Package 'scCATCH'

Title: Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity
Description: An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
Authors: Xin Shao
Maintainer: Xin Shao<[email protected]>
License: GPL (>= 3)
Version: 3.2.2
Built: 2026-06-08 08:49:25 UTC
Source: https://github.com/zjufanlab/sccatch

Help Index

cellmatch

Description

Marker genes of 'Human' and 'Mouse'.

Usage

cellmatch

Format

An object of class data.frame with 49560 rows and 11 columns.

Source

https://github.com/ZJUFanLab/scCATCH/tree/master/data

scCATCH object

Description

create scCATCH object using single-cell count data and cluster information.

Usage

createscCATCH(data, cluster)

Arguments

data

A matrix or dgCMatrix containing normalized single-cell RNA-seq data, each column representing a cell, each row representing a gene. See demo_data.
cluster

A character containing the cluster information for each cell. The length of it must be equal to the ncol of the data.

Value

scCATCH object

Demo data of single-cell RNA-seq data

Description

Demo data of single-cell RNA-seq data

Usage

demo_data()

Details

data used in createscCATCH must be a matrix object, each column representing a cell, each row representing a gene.

Value

A demo data matrix.

Examples

data_demo <- demo_data()

Demo data of geneinfo

Description

Demo data of geneinfo

Usage

demo_geneinfo()

Details

geneinfo used in rev_gene must be a data.frame object with three columns, namely 'symbol', 'synonyms', 'species'.

Value

A demo geneinfo data.frame.

Examples

geneinfo_demo <- demo_geneinfo()

Demo data of markers

Description

Demo data of markers

Usage

demo_marker()

Details

markers used in findmarkergene must be a data.frame object with eleven columns.

Value

A demo marker data.frame.

Examples

markers_demo <- demo_marker()

Evidence-based score and annotation for each cluster

Description

Evidence-based score and annotation for each cluster.

Usage

findcelltype(object, verbose = TRUE)

Arguments

object

scCATCH object generated from findmarkergene.
verbose

Show progress messages.

Value

scCATCH object containing the results of predicted cell types for each cluster.

Find potential marker genes for each cluster

Description

Identify potential marker genes for each cluster.

Usage

findmarkergene(
  object,
  species = NULL,
  cluster = "All",
  if_use_custom_marker = FALSE,
  marker = NULL,
  cancer = "Normal",
  tissue = NULL,
  use_method = "1",
  comp_cluster = NULL,
  cell_min_pct = 0.25,
  logfc = 0.25,
  pvalue = 0.05,
  verbose = TRUE
)

Arguments

object

scCATCH object generated from createscCATCH.
species

The specie of cells. The species must be defined. 'Human' or 'Mouse'. When if_use_custom_marker is set TRUE, no need to define the species.
cluster

Select which clusters for potential marker genes identification. e.g. '1', '2', etc. The default is 'All' to find potential makrer genes for each cluster.
if_use_custom_marker

Whether to use custom markers data.frame.
marker

A data.frame containing marker genes. See demo_marker. Default is to use the system cellmatch data.frame.
cancer

If the sample is from cancer tissue, then the cancer type may be defined. When if_use_custom_marker is set TRUE, no need to define the cancer.
tissue

Tissue origin of cells must be defined. Select one or more related tissue types. When if_use_custom_marker is set TRUE, no need to define the tissue.
use_method

'1' is to compare with other every cluster. '2' is to compare with other clusters together.
comp_cluster

Number of clusters to compare. Default is to compare all other cluster for each cluster. Set it between 1 and length of unique clusters. More marker genes will be obtained for smaller comp_cluster.
cell_min_pct

Include the gene detected in at least this many cells in each cluster.
logfc

Include the gene with at least this fold change of average gene expression compared to every other clusters.
pvalue

Include the significantly highly expressed gene with this cutoff of p value from wilcox test compared to every other clusters.
verbose

Show progress messages.

Details

Details of available tissues see https://github.com/ZJUFanLab/scCATCH/wiki

Value

scCATCH object

geneinfo

Description

Gene symbols of 'Human' and 'Mouse' updated on Jan. 2, 2022 for revising genes.

Usage

geneinfo

Format

An object of class data.frame with 240502 rows and 3 columns.

Source

https://www.ncbi.nlm.nih.gov/gene

Pre-processing step: revising gene symbols

Description

Revise genes according to NCBI Gene symbols updated in June 19, 2022 for count matrix, user-custom cell marker data.frame.

Usage

rev_gene(data = NULL, data_type = NULL, species = NULL, geneinfo = NULL)

Arguments

data

A matrix or dgCMatrix containing count or normalized data, each column representing a spot or a cell, each row representing a gene; Or a data.frame containing cell markers, use demo_marker.
data_type

A character to define the type of data, select 'data' for the data matrix, 'marker' for the data.frame containing cell markers.
species

Species of the data.'Human' or 'Mouse'.
geneinfo

A data.frame of the system data containing gene symbols of 'Human' and 'Mouse' updated on Jan. 1, 2022.

Value

A new matrix or data.frame.

Definition of 'scCATCH' class

Description

An S4 class containing the data, meta, and results of inferred cell types.

Slots

data

A list containing normalized data. See demo_data.

meta

A data frame containing the meta data.

para

A list containing the parameters.

markergene

A data frame containing the identified markers for each cluster.

celltype

A data frame containing the cell types for each cluster.

marker

A data frame containing the known markers. See demo_marker.