Package 'chevreulProcess' reference manual

Title:	Tools for managing SingleCellExperiment objects as projects
Description:	Tools analyzing SingleCellExperiment objects as projects. for input into the Chevreul app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.
Authors:	Kevin Stachelek [aut, cre] , Bhavana Bhat [aut]
Maintainer:	Kevin Stachelek <[email protected]>
License:	MIT + file LICENSE
Version:	0.99.21
Built:	2024-11-18 16:31:23 UTC
Source:	https://github.com/whtns/chevreulProcess

Annotate percent mitochondrial reads per cell

Description

Add a Percentage of Mitochondrial Read Count Categorical Variable to the Object (based on nCount_RNA)

Usage

add_percent_mito(object, experiment = "gene")
add_percent_mito(object, experiment = "gene")

Arguments

`object`	A object
`experiment`	gene

Value

a single cell object with cell metadata column containing mitochondrial percentage

Annotate Cell Cycle

Description

Annotate Cell Cycle for Gene and Transcript SingleCellExperiment Objects

Usage

annotate_cell_cycle(object)
annotate_cell_cycle(object)

Arguments

object

A SingleCellExperiment object

Value

a SingleCellExperiment object

Update a database of chevreul projects

Description

Append projects to database

Usage

append_to_project_db(
  new_project_path,
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)
append_to_project_db(
  new_project_path,
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)

Arguments

`new_project_path`	new project path
`cache_location`	Path to cache "~/.cache/chevreul"
`sqlite_db`	sqlite db
`verbose`	print messages

Value

a sqlite database with SingleCellExperiment objects

Create a database of bigwigfiles

Description

Create a sqlite database of bigwig files matching cell ids in objects

Usage

build_bigwig_db(bam_files, bigwig_db = "~/.cache/chevreul/bw-files.db")
build_bigwig_db(bam_files, bigwig_db = "~/.cache/chevreul/bw-files.db")

Arguments

`bam_files`	vector of paths to bam files
`bigwig_db`	bigwig database

Value

a path to a bigwig file sqlite database

Cyclone cell cycle pairs by symbol

Description

cell cycle genes with paired expression represented by HGNC symbol

Usage

cc.genes.cyclone
cc.genes.cyclone

Format

a list of dataframes with G1, G2, and S gene expression

G1: G1 gene symbols
G2: G2 gene symbols
S: S gene symbols

...

Source

cyclone

Clustering Workflow

Description

Cluster and Reduce Dimensions of a object

Usage

clustering_workflow(
  object,
  excluded_cells,
  resolution = seq(0.2, 1, by = 0.2),
  organism = "human",
  experiment_name = "default_experiment",
  ...
)
clustering_workflow(
  object,
  excluded_cells,
  resolution = seq(0.2, 1, by = 0.2),
  organism = "human",
  experiment_name = "default_experiment",
  ...
)

Arguments

`object`	a SingleCellExperiment object
`excluded_cells`	named list of cells to exclude
`resolution`	resolution(s) to use for clustering cells
`organism`	Organism
`experiment_name`	name of the experiment
`...`	extra args passed to sce_process

Value

a clustered SingleCellExperiment object

Convert SingleCellExperiment Objects from Human to Mouse

Description

Convert SingleCellExperiment Objects from Human to Mouse

Usage

convert_human_sce_to_mouse(object, ...)
convert_human_sce_to_mouse(object, ...)

Arguments

`object`	Human SingleCellExperiment object
`...`	to be passed to `convert_symbols_by_species`

Value

a SingleCellExperiment object

Convert gene symbols between mouse and human

Description

Convert gene symbols between mouse and human

Usage

convert_symbols_by_species(src_genes, src_species)
convert_symbols_by_species(src_genes, src_species)

Arguments

`src_genes`	Source gene symbol to be converted
`src_species`	Source species

Value

a SingleCellExperiment object

Create a database of chevreul projects

Description

Create a database containing chevreul projects

Usage

create_project_db(
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)
create_project_db(
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)

Arguments

`cache_location`	Path to cache "~/.cache/chevreul"
`sqlite_db`	Database to be created
`verbose`	print messages

Value

a sqlite database with SingleCellExperiment objects

Ensembl version used for build

Description

Ensembl version used for build

Usage

ensembl_version
ensembl_version

Format

An object of class character of length 1.

Source

http://www.ensembl.org/

Examples

# ensembl_version

# ensembl_version

Find All Markers

Description

Find all markers at a range of resolutions

Usage

find_all_markers(object, group_by = NULL, experiment = "gene", ...)
find_all_markers(object, group_by = NULL, experiment = "gene", ...)

Arguments

`object`	An object.
`group_by`	A metadata variable to group by.
`experiment`	Assay to use, Default "gene".
`...`	extra args passed to stash_marker_features

Value

a SingleCellExperiment object containing marker genes

Examples

data("small_example_dataset")
find_all_markers(small_example_dataset, "gene_snn_res.1")

data("small_example_dataset")
find_all_markers(small_example_dataset, "gene_snn_res.1")

Gene Symbols to Ensembl Transcript Ids

Description

convert hgnc gene symbols to ensembl transcript ids

Usage

genes_to_transcripts(symbols)
genes_to_transcripts(symbols)

Arguments

symbols

character vector of gene symbols

Value

a vector of transcripts

Examples


genes_to_transcripts("NRL")
genes_to_transcripts("NRL")

Get cell metadata from a given object

Description

Get cell metadata

Usage

get_colData(object)
get_colData(object)

Arguments

object

a SingleCellExperiment object

Value

dataframe containing object metadata

Examples


data(small_example_dataset)
get_colData(small_example_dataset)
data(small_example_dataset)
get_colData(small_example_dataset)

Get Feature Types

Description

Get Feature Types

Usage

get_feature_types(object)
get_feature_types(object)

Arguments

object

a SingleCellExperiment object

Value

vector of feature types in an object

Examples


data(small_example_dataset)
get_feature_types(small_example_dataset)
data(small_example_dataset)
get_feature_types(small_example_dataset)

Get feature names

Description

Get feature names

Usage

get_features(object, experiment = "gene")
get_features(object, experiment = "gene")

Arguments

`object`	a SingleCellExperiment object
`experiment`	"gene" or "transcript"

Value

variable features from a SingleCellExperiment object

Examples


data(small_example_dataset)
get_features(small_example_dataset)
data(small_example_dataset)
get_features(small_example_dataset)

Get object metadata

Description

Get object metadata

Usage

get_sce_metadata(object)
get_sce_metadata(object)

Arguments

object

a SingleCellExperiment object

Value

variable features from a SingleCellExperiment object

Get Transcripts in object

Description

Get transcript ids in objects for one or more gene of interest

Usage

get_transcripts_from_sce(object, gene)
get_transcripts_from_sce(object, gene)

Arguments

`object`	A SingleCellExperiment object
`gene`	Gene of interest

Value

transcripts constituting a gene of interest in a SingleCellExperiment object

Get variable features

Description

Get variable features

Usage

get_variable_features(object, experiment = "gene")
get_variable_features(object, experiment = "gene")

Arguments

`object`	a SingleCellExperiment object
`experiment`	"gene" or "transcript"

Value

variable features from a SingleCellExperiment object

Examples


data(small_example_dataset)
get_variable_features(small_example_dataset)
data(small_example_dataset)
get_variable_features(small_example_dataset)

Human annotation data

Description

Human (Homo sapiens) annotations based on genome assembly GRCH38 from Ensembl.

Usage

grch38
grch38

Format

An object of class tbl_df (inherits from tbl, data.frame) with 76062 rows and 9 columns.

Details

Variables:

ensgene
entrez
symbol
chr
start
end
strand
biotype
description

Source

http://ensembl.org/homo_sapiens

Examples

data("grch38")
head(grch38)
data("grch38")
head(grch38)

Human transcripts to genes

Description

Lookup table for converting Human (Homo sapiens) Ensembl transcript IDs to gene IDs based on genome assembly GRCH38 from Ensembl.

Usage

grch38_tx2gene
grch38_tx2gene

Format

An object of class tbl_df (inherits from tbl, data.frame) with 277081 rows and 2 columns.

Details

Variables:

enstxp
ensgene

Source

http://ensembl.org/homo_sapiens

Examples

data(grch38_tx2gene)
head(grch38_tx2gene)
data(grch38_tx2gene)
head(grch38_tx2gene)

Gene Homologs Between Human and Mouse

Description

Homologs drawn from Biomart

Usage

human_to_mouse_homologs
human_to_mouse_homologs

Format

A data frame with 23188 rows and 2 columns

HGNC.symbol: human gene symbols
MGI.symbol: mouse gene symbols

...

Source

bioMart

Batch Correct Multiple Single Cell Objects

Description

Batch Correct Multiple Single Cell Objects

Usage

integrate(sce_list, organism = "human", ...)
integrate(sce_list, organism = "human", ...)

Arguments

`sce_list`	List of two or more SingleCellExperiment objects
`organism`	human or mouse
`...`	extra args passed to sce_reduce_dimensions

Value

an integrated SingleCellExperiment object

Integration Workflow

Description

Integrate multiple objects and save to file

Usage

integration_workflow(
  batches,
  excluded_cells = NULL,
  resolution = seq(0.2, 1, by = 0.2),
  experiment_name = "default_experiment",
  organism = "human",
  ...
)
integration_workflow(
  batches,
  excluded_cells = NULL,
  resolution = seq(0.2, 1, by = 0.2),
  experiment_name = "default_experiment",
  organism = "human",
  ...
)

Arguments

`batches`	objects for all batches provided as a list. If named, the resulting integrated object will be identified with corresponding values in 'batch' metadata
`excluded_cells`	named list of cells to exclude
`resolution`	value(s) to control the clustering resolution via `scran::findMarkers`
`experiment_name`	arbitrary name to identify experiment
`organism`	either "human" or "mouse"
`...`	extra args passed to sce_integrate

Value

an integrated SingleCellExperiment object

Load Bigwigs

Description

Load a tibble of bigwig file paths by cell id

Usage

load_bigwigs(object, bigwig_db = "~/.cache/chevreul/bw-files.db")
load_bigwigs(object, bigwig_db = "~/.cache/chevreul/bw-files.db")

Arguments

`object`	A object
`bigwig_db`	Sqlite database of bigwig files

Value

a vector of bigwigs file paths

Load SingleCellExperiment Files from a single project path

Description

Load SingleCellExperiment Files from a single project path

Usage

load_sce_from_proj(proj_dir, ...)
load_sce_from_proj(proj_dir, ...)

Arguments

`proj_dir`	project directory
`...`	extra args passed to load_sce_path

Value

a SingleCellExperiment object

Read in Gene and Transcript SingleCellExperiment Objects

Description

Read in Gene and Transcript SingleCellExperiment Objects

Usage

load_sce_path(proj_dir = getwd(), prefix = "unfiltered")
load_sce_path(proj_dir = getwd(), prefix = "unfiltered")

Arguments

`proj_dir`	path to project directory
`prefix`	default "unfiltered"

Value

a SingleCellExperiment object

Make Bigwig Database

Description

Make Bigwig Database

Usage

make_bigwig_db(
  new_project = NULL,
  cache_location = "~/.cache/chevreul/",
  sqlite_db = "bw-files.db"
)
make_bigwig_db(
  new_project = NULL,
  cache_location = "~/.cache/chevreul/",
  sqlite_db = "bw-files.db"
)

Arguments

`new_project`	Project directory
`cache_location`	Path to cache "~/.cache/chevreul"
`sqlite_db`	sqlite db containing bw files

Value

a sqlite database of bigwig files for cells in a SingleCellExperiment object

Merge Small SingleCellExperiment Objects

Description

Merge Small SingleCellExperiment Objects

Usage

merge_small_sces(..., k.filter = 50)
merge_small_sces(..., k.filter = 50)

Arguments

`...`	two or more singlecell objects
`k.filter`	minimum cell number for integration

Value

a SingleCellExperiment object

Retrieve Metadata from Batch

Description

Retrieve Metadata from Batch

Usage

metadata_from_batch(
  batch,
  projects_dir = "/dataVolume/storage/single_cell_projects",
  db_path = "single-cell-projects.db"
)
metadata_from_batch(
  batch,
  projects_dir = "/dataVolume/storage/single_cell_projects",
  db_path = "single-cell-projects.db"
)

Arguments

`batch`	batch
`projects_dir`	path to project dir
`db_path`	path to .db file

Value

a tibble with cell level metadata from a SingleCellExperiment object

Propagate Metadata Changes

Description

Propagate Metadata Changes

Usage

propagate_spreadsheet_changes(meta, object)
propagate_spreadsheet_changes(meta, object)

Arguments

`meta`	updated metadata
`object`	a SingleCellExperiment object

Value

a SingleCellExperiment object

Examples



data(small_example_dataset)
new_meta <- data.frame(row.names = colnames(small_example_dataset))
new_meta$example <- "example"

propagate_spreadsheet_changes(new_meta, small_example_dataset)
data(small_example_dataset)
new_meta <- data.frame(row.names = colnames(small_example_dataset))
new_meta$example <- "example"

propagate_spreadsheet_changes(new_meta, small_example_dataset)

Query Experiment

Description

Query Experiment

Usage

query_experiment(object, experiment)
query_experiment(object, experiment)

Arguments

`object`	a SingleCellExperiment object
`experiment`	an experiment name

Value

logical scalar indicating if experiment is present in object

Examples

data(small_example_dataset)
query_experiment(small_example_dataset, "gene")
data(small_example_dataset)
query_experiment(small_example_dataset, "gene")

Read a database of chevreul projects

Description

Reads database of chevreul projects to a data frame

Usage

read_project_db(
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)
read_project_db(
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)

Arguments

`cache_location`	Path to cache "~/.cache/chevreul"
`sqlite_db`	sqlite db
`verbose`	print messages

Value

a tibble with SingleCellExperiment objects

Record Experiment Metadata

Description

Records miscellaneous data

Usage

record_experiment_data(
  object,
  experiment_name = "default_experiment",
  organism = "human"
)
record_experiment_data(
  object,
  experiment_name = "default_experiment",
  organism = "human"
)

Arguments

`object`	A object
`experiment_name`	name of the experiment
`organism`	human or mouse

Value

a SingleCellExperiment object

Examples


data(small_example_dataset)
record_experiment_data(small_example_dataset)

data(small_example_dataset)
record_experiment_data(small_example_dataset)

Regress SingleCellExperiment Object by Given Set of Genes

Description

Regress SingleCellExperiment Object by Given Set of Genes

Usage

regress_cell_cycle(object)
regress_cell_cycle(object)

Arguments

object

A object

Value

a SingleCellExperiment object with features regressed

Reintegrate (filtered) SingleCellExperiment objects

Description

This function takes a SCE object and performs the below steps

split by batch
integrate
run integration pipeline and save

Usage

reintegrate_sce(object, suffix = "", reduction = "PCA", ...)
reintegrate_sce(object, suffix = "", reduction = "PCA", ...)

Arguments

`object`	A SingleCellExperiment objects
`suffix`	to be appended to file saved in output dir
`reduction`	to use default is pca
`...`	extra args passed to sce_integrate

Value

a SingleCellExperiment object

Retrieve Assay

Description

Retrieve Assay

Usage

retrieve_experiment(object, experiment)
retrieve_experiment(object, experiment)

Arguments

`object`	a SingleCellExperiment object
`experiment`	an experiment name

Value

Main or alt experiment in a SingleCellExperiment object

Save object to /output/sce/_sce.rds

Description

Save object to /output/sce/_sce.rds

Usage

save_sce(object, prefix = "unfiltered", proj_dir = getwd())
save_sce(object, prefix = "unfiltered", proj_dir = getwd())

Arguments

`object`	a SingleCellExperiment object
`prefix`	a prefix for saving
`proj_dir`	path to a project directory

Value

a path to an rds file containing a SingleCellExperiment object

Calculate Read Count Metrics for a object

Description

Recalculate counts/features per cell for a object

Usage

sce_calcn(object)
sce_calcn(object)

Arguments

object

A SingleCellExperiment object

Value

a SingleCellExperiment object with nfeatures and ngenes stored in metadata

Examples


data(small_example_dataset)
sce_calcn(small_example_dataset)
data(small_example_dataset)
sce_calcn(small_example_dataset)

Run Louvain Clustering at Multiple Resolutions

Description

Run Louvain Clustering at Multiple Resolutions

Usage

sce_cluster(
  object = object,
  resolution = 0.6,
  custom_clust = NULL,
  reduction = "PCA",
  algorithm = 1,
  ...
)
sce_cluster(
  object = object,
  resolution = 0.6,
  custom_clust = NULL,
  reduction = "PCA",
  algorithm = 1,
  ...
)

Arguments

`object`	A SingleCellExperiment objects
`resolution`	Clustering resolution
`custom_clust`	custom cluster
`reduction`	Set dimensional reduction object
`algorithm`	1
`...`	extra args passed to single cell packages

Value

a SingleCellExperiment object with louvain clusters

Examples

data(small_example_dataset)
sce_cluster(small_example_dataset)

data(small_example_dataset)
sce_cluster(small_example_dataset)

Run Differential Expression

Description

Run Differential Expression

Usage

sce_de(
  object,
  cluster1,
  cluster2,
  resolution = 0.2,
  diffex_scheme = "louvain",
  featureType = "gene",
  tests = c("t", "wilcox", "bimod")
)
sce_de(
  object,
  cluster1,
  cluster2,
  resolution = 0.2,
  diffex_scheme = "louvain",
  featureType = "gene",
  tests = c("t", "wilcox", "bimod")
)

Arguments

`object`	a SingleCellExperiment object
`cluster1`	cluster 1
`cluster2`	cluster 2
`resolution`	resolution
`diffex_scheme`	scheme for differential expression
`featureType`	gene or transcript
`tests`	t, wilcox, or bimod

Value

a dataframe with differential expression information

Examples

data("tiny_sce")
sce_de(tiny_sce, 
colnames(tiny_sce)[1:100], 
colnames(tiny_sce)[101:200], 
diffex_scheme = "custom")

data("tiny_sce")
sce_de(tiny_sce, 
colnames(tiny_sce)[1:100], 
colnames(tiny_sce)[101:200], 
diffex_scheme = "custom")

Run SingleCellExperiment Integration

Description

Run batch correction, followed by:

stashing of batches in metadata 'batch'
clustering with resolution 0.2 to 2.0 in increments of 0.2
saving to <proj_dir>/output/sce/sce.rds

Usage

sce_integrate(
  sce_list,
  resolution = seq(0.2, 1, by = 0.2),
  suffix = "",
  organism = "human",
  batch_correct = TRUE,
  annotate_cell_cycle = FALSE,
  annotate_percent_mito = FALSE,
  reduction = "corrected",
  ...
)
sce_integrate(
  sce_list,
  resolution = seq(0.2, 1, by = 0.2),
  suffix = "",
  organism = "human",
  batch_correct = TRUE,
  annotate_cell_cycle = FALSE,
  annotate_percent_mito = FALSE,
  reduction = "corrected",
  ...
)

Arguments

`sce_list`	List of objects to be integrated
`resolution`	Range of resolution
`suffix`	a suffix to be appended to a file save in output dir
`organism`	Default "human"
`batch_correct`	whether to integrate by batch correction
`annotate_cell_cycle`	whether to score cell cycle phases
`annotate_percent_mito`	logical scalar whether to annotate mitochondrial percentage
`reduction`	pca, umap, or tsne
`...`	extra args passed to integrate

Value

an integrated SingleCellExperiment object

Examples

data("tiny_sce")
tiny_sce |> 
splitByCol("bath") |> 
sce_integrate(resolution = 0.2, batch_correct = FALSE)


data("tiny_sce")
tiny_sce |> 
splitByCol("bath") |> 
sce_integrate(resolution = 0.2, batch_correct = FALSE)

Preprocess Single Cell Object

Description

Performs standard pre-processing workflow for scRNA-seq data

Usage

sce_preprocess(
  object,
  scale = TRUE,
  normalize = TRUE,
  features = NULL,
  legacy_settings = FALSE,
  ...
)
sce_preprocess(
  object,
  scale = TRUE,
  normalize = TRUE,
  features = NULL,
  legacy_settings = FALSE,
  ...
)

Arguments

`object`	Assay to use
`scale`	Perform linear transformation 'Scaling'
`normalize`	Perform normalization
`features`	Identify highly variable features
`legacy_settings`	Use legacy settings
`...`	extra args passed to scaling functions

Value

a preprocessed SingleCellExperiment object

Run SingleCellExperiment Pipeline

Description

This functions allows you to preprocess, cluster and reduce dimensions for one SingleCellExperiment object.

Usage

sce_process(
  object,
  experiment = "gene",
  resolution = 0.6,
  reduction = "PCA",
  organism = "human",
  process = TRUE,
  ...
)
sce_process(
  object,
  experiment = "gene",
  resolution = 0.6,
  reduction = "PCA",
  organism = "human",
  process = TRUE,
  ...
)

Arguments

`object`	A SingleCellExperiment object
`experiment`	Assay of interest in SingleCellExperiment object
`resolution`	Resolution for clustering cells. Default set to 0.6.
`reduction`	Dimensional reduction object
`organism`	Organism
`process`	whether to run dimensional reduction and clustering
`...`	extra parameters passed to internal functions

Value

a processed SingleCellExperiment object

Examples

data(tiny_sce)
sce_process(tiny_sce, process = FALSE)

data(tiny_sce)
sce_process(tiny_sce, process = FALSE)

Dimensional Reduction

Description

Run PCA, TSNE and UMAP on a singlecell objects perplexity should not be bigger than 3 * perplexity < nrow(X) - 1, see details for interpretation

Usage

sce_reduce_dimensions(object, experiment = "gene", ...)
sce_reduce_dimensions(object, experiment = "gene", ...)

Arguments

`object`	A SingleCellExperiment object
`experiment`	Experiment of interest to be processed
`...`	Extra parameters passed to sce_reduce_dimensions

Value

a SingleCellExperiment object with embeddings

Set cell metadata

Description

Set cell metadata from a given object

Usage

set_colData(object, meta)
set_colData(object, meta)

Arguments

`object`	a SingleCellExperiment object
`meta`	a dataframe containing object metadata

Value

a SingleCellExperiment object with new colData

Examples


data(small_example_dataset)
new_meta <- data.frame(row.names = colnames(small_example_dataset))
new_meta$example <- "example"
set_colData(small_example_dataset, new_meta)
data(small_example_dataset)
new_meta <- data.frame(row.names = colnames(small_example_dataset))
new_meta$example <- "example"
set_colData(small_example_dataset, new_meta)

Set Feature Types

Description

Set Feature Types

Usage

set_feature_type(object, feature_type)
set_feature_type(object, feature_type)

Arguments

`object`	a SingleCellExperiment object
`feature_type`	feature type

Value

a SingleCellExperiment object with assigned feature type

Examples


data(small_example_dataset)
set_feature_type(small_example_dataset, "transcript")
data(small_example_dataset)
set_feature_type(small_example_dataset, "transcript")

Small example SingleCellExperiment

Description

created with scuttle::mockSCE

Usage

small_example_dataset
small_example_dataset

Format

An SCE with 200 cells and 1000 genes

Source

scuttle::mockSCE

Split SingleCellExperiment by colData variable

Description

Split SingleCellExperiment by colData variable

Usage

splitByCol(x, f = "batch")
splitByCol(x, f = "batch")

Arguments

`x`	SingleCellExperiment object
`f`	colData variable as a string

Value

a list of singlecellexperiments name by colData value

Examples


data(small_example_dataset)
splitByCol(small_example_dataset, "batch")
data(small_example_dataset)
splitByCol(small_example_dataset, "batch")

Stash Marker Genes in a SingleCellExperiment Object

Description

Marker Genes will be stored in object metadata as markers

Usage

stash_marker_features(
  object,
  group_by,
  experiment = "gene",
  top_n = 200,
  p_val_cutoff = 0.5
)
stash_marker_features(
  object,
  group_by,
  experiment = "gene",
  top_n = 200,
  p_val_cutoff = 0.5
)

Arguments

`object`	A object
`group_by`	A metadata variable to group by
`experiment`	An experiment to use
`top_n`	Use top n genes, Default 200
`p_val_cutoff`	p value cut-off, Default value is "0.5"

Value

a SingleCellExperiment object with marker genes

Subset by new colData

Description

Subset the object using new colData

Usage

subset_by_colData(colData_path, object)
subset_by_colData(colData_path, object)

Arguments

`colData_path`	Path to new colData
`object`	A object

Value

a SingleCellExperiment object

Tiny example SingleCellExperiment

Description

subset to only NRL from chevreuldata::human_gene_transcript_sce()

Usage

tiny_sce
tiny_sce

Format

An SCE with only expression of NRL gene and NRL transripts

Source

chevreuldata::human_gene_transcript_sce()

Ensembl Transcript Ids to Gene Symbols

Description

Convert ensembl transcript ids to hgnc gene symbols

Usage

transcripts_to_genes(transcripts)
transcripts_to_genes(transcripts)

Arguments

transcripts

human transcripts

Value

a vector of gene symbols

Examples


NRL_transcripts_hs <-
c("ENST00000359842", "ENST00000470566", "ENST00000465764")

transcripts_to_genes(transcripts = NRL_transcripts_hs)

NRL_transcripts_hs <-
c("ENST00000359842", "ENST00000470566", "ENST00000465764")

transcripts_to_genes(transcripts = NRL_transcripts_hs)

Update a database of chevreul projects

Description

Add new/update existing projects to the database by recursing fully

Usage

update_project_db(
  projects_dir = NULL,
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)
update_project_db(
  projects_dir = NULL,
  cache_location = "~/.cache/chevreul",
  sqlite_db = "single-cell-projects.db",
  verbose = TRUE
)

Arguments

`projects_dir`	The project directory to be updated
`cache_location`	Path to cache "~/.cache/chevreul"
`sqlite_db`	sqlite db
`verbose`	print messages

Value

a sqlite database with SingleCellExperiment objects

Package 'chevreulProcess'

Help Index

Annotate percent mitochondrial reads per cell

Description

Usage

Arguments

Value

Annotate Cell Cycle

Description

Usage

Arguments

Value

Update a database of chevreul projects

Description

Usage

Arguments

Value

Create a database of bigwigfiles

Description

Usage

Arguments

Value

Cyclone cell cycle pairs by symbol

Description

Usage

Format

Source

Clustering Workflow

Description

Usage

Arguments

Value

Convert SingleCellExperiment Objects from Human to Mouse

Description

Usage

Arguments

Value

Convert gene symbols between mouse and human

Description

Usage

Arguments

Value

Create a database of chevreul projects

Description

Usage

Arguments

Value

Ensembl version used for build

Description

Usage

Format

Source

Examples

Find All Markers

Description

Usage

Arguments

Value

Examples

Gene Symbols to Ensembl Transcript Ids

Description

Usage

Arguments

Value

Examples

Get cell metadata from a given object

Description

Usage

Arguments

Value

Examples

Get Feature Types

Description

Usage

Arguments

Value

Examples

Get feature names

Description

Usage