vignette.html#identify-differentially-expressed-proteins-between-clusters

See Also

Other data: cbmc_m, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

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

Description

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

Usage

check_raw_counts(counts_matrix, max_log_value = 50)

Arguments

counts_matrix

Count matrix containing scRNA-seq read data
max_log_value

Static value to determine if a matrix is normalized

Value

String either raw counts, log-normalized or normalized

Examples

check_raw_counts(pbmc_matrix_small)

Compare scRNA-seq data to reference data.

Description

Compare scRNA-seq data to reference data.

Usage

clustify(input, ...)

## Default S3 method:
clustify(
  input,
  ref_mat,
  metadata = NULL,
  cluster_col = NULL,
  query_genes = NULL,
  n_genes = 1000,
  per_cell = FALSE,
  n_perm = 0,
  compute_method = "spearman",
  pseudobulk_method = "mean",
  verbose = TRUE,
  lookuptable = NULL,
  rm0 = FALSE,
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  rename_prefix = NULL,
  threshold = "auto",
  low_threshold_cell = 0,
  exclude_genes = c(),
  if_log = TRUE,
  organism = "hsapiens",
  plot_name = NULL,
  rds_name = NULL,
  expand_unassigned = FALSE,
  ...
)

## S3 method for class 'Seurat'
clustify(
  input,
  ref_mat,
  cluster_col = NULL,
  query_genes = NULL,
  n_genes = 1000,
  per_cell = FALSE,
  n_perm = 0,
  compute_method = "spearman",
  pseudobulk_method = "mean",
  use_var_genes = TRUE,
  dr = "umap",
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  threshold = "auto",
  verbose = TRUE,
  rm0 = FALSE,
  rename_prefix = NULL,
  exclude_genes = c(),
  metadata = NULL,
  organism = "hsapiens",
  plot_name = NULL,
  rds_name = NULL,
  expand_unassigned = FALSE,
  ...
)

## S3 method for class 'SingleCellExperiment'
clustify(
  input,
  ref_mat,
  cluster_col = NULL,
  query_genes = NULL,
  per_cell = FALSE,
  n_perm = 0,
  compute_method = "spearman",
  pseudobulk_method = "mean",
  use_var_genes = TRUE,
  dr = "umap",
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  threshold = "auto",
  verbose = TRUE,
  rm0 = FALSE,
  rename_prefix = NULL,
  exclude_genes = c(),
  metadata = NULL,
  organism = "hsapiens",
  plot_name = NULL,
  rds_name = NULL,
  expand_unassigned = FALSE,
  ...
)

Arguments

input

single-cell expression matrix or Seurat object
...

additional arguments to pass to compute_method function
ref_mat

reference expression matrix
metadata

cell cluster assignments, supplied as a vector or data.frame. If data.frame is supplied then cluster_col needs to be set. Not required if running correlation per cell.
cluster_col

column in metadata that contains cluster ids per cell. Will default to first column of metadata if not supplied. Not required if running correlation per cell.
query_genes

A vector of genes of interest to compare. If NULL, then common genes between the expr_mat and ref_mat will be used for comparision.
n_genes

number of genes limit for Seurat variable genes, by default 1000, set to 0 to use all variable genes (generally not recommended)
per_cell

if true run per cell, otherwise per cluster.
n_perm

number of permutations, set to 0 by default
compute_method

method(s) for computing similarity scores
pseudobulk_method

method used for summarizing clusters, options are mean (default), median, truncate (10% truncated mean), or trimean, max, min
verbose

whether to report certain variables chosen and steps
lookuptable

if not supplied, will look in built-in table for object parsing
rm0

consider 0 as missing data, recommended for per_cell
obj_out

whether to output object instead of cor matrix
seurat_out

output cor matrix or called seurat object (deprecated, use obj_out instead)
vec_out

only output a result vector in the same order as metadata
rename_prefix

prefix to add to type and r column names
threshold

identity calling minimum correlation score threshold, only used when obj_out = TRUE
low_threshold_cell

option to remove clusters with too few cells
exclude_genes

a vector of gene names to throw out of query
if_log

input data is natural log, averaging will be done on unlogged data
organism

for GO term analysis, organism name: human - 'hsapiens', mouse - 'mmusculus'
plot_name

name for saved pdf, if NULL then no file is written (default)
rds_name

name for saved rds of rank_diff, if NULL then no file is written (default)
expand_unassigned

test all ref clusters for unassigned results
use_var_genes

if providing a seurat object, use the variable genes (stored in [email protected]) as the query_genes.
dr

stored dimension reduction

Value

single cell object with identity assigned in metadata, or matrix of correlation values, clusters from input as row names, cell types from ref_mat as column names

Examples

# Annotate a matrix and metadata
clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "RNA_snn_res.0.5",
    verbose = TRUE
)

# Annotate using a different method
clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "RNA_snn_res.0.5",
    compute_method = "cosine"
)

# Annotate a SingleCellExperiment object
sce <- sce_pbmc()
clustify(
    sce,
    cbmc_ref,
    cluster_col = "clusters",
    obj_out = TRUE,
    per_cell = FALSE,
    dr = "umap"
)

# Annotate a Seurat object
so <- so_pbmc()
clustify(
    so,
    cbmc_ref,
    cluster_col = "seurat_clusters",
    obj_out = TRUE,
    per_cell = FALSE,
    dr = "umap"
)

# Annotate (and return) a Seurat object per-cell
clustify(
    input = so,
    ref_mat = cbmc_ref,
    cluster_col = "seurat_clusters",
    obj_out = TRUE,
    per_cell = TRUE,
    dr = "umap"
)

Main function to compare scRNA-seq data to gene lists.

Description

Main function to compare scRNA-seq data to gene lists.

Usage

clustify_lists(input, ...)

## Default S3 method:
clustify_lists(
  input,
  marker,
  marker_inmatrix = TRUE,
  metadata = NULL,
  cluster_col = NULL,
  if_log = TRUE,
  per_cell = FALSE,
  topn = 800,
  cut = 0,
  genome_n = 30000,
  metric = "hyper",
  output_high = TRUE,
  lookuptable = NULL,
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  rename_prefix = NULL,
  threshold = 0,
  low_threshold_cell = 0,
  verbose = TRUE,
  input_markers = FALSE,
  details_out = FALSE,
  ...
)

## S3 method for class 'Seurat'
clustify_lists(
  input,
  metadata = NULL,
  cluster_col = NULL,
  if_log = TRUE,
  per_cell = FALSE,
  topn = 800,
  cut = 0,
  marker,
  marker_inmatrix = TRUE,
  genome_n = 30000,
  metric = "hyper",
  output_high = TRUE,
  dr = "umap",
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  verbose = TRUE,
  details_out = FALSE,
  ...
)

## S3 method for class 'SingleCellExperiment'
clustify_lists(
  input,
  metadata = NULL,
  cluster_col = NULL,
  if_log = TRUE,
  per_cell = FALSE,
  topn = 800,
  cut = 0,
  marker,
  marker_inmatrix = TRUE,
  genome_n = 30000,
  metric = "hyper",
  output_high = TRUE,
  dr = "umap",
  obj_out = TRUE,
  seurat_out = obj_out,
  vec_out = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  verbose = TRUE,
  details_out = FALSE,
  ...
)

Arguments

input

single-cell expression matrix, Seurat object, or SingleCellExperiment
...

passed to matrixize_markers
marker

matrix or dataframe of candidate genes for each cluster
marker_inmatrix

whether markers genes are already in preprocessed matrix form
metadata

cell cluster assignments, supplied as a vector or data.frame. If data.frame is supplied then cluster_col needs to be set. Not required if running correlation per cell.
cluster_col

column in metadata with cluster number
if_log

input data is natural log, averaging will be done on unlogged data
per_cell

compare per cell or per cluster
topn

number of top expressing genes to keep from input matrix
cut

expression cut off from input matrix
genome_n

number of genes in the genome
metric

adjusted p-value for hypergeometric test, or jaccard index
output_high

if true (by default to fit with rest of package), -log10 transform p-value
lookuptable

if not supplied, will look in built-in table for object parsing
obj_out

whether to output object instead of cor matrix
seurat_out

output cor matrix or called seurat object (deprecated, use obj_out instead)
vec_out

only output a result vector in the same order as metadata
rename_prefix

prefix to add to type and r column names
threshold

identity calling minimum correlation score threshold, only used when obj_out = T
low_threshold_cell

option to remove clusters with too few cells
verbose

whether to report certain variables chosen and steps
input_markers

whether input is marker data.frame of 0 and 1s (output of pos_neg_marker), and uses alternate enrichment mode
details_out

whether to also output shared gene list from jaccard
dr

stored dimension reduction

Value

matrix of numeric values, clusters from input as row names, cell types from marker_mat as column names

Examples

# Annotate a matrix and metadata

# Annotate using a different method
clustify_lists(
    input = pbmc_matrix_small,
    marker = cbmc_m,
    metadata = pbmc_meta,
    cluster_col = "classified",
    verbose = TRUE,
    metric = "jaccard"
)

Combined function to compare scRNA-seq data to bulk RNA-seq data and marker list

Description

Combined function to compare scRNA-seq data to bulk RNA-seq data and marker list

Usage

clustify_nudge(input, ...)

## Default S3 method:
clustify_nudge(
  input,
  ref_mat,
  marker,
  metadata = NULL,
  cluster_col = NULL,
  query_genes = NULL,
  compute_method = "spearman",
  weight = 1,
  threshold = -Inf,
  dr = "umap",
  norm = "diff",
  call = TRUE,
  marker_inmatrix = TRUE,
  mode = "rank",
  obj_out = FALSE,
  seurat_out = obj_out,
  rename_prefix = NULL,
  lookuptable = NULL,
  ...
)

## S3 method for class 'Seurat'
clustify_nudge(
  input,
  ref_mat,
  marker,
  cluster_col = NULL,
  query_genes = NULL,
  compute_method = "spearman",
  weight = 1,
  obj_out = TRUE,
  seurat_out = obj_out,
  threshold = -Inf,
  dr = "umap",
  norm = "diff",
  marker_inmatrix = TRUE,
  mode = "rank",
  rename_prefix = NULL,
  ...
)

Arguments

input

express matrix or object
...

passed to matrixize_markers
ref_mat

reference expression matrix
marker

matrix of markers
metadata

cell cluster assignments, supplied as a vector or data.frame. If data.frame is supplied then cluster_col needs to be set.
cluster_col

column in metadata that contains cluster ids per cell. Will default to first column of metadata if not supplied. Not required if running correlation per cell.
query_genes

A vector of genes of interest to compare. If NULL, then common genes between the expr_mat and ref_mat will be used for comparision.
compute_method

method(s) for computing similarity scores
weight

relative weight for the gene list scores, when added to correlation score
threshold

identity calling minimum score threshold, only used when obj_out = T
dr

stored dimension reduction
norm

whether and how the results are normalized
call

make call or just return score matrix
marker_inmatrix

whether markers genes are already in preprocessed matrix form
mode

use marker expression pct or ranked cor score for nudging
obj_out

whether to output object instead of cor matrix
seurat_out

output cor matrix or called seurat object (deprecated, use obj_out)
rename_prefix

prefix to add to type and r column names
lookuptable

if not supplied, will look in built-in table for object parsing

Value

single cell object, or matrix of numeric values, clusters from input as row names, cell types from ref_mat as column names

Examples

# Seurat
so <- so_pbmc()
clustify_nudge(
    input = so,
    ref_mat = cbmc_ref,
    marker = cbmc_m,
    cluster_col = "seurat_clusters",
    threshold = 0.8,
    obj_out = FALSE,
    mode = "pct",
    dr = "umap"
)

# Matrix
clustify_nudge(
    input = pbmc_matrix_small,
    ref_mat = cbmc_ref,
    metadata = pbmc_meta,
    marker = as.matrix(cbmc_m),
    query_genes = pbmc_vargenes,
    cluster_col = "classified",
    threshold = 0.8,
    call = FALSE,
    marker_inmatrix = FALSE,
    mode = "pct"
)

Correlation functions available in clustifyr

Description

Correlation functions available in clustifyr

Usage

clustifyr_methods

Format

An object of class character of length 5.

Examples

clustifyr_methods

From per-cell calls, take highest freq call in each cluster

Description

From per-cell calls, take highest freq call in each cluster

Usage

collapse_to_cluster(res, metadata, cluster_col, threshold = 0)

Arguments

res

dataframe of idents, such as output of cor_to_call
metadata

input metadata with tsne or umap coordinates and cluster ids
cluster_col

metadata column for cluster
threshold

minimum correlation coefficent cutoff for calling clusters

Value

new metadata with added columns

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    ref_mat = cbmc_ref,
    per_cell = TRUE
)

res2 <- cor_to_call(res)

collapse_to_cluster(
    res2,
    metadata = pbmc_meta,
    cluster_col = "classified",
    threshold = 0
)

Calculate adjusted p-values for hypergeometric test of gene lists or jaccard index

Description

Calculate adjusted p-values for hypergeometric test of gene lists or jaccard index

Usage

compare_lists(
  bin_mat,
  marker_mat,
  n = 30000,
  metric = "hyper",
  output_high = TRUE,
  details_out = FALSE
)

Arguments

bin_mat

binarized single-cell expression matrix, feed in by_cluster mat, if desired
marker_mat

matrix or dataframe of candidate genes for each cluster
n

number of genes in the genome
metric

adjusted p-value for hypergeometric test, or jaccard index
output_high

if true (by default to fit with rest of package), -log10 transform p-value
details_out

whether to also output shared gene list from jaccard

Value

matrix of numeric values, clusters from expr_mat as row names, cell types from marker_mat as column names

Examples

pbmc_mm <- matrixize_markers(pbmc_markers)

pbmc_avg <- average_clusters(
    pbmc_matrix_small,
    pbmc_meta,
    cluster_col = "classified"
)

pbmc_avgb <- binarize_expr(pbmc_avg)

compare_lists(
    pbmc_avgb,
    pbmc_mm,
    metric = "spearman"
)

get best calls for each cluster

Description

get best calls for each cluster

Usage

cor_to_call(
  cor_mat,
  metadata = NULL,
  cluster_col = "cluster",
  collapse_to_cluster = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  carry_r = FALSE
)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with tsne or umap coordinates and cluster ids
cluster_col

metadata column, can be cluster or cellid
collapse_to_cluster

if a column name is provided, takes the most frequent call of entire cluster to color in plot
threshold

minimum correlation coefficent cutoff for calling clusters
rename_prefix

prefix to add to type and r column names
carry_r

whether to include threshold in unassigned names

Value

dataframe of cluster, new ident, and r info

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    ref_mat = cbmc_ref
)

cor_to_call(res)

get ranked calls for each cluster

Description

get ranked calls for each cluster

Usage

cor_to_call_rank(
  cor_mat,
  metadata = NULL,
  cluster_col = "cluster",
  collapse_to_cluster = FALSE,
  threshold = 0,
  rename_prefix = NULL,
  top_n = NULL
)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with tsne or umap coordinates and cluster ids
cluster_col

metadata column, can be cluster or cellid
collapse_to_cluster

if a column name is provided, takes the most frequent call of entire cluster to color in plot
threshold

minimum correlation coefficent cutoff for calling clusters
rename_prefix

prefix to add to type and r column names
top_n

the number of ranks to keep, the rest will be set to 100

Value

dataframe of cluster, new ident, and r info

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    ref_mat = cbmc_ref
)

cor_to_call_rank(res, threshold = "auto")

get top calls for each cluster

Description

get top calls for each cluster

Usage

cor_to_call_topn(
  cor_mat,
  metadata = NULL,
  col = "cluster",
  collapse_to_cluster = FALSE,
  threshold = 0,
  topn = 2
)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with tsne or umap coordinates and cluster ids
col

metadata column, can be cluster or cellid
collapse_to_cluster

if a column name is provided, takes the most frequent call of entire cluster to color in plot
threshold

minimum correlation coefficent cutoff for calling clusters
topn

number of calls for each cluster

Value

dataframe of cluster, new potential ident, and r info

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "classified"
)

cor_to_call_topn(
    cor_mat = res,
    metadata = pbmc_meta,
    col = "classified",
    collapse_to_cluster = FALSE,
    threshold = 0.5
)

Cosine distance

Description

Cosine distance

Usage

cosine(vec1, vec2)

Arguments

vec1

test vector
vec2

reference vector

Value

numeric value of cosine distance between the vectors

table of references stored in clustifyrdata

Description

table of references stored in clustifyrdata

Usage

downrefs

Format

An object of class tbl_df (inherits from tbl, data.frame) with 9 rows and 6 columns.

Source

various packages

See Also

Other data: cbmc_m, cbmc_ref, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

downsample matrix by cluster or completely random

Description

downsample matrix by cluster or completely random

Usage

downsample_matrix(
  mat,
  n = 1,
  keep_cluster_proportions = TRUE,
  metadata = NULL,
  cluster_col = "cluster"
)

Arguments

mat

expression matrix
n

number per cluster or fraction to keep
keep_cluster_proportions

whether to subsample
metadata

data.frame or vector containing cluster assignments per cell. Order must match column order in supplied matrix. If a data.frame provide the cluster_col parameters.
cluster_col

column in metadata with cluster number

Value

new smaller mat with less cell_id columns

Examples

set.seed(42)
mat <- downsample_matrix(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta$classified,
    n = 10,
    keep_cluster_proportions = TRUE
)
mat[1:3, 1:3]

Returns a list of variable genes based on PCA

Description

Extract genes, i.e. "features", based on the top loadings of principal components formed from the bulk expression data set

Usage

feature_select_PCA(
  mat = NULL,
  pcs = NULL,
  n_pcs = 10,
  percentile = 0.99,
  if_log = TRUE
)

Arguments

mat

Expression matrix. Rownames are genes, colnames are single cell cluster name, and values are average single cell expression (log transformed).
pcs

Precalculated pcs if available, will skip over processing on mat.
n_pcs

Number of PCs to selected gene loadings from. See the explore_PCA_corr.Rmd vignette for details.
percentile

Select the percentile of absolute values of PCA loadings to select genes from. E.g. 0.999 would select the top point 1 percent of genes with the largest loadings.
if_log

whether the data is already log transformed

Value

vector of genes

Examples

feature_select_PCA(
    cbmc_ref,
    if_log = FALSE
)

takes files with positive and negative markers, as described in garnett, and returns list of markers

Description

takes files with positive and negative markers, as described in garnett, and returns list of markers

Usage

file_marker_parse(filename)

Arguments

filename

txt file to load

Value

list of positive and negative gene markers

Examples

marker_file <- system.file(
    "extdata",
    "hsPBMC_markers.txt",
    package = "clustifyr"
)

file_marker_parse(marker_file)

Find rank bias

Description

Find rank bias

Usage

find_rank_bias(avg_mat, ref_mat, query_genes = NULL)

Arguments

avg_mat

average expression matrix
ref_mat

reference expression matrix
query_genes

original vector of genes used to clustify

Value

list of matrix of rank diff values

Examples

avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    if_log = FALSE
)

rankdiff <- find_rank_bias(
    avg,
    cbmc_ref,
    query_genes = pbmc_vargenes
)

pct of cells in each cluster that express genelist

Description

pct of cells in each cluster that express genelist

Usage

gene_pct(matrix, genelist, clusters, returning = "mean")

Arguments

matrix

expression matrix
genelist

vector of marker genes for one identity
clusters

vector of cluster identities
returning

whether to return mean, min, or max of the gene pct in the gene list

Value

vector of numeric values

pct of cells in every cluster that express a series of genelists

Description

pct of cells in every cluster that express a series of genelists

Usage

gene_pct_markerm(matrix, marker_m, metadata, cluster_col = NULL, norm = NULL)

Arguments

matrix

expression matrix
marker_m

matrixized markers
metadata

data.frame or vector containing cluster assignments per cell. Order must match column order in supplied matrix. If a data.frame provide the cluster_col parameters.
cluster_col

column in metadata with cluster number
norm

whether and how the results are normalized

Value

matrix of numeric values, clusters from mat as row names, cell types from marker_m as column names

Examples

gene_pct_markerm(
    matrix = pbmc_matrix_small,
    marker_m = cbmc_m,
    metadata = pbmc_meta,
    cluster_col = "classified"
)

Function to make best call from correlation matrix

Description

Function to make best call from correlation matrix

Usage

get_best_match_matrix(cor_mat)

Arguments

cor_mat

correlation matrix

Value

matrix of 1s and 0s

Function to make call and attach score

Description

Function to make call and attach score

Usage

get_best_str(name, best_mat, cor_mat, carry_cor = TRUE)

Arguments

name

name of row to query
best_mat

binarized call matrix
cor_mat

correlation matrix
carry_cor

whether the correlation score gets reported

Value

string with ident call and possibly cor value

Find entries shared in all vectors

Description

return entries found in all supplied vectors. If the vector supplied is NULL or NA, then it will be excluded from the comparison.

Usage

get_common_elements(...)

Arguments

...

vectors

Value

vector of shared elements

Compute similarity of matrices

Description

Compute similarity of matrices

Usage

get_similarity(
  expr_mat,
  ref_mat,
  cluster_ids,
  compute_method,
  pseudobulk_method = "mean",
  per_cell = FALSE,
  rm0 = FALSE,
  if_log = TRUE,
  low_threshold = 0,
  ...
)

Arguments

expr_mat

single-cell expression matrix
ref_mat

reference expression matrix
cluster_ids

vector of cluster ids for each cell
compute_method

method(s) for computing similarity scores
pseudobulk_method

method used for summarizing clusters, options are mean (default), median, truncate (10% truncated mean), or trimean, max, min
per_cell

run per cell?
rm0

consider 0 as missing data, recommended for per_cell
if_log

input data is natural log, averaging will be done on unlogged data
low_threshold

option to remove clusters with too few cells
...

additional parameters not used yet

Value

matrix of numeric values, clusters from expr_mat as row names, cell types from ref_mat as column names

Build reference atlases from external UCSC cellbrowsers

Description

Build reference atlases from external UCSC cellbrowsers

Usage

get_ucsc_reference(cb_url, cluster_col, ...)

Arguments

cb_url

URL of cellbrowser dataset (e.g. http://cells.ucsc.edu/?ds=cortex-dev). Note that the URL must contain the ds=dataset-name suffix.
cluster_col

annotation field for summarizing gene expression (e.g. clustering, cell-type name, samples, etc.)
...

additional args passed to average_clusters

Value

reference matrix

Examples

## Not run: 

# many datasets hosted by UCSC have UMI counts in the expression matrix
# set if_log = FALSE if the expression matrix has not been natural log transformed

get_ucsc_reference(cb_url = "https://cells.ucsc.edu/?ds=evocell+mus-musculus+marrow",
                   cluster_col = "Clusters", if_log = FALSE)
                   
get_ucsc_reference(cb_url = "http://cells.ucsc.edu/?ds=muscle-cell-atlas",
                   cluster_col = "cell_annotation",
                   if_log = FALSE)

## End(Not run)

Generate a unique column id for a dataframe

Description

Generate a unique column id for a dataframe

Usage

get_unique_column(df, id = NULL)

Arguments

df

dataframe with column names
id

desired id if unique

Value

character

Generate variable gene list from marker matrix

Description

Variable gene list is required for clustify main function. This function parses variables genes from a matrix input.

Usage

get_vargenes(marker_mat)

Arguments

marker_mat

matrix or dataframe of candidate genes for each cluster

Value

vector of marker gene names

Examples

get_vargenes(cbmc_m)

convert gmt format of pathways to list of vectors

Description

convert gmt format of pathways to list of vectors

Usage

gmt_to_list(
  path,
  cutoff = 0,
  sep = "\thttp://www.broadinstitute.org/gsea/msigdb/cards/.*?\t"
)

Arguments

path

gmt file path
cutoff

remove pathways with less genes than this cutoff
sep

sep used in file to split path and genes

Value

list of genes in each pathway

Examples

gmt_file <- system.file(
    "extdata",
    "c2.cp.reactome.v6.2.symbols.gmt.gz",
    package = "clustifyr"
)

gene.lists <- gmt_to_list(path = gmt_file)
length(gene.lists)

Vector of human genes for 10x cellranger pipeline

Description

Vector of human genes for 10x cellranger pipeline

Usage

human_genes_10x

Format

An object of class character of length 33514.

Source

https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest

See Also

Other data: cbmc_m, cbmc_ref, downrefs, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

more flexible metadata update of single cell objects

Description

more flexible metadata update of single cell objects

Usage

insert_meta_object(
  input,
  new_meta,
  type = class(input),
  meta_loc = NULL,
  lookuptable = NULL
)

Arguments

input

input object
new_meta

new metadata table to insert back into object
type

look up predefined slots/loc
meta_loc

metadata location
lookuptable

if not supplied, will look in built-in table for object parsing

Value

new object with new metadata inserted

Examples

so <- so_pbmc()
insert_meta_object(so, seurat_meta(so, dr = "umap"))

KL divergence

Description

Use package entropy to compute Kullback-Leibler divergence. The function first converts each vector's reads to pseudo-number of transcripts by normalizing the total reads to total_reads. The normalized read for each gene is then rounded to serve as the pseudo-number of transcripts. Function entropy::KL.shrink is called to compute the KL-divergence between the two vectors, and the maximal allowed divergence is set to max_KL. Finally, a linear transform is performed to convert the KL divergence, which is between 0 and max_KL, to a similarity score between -1 and 1.

Usage

kl_divergence(vec1, vec2, if_log = FALSE, total_reads = 1000, max_KL = 1)

Arguments

vec1

Test vector
vec2

Reference vector
if_log

Whether the vectors are log-transformed. If so, the raw count should be computed before computing KL-divergence.
total_reads

Pseudo-library size
max_KL

Maximal allowed value of KL-divergence.

Value

numeric value, with additional attributes, of kl divergence between the vectors

make combination ref matrix to assess intermixing

Description

make combination ref matrix to assess intermixing

Usage

make_comb_ref(ref_mat, if_log = TRUE, sep = "_and_")

Arguments

ref_mat

reference expression matrix
if_log

whether input data is natural
sep

separator for name combinations

Value

expression matrix

Examples

ref <- make_comb_ref(
    cbmc_ref,
    sep = "_+_"
)
ref[1:3, 1:3]

decide for one gene whether it is a marker for a certain cell type

Description

decide for one gene whether it is a marker for a certain cell type

Usage

marker_select(row1, cols, cut = 1, compto = 1)

Arguments

row1

a numeric vector of expression values (row)
cols

a vector of cell types (column)
cut

an expression minimum cutoff
compto

compare max expression to the value of next 1 or more

Value

vector of cluster name and ratio value

Examples

pbmc_avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    if_log = FALSE
)

marker_select(
    row1 = pbmc_avg["PPBP", ],
    cols = names(pbmc_avg["PPBP", ])
)

Convert candidate genes list into matrix

Description

Convert candidate genes list into matrix

Usage

matrixize_markers(
  marker_df,
  ranked = FALSE,
  n = NULL,
  step_weight = 1,
  background_weight = 0,
  unique = FALSE,
  metadata = NULL,
  cluster_col = "classified",
  remove_rp = FALSE
)

Arguments

marker_df

dataframe of candidate genes, must contain "gene" and "cluster" columns, or a matrix of gene names to convert to ranked
ranked

unranked gene list feeds into hyperp, the ranked gene list feeds into regular corr_coef
n

number of genes to use
step_weight

ranked genes are tranformed into pseudo expression by descending weight
background_weight

ranked genes are tranformed into pseudo expression with added weight
unique

whether to use only unique markers to 1 cluster
metadata

vector or dataframe of cluster names, should have column named cluster
cluster_col

column for cluster names to replace original cluster, if metadata is dataframe
remove_rp

do not include rps, rpl, rp1-9 in markers

Value

matrix of unranked gene marker names, or matrix of ranked expression

Examples

matrixize_markers(pbmc_markers)

Vector of mouse genes for 10x cellranger pipeline

Description

Vector of mouse genes for 10x cellranger pipeline

Usage

mouse_genes_10x

Format

An object of class character of length 31017.

Source

https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest

See Also

Other data: cbmc_m, cbmc_ref, downrefs, human_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

black and white palette for plotting continous variables

Description

black and white palette for plotting continous variables

Usage

not_pretty_palette

Format

An object of class character of length 9.

Value

vector of colors

Function to access object data

Description

Function to access object data

Usage

object_data(object, ...)

## S3 method for class 'Seurat'
object_data(object, slot, n_genes = 1000, ...)

## S3 method for class 'SingleCellExperiment'
object_data(object, slot, ...)

Arguments

object

object after tsne or umap projections and clustering
...

additional arguments
slot

data to access
n_genes

number of genes limit for Seurat variable genes, by default 1000, set to 0 to use all variable genes (generally not recommended)

Value

expression matrix, with genes as row names, and cell types as column names

Examples

so <- so_pbmc()
mat <- object_data(
    object = so,
    slot = "data"
)
mat[1:3, 1:3]
sce <- sce_pbmc()
mat <- object_data(
    object = sce,
    slot = "data"
)
mat[1:3, 1:3]

lookup table for single cell object structures

Description

lookup table for single cell object structures

Usage

object_loc_lookup()

Function to convert labelled object to avg expression matrix

Description

Function to convert labelled object to avg expression matrix

Usage

object_ref(input, ...)

## Default S3 method:
object_ref(
  input,
  cluster_col = NULL,
  var_genes_only = FALSE,
  assay_name = NULL,
  method = "mean",
  lookuptable = NULL,
  if_log = TRUE,
  ...
)

## S3 method for class 'Seurat'
object_ref(
  input,
  cluster_col = NULL,
  var_genes_only = FALSE,
  assay_name = NULL,
  method = "mean",
  lookuptable = NULL,
  if_log = TRUE,
  ...
)

## S3 method for class 'SingleCellExperiment'
object_ref(
  input,
  cluster_col = NULL,
  var_genes_only = FALSE,
  assay_name = NULL,
  method = "mean",
  lookuptable = NULL,
  if_log = TRUE,
  ...
)

Arguments

input

object after tsne or umap projections and clustering
...

additional arguments
cluster_col

column name where classified cluster names are stored in seurat meta data, cannot be "rn"
var_genes_only

whether to keep only var.genes in the final matrix output, could also look up genes used for PCA
assay_name

any additional assay data, such as ADT, to include. If more than 1, pass a vector of names
method

whether to take mean (default) or median
lookuptable

if not supplied, will look in built-in table for object parsing
if_log

input data is natural log, averaging will be done on unlogged data

Value

reference expression matrix, with genes as row names, and cell types as column names

Examples

so <- so_pbmc()
object_ref(
    so,
    cluster_col = "seurat_clusters"
)

Overcluster by kmeans per cluster

Description

Overcluster by kmeans per cluster

Usage

overcluster(mat, cluster_id, power = 0.15)

Arguments

mat

expression matrix
cluster_id

list of ids per cluster
power

decides the number of clusters for kmeans

Value

new cluster_id list of more clusters

Examples

res <- overcluster(
    mat = pbmc_matrix_small,
    cluster_id = split(colnames(pbmc_matrix_small), pbmc_meta$classified)
)
length(res)

compare clustering parameters and classification outcomes

Description

compare clustering parameters and classification outcomes

Usage

overcluster_test(
  expr,
  metadata,
  ref_mat,
  cluster_col,
  x_col = "UMAP_1",
  y_col = "UMAP_2",
  n = 5,
  ngenes = NULL,
  query_genes = NULL,
  threshold = 0,
  do_label = TRUE,
  do_legend = FALSE,
  newclustering = NULL,
  combine = TRUE
)

Arguments

expr

expression matrix
metadata

metadata including cluster info and dimension reduction plotting
ref_mat

reference matrix
cluster_col

column of clustering from metadata
x_col

column of metadata for x axis plotting
y_col

column of metadata for y axis plotting
n

expand n-fold for over/under clustering
ngenes

number of genes to use for feature selection, use all genes if NULL
query_genes

vector, otherwise genes with be recalculated
threshold

type calling threshold
do_label

whether to label each cluster at median center
do_legend

whether to draw legend
newclustering

use kmeans if NULL on dr or col name for second column of clustering
combine

if TRUE return a single plot with combined panels, if FALSE return list of plots (default: TRUE)

Value

faceted ggplot object

Examples

set.seed(42)
overcluster_test(
    expr = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    cluster_col = "classified",
    x_col = "UMAP_1",
    y_col = "UMAP_2"
)

more flexible parsing of single cell objects

Description

more flexible parsing of single cell objects

Usage

parse_loc_object(
  input,
  type = class(input),
  expr_loc = NULL,
  meta_loc = NULL,
  var_loc = NULL,
  cluster_col = NULL,
  lookuptable = NULL
)

Arguments

input

input object
type

look up predefined slots/loc
expr_loc

function that extracts expression matrix
meta_loc

function that extracts metadata
var_loc

function that extracts variable genes
cluster_col

column of clustering from metadata
lookuptable

if not supplied, will use object_loc_lookup() for parsing.

Value

list of expression, metadata, vargenes, cluster_col info from object

Examples

so <- so_pbmc() 
obj <- parse_loc_object(so)
length(obj)

Marker genes identified by Seurat from single-cell RNA-seq PBMCs.

Description

Dataframe of markers from Seurat FindAllMarkers function

Usage

pbmc_markers

Format

An object of class data.frame with 2304 rows and 7 columns.

Source

⁠[pbmc_matrix]⁠ processed by Seurat

See Also

Other data: cbmc_m, cbmc_ref, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

Marker genes identified by M3Drop from single-cell RNA-seq PBMCs.

Description

Selected features of 3k pbmcs from Seurat3 tutorial

Usage

pbmc_markers_M3Drop

Format

A data frame with 3 variables:

Source

⁠[pbmc_matrix]⁠ processed by ⁠[M3Drop]⁠

See Also

Other data: cbmc_m, cbmc_ref, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_matrix_small, pbmc_meta, pbmc_vargenes

Matrix of single-cell RNA-seq PBMCs.

Description

Count matrix of 3k pbmcs from Seurat3 tutorial, with only var.features

Usage

pbmc_matrix_small

Format

A sparseMatrix with genes as rows and cells as columns.

Source

https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html

See Also

Other data: cbmc_m, cbmc_ref, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_meta, pbmc_vargenes

Meta-data for single-cell RNA-seq PBMCs.

Description

Metadata, including umap, of 3k pbmcs from Seurat3 tutorial

Usage

pbmc_meta

Format

An object of class data.frame with 2638 rows and 9 columns.

Source

⁠[pbmc_matrix]⁠ processed by Seurat

See Also

Other data: cbmc_m, cbmc_ref, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_vargenes

Variable genes identified by Seurat from single-cell RNA-seq PBMCs.

Description

Top 2000 variable genes from 3k pbmcs from Seurat3 tutorial

Usage

pbmc_vargenes

Format

An object of class character of length 2000.

Source

⁠[pbmc_matrix]⁠ processed by Seurat

See Also

Other data: cbmc_m, cbmc_ref, downrefs, human_genes_10x, mouse_genes_10x, pbmc_markers, pbmc_markers_M3Drop, pbmc_matrix_small, pbmc_meta

Percentage detected per cluster

Description

Percentage detected per cluster

Usage

percent_clusters(mat, metadata, cluster_col = "cluster", cut_num = 0.5)

Arguments

mat

expression matrix
metadata

data.frame with cells
cluster_col

column in metadata with cluster number
cut_num

binary cutoff for detection

Value

matrix of numeric values, with genes for row names, and clusters for column names

Compute a p-value for similarity using permutation

Description

Permute cluster labels to calculate empirical p-value

Usage

permute_similarity(
  expr_mat,
  ref_mat,
  cluster_ids,
  n_perm,
  per_cell = FALSE,
  compute_method,
  pseudobulk_method = "mean",
  rm0 = FALSE,
  ...
)

Arguments

expr_mat

single-cell expression matrix
ref_mat

reference expression matrix
cluster_ids

clustering info of single-cell data assume that genes have ALREADY BEEN filtered
n_perm

number of permutations
per_cell

run per cell?
compute_method

method(s) for computing similarity scores
pseudobulk_method

method used for summarizing clusters, options are mean (default), median, truncate (10% truncated mean), or trimean, max, min
rm0

consider 0 as missing data, recommended for per_cell
...

additional parameters

Value

matrix of numeric values

Plot best calls for each cluster on a tSNE or umap

Description

Plot best calls for each cluster on a tSNE or umap

Usage

plot_best_call(
  cor_mat,
  metadata,
  cluster_col = "cluster",
  collapse_to_cluster = FALSE,
  threshold = 0,
  x = "UMAP_1",
  y = "UMAP_2",
  plot_r = FALSE,
  per_cell = FALSE,
  ...
)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with tsne or umap coordinates and cluster ids
cluster_col

metadata column, can be cluster or cellid
collapse_to_cluster

if a column name is provided, takes the most frequent call of entire cluster to color in plot
threshold

minimum correlation coefficent cutoff for calling clusters
x

x variable
y

y variable
plot_r

whether to include second plot of cor eff for best call
per_cell

whether the cor_mat was generate per cell or per cluster
...

passed to plot_dims

Value

ggplot object, cells projected by dr, colored by cell type classification

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "classified"
)

plot_best_call(
    cor_mat = res,
    metadata = pbmc_meta,
    cluster_col = "classified"
)

Plot called clusters on a tSNE or umap, for each reference cluster given

Description

Plot called clusters on a tSNE or umap, for each reference cluster given

Usage

plot_call(cor_mat, metadata, data_to_plot = colnames(cor_mat), ...)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with tsne or umap coordinates and cluster ids
data_to_plot

colname of data to plot, defaults to all
...

passed to plot_dims

Value

list of ggplot object, cells projected by dr, colored by cell type classification

Plot similarity measures on a tSNE or umap

Description

Plot similarity measures on a tSNE or umap

Usage

plot_cor(
  cor_mat,
  metadata,
  data_to_plot = colnames(cor_mat),
  cluster_col = NULL,
  x = "UMAP_1",
  y = "UMAP_2",
  scale_legends = FALSE,
  ...
)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with per cell tsne or umap coordinates and cluster ids
data_to_plot

colname of data to plot, defaults to all
cluster_col

colname of clustering data in metadata, defaults to rownames of the metadata if not supplied.
x

metadata column name with 1st axis dimension. defaults to "UMAP_1".
y

metadata column name with 2nd axis dimension. defaults to "UMAP_2".
scale_legends

if TRUE scale all legends to maximum values in entire correlation matrix. if FALSE scale legends to maximum for each plot. A two-element numeric vector can also be passed to supply custom values i.e. c(0, 1)
...

passed to plot_dims

Value

list of ggplot objects, cells projected by dr, colored by cor values

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "classified"
)

plot_cor(
    cor_mat = res,
    metadata = pbmc_meta,
    data_to_plot = colnames(res)[1:2],
    cluster_col = "classified",
    x = "UMAP_1",
    y = "UMAP_2"
)

Plot similarity measures on heatmap

Description

Plot similarity measures on heatmap

Usage

plot_cor_heatmap(
  cor_mat,
  metadata = NULL,
  cluster_col = NULL,
  col = not_pretty_palette,
  legend_title = NULL,
  ...
)

Arguments

cor_mat

input similarity matrix
metadata

input metadata with per cell tsne or umap cooordinates and cluster ids
cluster_col

colname of clustering data in metadata, defaults to rownames of the metadata if not supplied.
col

color ramp to use
legend_title

legend title to pass to Heatmap
...

passed to Heatmap

Value

complexheatmap object

Examples

res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes,
    cluster_col = "classified",
    per_cell = FALSE
)

plot_cor_heatmap(res)

Plot a tSNE or umap colored by feature.

Description

Plot a tSNE or umap colored by feature.

Usage

plot_dims(
  data,
  x = "UMAP_1",
  y = "UMAP_2",
  feature = NULL,
  legend_name = "",
  c_cols = pretty_palette2,
  d_cols = NULL,
  pt_size = 0.25,
  alpha_col = NULL,
  group_col = NULL,
  scale_limits = NULL,
  do_label = FALSE,
  do_legend = TRUE,
  do_repel = TRUE
)

Arguments

data

input data
x

x variable
y

y variable
feature

feature to color by
legend_name

legend name to display, defaults to no name
c_cols

character vector of colors to build color gradient for continuous values, defaults to pretty_palette
d_cols

character vector of colors for discrete values. defaults to RColorBrewer paired palette
pt_size

point size
alpha_col

whether to refer to data column for alpha values
group_col

group by another column instead of feature, useful for labels
scale_limits

defaults to min = 0, max = max(data$x), otherwise a two-element numeric vector indicating min and max to plot
do_label

whether to label each cluster at median center
do_legend

whether to draw legend
do_repel

whether to use ggrepel on labels

Value

ggplot object, cells projected by dr, colored by feature

Examples

plot_dims(
    pbmc_meta,
    feature = "classified"
)

Plot gene expression on to tSNE or umap

Description

Plot gene expression on to tSNE or umap

Usage

plot_gene(expr_mat, metadata, genes, cell_col = NULL, ...)

Arguments

expr_mat

input single cell matrix
metadata

data.frame with tSNE or umap coordinates
genes

gene(s) to color tSNE or umap
cell_col

column name in metadata containing cell ids, defaults to rownames if not supplied
...

additional arguments passed to ⁠[clustifyr::plot_dims()]⁠

Value

list of ggplot object, cells projected by dr, colored by gene expression

Examples

genes <- c(
    "RP11-314N13.3",
    "ARF4"
)

plot_gene(
    expr_mat = pbmc_matrix_small,
    metadata = tibble::rownames_to_column(pbmc_meta, "rn"),
    genes = genes,
    cell_col = "rn"
)

plot GSEA pathway scores as heatmap, returns a list containing results and plot.

Description

plot GSEA pathway scores as heatmap, returns a list containing results and plot.

Usage

plot_pathway_gsea(
  mat,
  pathway_list,
  n_perm = 1000,
  scale = TRUE,
  topn = 5,
  returning = "both"
)

Arguments

mat

expression matrix
pathway_list

a list of vectors, each named for a specific pathway, or dataframe
n_perm

Number of permutation for fgsea function. Defaults to 1000.
scale

convert expr_mat into zscores prior to running GSEA?, default = TRUE
topn

number of top pathways to plot
returning

to return "both" list and plot, or either one

Value

list of matrix and plot, or just plot, matrix of GSEA NES values, cell types as row names, pathways as column names

Examples

gl <- list(
    "n" = c("PPBP", "LYZ", "S100A9"),
    "a" = c("IGLL5", "GNLY", "FTL")
)

pbmc_avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified"
)

plot_pathway_gsea(
    pbmc_avg,
    gl,
    5
)

Query rank bias results

Description

Query rank bias results

Usage

plot_rank_bias(bias_df, organism = "hsapiens")

Arguments

bias_df

data.frame of rank diff matrix between cluster and reference cell types
organism

for GO term analysis, organism name: human - 'hsapiens', mouse - 'mmusculus'

Value

ggplot object of distribution and annotated GO terms

Examples

## Not run: 
avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    if_log = FALSE
)

rankdiff <- find_rank_bias(
    avg,
    cbmc_ref,
    query_genes = pbmc_vargenes
)

qres <- query_rank_bias(
    rankdiff,
    "CD14+ Mono",
    "CD14+ Mono"
)

g <- plot_rank_bias(
    qres
)

## End(Not run)

generate pos and negative marker expression matrix from a list/dataframe of positive markers

Description

generate pos and negative marker expression matrix from a list/dataframe of positive markers

Usage

pos_neg_marker(mat)

Arguments

mat

matrix or dataframe of markers

Value

matrix of gene expression

Examples

m1 <- pos_neg_marker(cbmc_m)

adapt clustify to tweak score for pos and neg markers

Description

adapt clustify to tweak score for pos and neg markers

Usage

pos_neg_select(
  input,
  ref_mat,
  metadata,
  cluster_col = "cluster",
  cutoff_n = 0,
  cutoff_score = 0.5
)

Arguments

input

single-cell expression matrix
ref_mat

reference expression matrix with positive and negative markers(set expression at 0)
metadata

cell cluster assignments, supplied as a vector or data.frame. If data.frame is supplied then cluster_col needs to be set. Not required if running correlation per cell.
cluster_col

column in metadata that contains cluster ids per cell. Will default to first column of metadata if not supplied. Not required if running correlation per cell.
cutoff_n

expression cutoff where genes ranked below n are considered non-expressing
cutoff_score

positive score lower than this cutoff will be considered as 0 to not influence scores

Value

matrix of numeric values, clusters from input as row names, cell types from ref_mat as column names

Examples

pn_ref <- data.frame(
    "Myeloid" = c(1, 0.01, 0),
    row.names = c("CD74", "clustifyr0", "CD79A")
)

pos_neg_select(
    input = pbmc_matrix_small,
    ref_mat = pn_ref,
    metadata = pbmc_meta,
    cluster_col = "classified",
    cutoff_score = 0.8
)

Color palette for plotting continous variables

Description

Color palette for plotting continous variables

Usage

pretty_palette

Format

An object of class character of length 6.

Value

vector of colors

Expanded color palette ramp for plotting discrete variables

Description

Expanded color palette ramp for plotting discrete variables

Usage

pretty_palette_ramp_d(n)

Arguments

n

number of colors to use

Value

color ramp

Color palette for plotting continous variables, starting at gray

Description

Color palette for plotting continous variables, starting at gray

Usage

pretty_palette2

Format

An object of class character of length 9.

Value

vector of colors

Query rank bias results

Description

Query rank bias results

Usage

query_rank_bias(bias_list, id_mat, id_ref)

Arguments

bias_list

list of rank diff matrix between cluster and reference cell types
id_mat

name of cluster from average cluster matrix
id_ref

name of cell type in reference matrix

Value

data.frame rank diff values

Examples

avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    if_log = FALSE
)

rankdiff <- find_rank_bias(
    avg,
    cbmc_ref,
    query_genes = pbmc_vargenes
)

qres <- query_rank_bias(
    rankdiff,
    "CD14+ Mono",
    "CD14+ Mono"
)

feature select from reference matrix

Description

feature select from reference matrix

Usage

ref_feature_select(mat, n = 3000, mode = "var", rm.lowvar = TRUE)

Arguments

mat

reference matrix
n

number of genes to return
mode

the method of selecting features
rm.lowvar

whether to remove lower variation genes first

Value

vector of genes

Examples

pbmc_avg <- average_clusters(
    mat = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified"
)

ref_feature_select(
    mat = pbmc_avg[1:100, ],
    n = 5
)

marker selection from reference matrix

Description

marker selection from reference matrix

Usage

ref_marker_select(mat, cut = 0.5, arrange = TRUE, compto = 1)

Arguments

mat

reference matrix
cut

an expression minimum cutoff
arrange

whether to arrange (lower means better)
compto

compare max expression to the value of next 1 or more

Value

dataframe, with gene, cluster, ratio columns

Examples

ref_marker_select(
    cbmc_ref,
    cut = 2
)

generate negative markers from a list of exclusive positive markers

Description

generate negative markers from a list of exclusive positive markers

Usage

reverse_marker_matrix(mat)

Arguments

mat

matrix or dataframe of markers

Value

matrix of gene names

Examples

reverse_marker_matrix(cbmc_m)

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

Description

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

Usage

run_clustifyr_app()

Value

instance of shiny app

Examples

## Not run: 
run_clustifyr_app()

## End(Not run)

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

Description

Use fgsea algorithm to compute normalized enrichment scores and pvalues for gene set ovelap

Usage

run_gsea(
  expr_mat,
  query_genes,
  cluster_ids = NULL,
  n_perm = 1000,
  per_cell = FALSE,
  scale = FALSE,
  no_warnings = TRUE
)

Arguments

expr_mat

single-cell expression matrix or Seurat object
query_genes

A vector or named list of vectors of genesets of interest to compare via GSEA. If supplying a named list, then the gene set names will appear in the output.
cluster_ids

vector of cell cluster assignments, supplied as a vector with order that matches columns in expr_mat. Not required if running per cell.
n_perm

Number of permutation for fgsea function. Defaults to 1000.
per_cell

if true run per cell, otherwise per cluster.
scale

convert expr_mat into zscores prior to running GSEA?, default = FALSE
no_warnings

suppress warnings from gsea ties

Value

dataframe of gsea scores (pval, NES), with clusters as rownames

An example SingleCellExperiment object

Description

An example SingleCellExperiment object

Usage

sce_pbmc()

Value

a SingleCellExperiment object populated with data from the pbmc_matrix_small scRNA-seq dataset, additionally annotated with cluster assignments.

Function to convert labelled seurat object to fully prepared metadata

Description

Function to convert labelled seurat object to fully prepared metadata

Usage

seurat_meta(seurat_object, ...)

## S3 method for class 'Seurat'
seurat_meta(seurat_object, dr = "umap", ...)

Arguments

seurat_object

seurat_object after tsne or umap projections and clustering
...

additional arguments
dr

dimension reduction method

Value

dataframe of metadata, including dimension reduction plotting info

Examples

so <- so_pbmc()
m <- seurat_meta(so)

Function to convert labelled seurat object to avg expression matrix

Description

Function to convert labelled seurat object to avg expression matrix

Usage

seurat_ref(seurat_object, ...)

## S3 method for class 'Seurat'
seurat_ref(
  seurat_object,
  cluster_col = "classified",
  var_genes_only = FALSE,
  assay_name = NULL,
  method = "mean",
  subclusterpower = 0,
  if_log = TRUE,
  ...
)

Arguments

seurat_object

seurat_object after tsne or umap projections and clustering
...

additional arguments
cluster_col

column name where classified cluster names are stored in seurat meta data, cannot be "rn"
var_genes_only

whether to keep only var_genes in the final matrix output, could also look up genes used for PCA
assay_name

any additional assay data, such as ADT, to include. If more than 1, pass a vector of names
method

whether to take mean (default) or median
subclusterpower

whether to get multiple averages per original cluster
if_log

input data is natural log, averaging will be done on unlogged data

Value

reference expression matrix, with genes as row names, and cell types as column names

Examples

so <- so_pbmc()
ref <- seurat_ref(so, cluster_col = "seurat_clusters")

An example Seurat object

Description

An example Seurat object

Usage

so_pbmc()

Value

a Seurat object populated with data from the pbmc_matrix_small scRNA-seq dataset, additionally annotated with cluster assignments.

Compute similarity between two vectors

Description

Compute the similarity score between two vectors using a customized scoring function Two vectors may be from either scRNA-seq or bulk RNA-seq data. The lengths of vec1 and vec2 must match, and must be arranged in the same order of genes. Both vectors should be provided to this function after pre-processing, feature selection and dimension reduction.

Usage

vector_similarity(vec1, vec2, compute_method, ...)

Arguments

vec1

test vector
vec2

reference vector
compute_method

method to run i.e. corr_coef
...

arguments to pass to compute_method function

Value

numeric value of desired correlation or distance measurement

Function to write metadata to object

Description

Function to write metadata to object

Usage

write_meta(object, ...)

## S3 method for class 'Seurat'
write_meta(object, meta, ...)

## S3 method for class 'SingleCellExperiment'
write_meta(object, meta, ...)

Arguments

object

object after tsne or umap projections and clustering
...

additional arguments
meta

new metadata dataframe

Value

object with newly inserted metadata columns

Examples

so <- so_pbmc()
obj <- write_meta(
    object = so,
    meta = seurat_meta(so)
)
sce <- sce_pbmc()
obj <- write_meta(
    object = sce,
    meta = object_data(sce, "meta.data")
)