Help for package CRMetrics

Title:

Cell Ranger Output Filtering and Metrics Visualization

Version:

0.3.3

Description:

Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or 'CellBender' by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) https://github.com/kharchenkolab/pagoda2 or 'Seurat' by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or 'DoubletDetection' by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

License:

GPL-3

Encoding:

UTF-8

Depends:

R (≥ 4.1.0)

Imports:

cowplot, dplyr, ggbeeswarm, ggplot2, ggpmisc, ggpubr, ggrepel, magrittr, Matrix, methods, R6, scales, sccore, sparseMatrixStats, stats, tibble, tidyr, utils

Suggests:

conos, data.table, knitr, markdown, pagoda2, reticulate, rhdf5, rmarkdown, Seurat, SoupX, testthat (≥ 3.0.0)

URL:

https://github.com/khodosevichlab/CRMetrics

BugReports:

https://github.com/khodosevichlab/CRMetrics/issues

Maintainer:

Rasmus Rydbirk <rrydbirk@bmb.sdu.dk>

Config/testthat/edition:

Config/roxygen2/version:

8.0.0

NeedsCompilation:

Packaged:

2026-06-01 20:00:18 UTC; ucloud

Author:

Rasmus Rydbirk [aut, cre], Fabienne Kick [aut], Henrietta Holze [aut], Xian Xin [ctb], Laura Wolbeck [aut]

Repository:

CRAN

Date/Publication:

2026-06-02 06:50:11 UTC

CRMetrics class object

Description

Functions to analyze 10x Genomics Cell Ranger and Parse Biosciences split-pipe count data. To initialize a new object, 'data.path' or 'cms' is needed. 'metadata' is also recommended, but not required.

Public fields

metadata: data.frame or character Path to metadata file or name of metadata data.frame object. Metadata must contain a column named 'sample' containing sample names that must match folder names in 'data.path' (default = NULL)
data.path: character Path(s) to Cell Ranger or Parse's split-pipe count data, one directory per sample. If multiple paths, do c("path1","path2") (default = NULL)
cms: list List with count matrices from Cell Ranger or Parse's split-pipe (default = NULL)
cms.preprocessed: list List with pagoda2- or seurat-preprocessed count matrices after $doPreprocessing() (default = NULL)
cms.raw: list List with raw, unfiltered count matrices, i.e., including all cell barcodes detected, also empty ones (default = NULL)
summary.metrics: data.frame Summary metrics from Cell Ranger or Parse's split-pipe pipeline (default = NULL)
detailed.metrics: data.frame Detailed metrics, i.e., total number of genes and UMIs per cell (default = NULL)
comp.group: character A group present in the metadata to compare the metrics by, can be added with addComparison (default = NULL)
verbose: logical Print messages or not (default = TRUE)
theme: ggplot2 theme (default: theme_bw())
pal: Plotting palette (default = NULL)
n.cores: numeric Number of cores for calculations (default = 1) Initialize a CRMetrics object

Methods

Public methods

CRMetrics$new()
CRMetrics$addDetailedMetrics()
CRMetrics$plotBarcodeRankPlot()
CRMetrics$getExpectedCells()
CRMetrics$addComparison()
CRMetrics$plotSamples()
CRMetrics$plotSummaryMetrics()
CRMetrics$plotDetailedMetrics()
CRMetrics$plotEmbedding()
CRMetrics$plotDepth()
CRMetrics$plotRawDepth()
CRMetrics$plotMitoFraction()
CRMetrics$detectDoublets()
CRMetrics$doPreprocessing()
CRMetrics$createEmbedding()
CRMetrics$filterCms()
CRMetrics$selectMetrics()
CRMetrics$plotFilteredCells()
CRMetrics$getDepth()
CRMetrics$getMitoFraction()
CRMetrics$prepareCellbender()
CRMetrics$saveCellbenderScript()
CRMetrics$addCms()
CRMetrics$plotCbTraining()
CRMetrics$plotCbCellProbs()
CRMetrics$plotCbAmbExp()
CRMetrics$plotCbAmbGenes()
CRMetrics$addSummaryFromCms()
CRMetrics$runSoupX()
CRMetrics$plotSoupX()
CRMetrics$plotCbCells()
CRMetrics$addDoublets()
CRMetrics$clone()

`CRMetrics$new()`

To initialize new object, 'data.path' or 'cms' and 'technology' is needed. 'metadata' is also recommended, but not required.

Usage

CRMetrics$new(
  data.path = NULL,
  technology = c("10x", "10xflex", "10xmultiome", "parse"),
  metadata = NULL,
  cms = NULL,
  samples = NULL,
  unique.names = TRUE,
  sep.cells = "!!",
  comp.group = NULL,
  verbose = TRUE,
  theme = theme_bw(),
  n.cores = 1,
  sep.meta = ",",
  raw.meta = FALSE,
  pal = NULL
)

Arguments

data.path: character Path to directory with count data, one directory per sample (default = NULL).
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))
metadata: data.frame or character Path to metadata file (comma-separated) or name of metadata dataframe object. Metadata must contain a column named 'sample' containing sample names that must match folder names in 'data.path' (default = NULL)
cms: list List with count matrices (default = NULL)
samples: character Sample names. Only relevant if cms is provided (default = NULL)
unique.names: logical Create unique cell names. Only relevant if cms is provided (default = TRUE)
sep.cells: character Sample-cell separator. Only relevant if cms is provided and unique.names=TRUE (default = "!!")
comp.group: character A grouping variable present in the metadata column names, can be added with addComparison (default = NULL)
verbose: logical Print messages or not (default = TRUE)
theme: ggplot2 theme (default: theme_bw())
n.cores: integer Number of cores for the calculations (default = self$n.cores)
sep.meta: character Separator for metadata file (default = ",")
raw.meta: logical Keep metadata in its raw format. If FALSE, classes will be converted using "type.convert" (default = FALSE)
pal: character Plotting palette (default = NULL)

Returns

CRMetrics object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")

`CRMetrics$addDetailedMetrics()`

Function to read in detailed metrics (total UMI count and gene count per cell). This is not done upon object initialization for speed.

Usage

CRMetrics$addDetailedMetrics(
  cms = self$cms,
  min.transcripts.per.cell = 100,
  n.cores = self$n.cores,
  verbose = self$verbose
)

Arguments

cms: list List of (sparse) count matrices (default = self$cms)
min.transcripts.per.cell: numeric Minimal number of transcripts per cell (default = 100)
n.cores: integer Number of cores for the calculations (default = self$n.cores).
verbose: logical Print messages or not (default = self$verbose).

Returns

Count matrices

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Run function
crm$addDetailedMetrics()

`CRMetrics$plotBarcodeRankPlot()`

Read in raw count matrices and save UMI counts to object in "cellbender" slot, then create barcode rank plots for quality control

Usage

CRMetrics$plotBarcodeRankPlot(
  shrinkage = 100,
  show.expected.cells = TRUE,
  cms.raw = self$cms.raw,
  technology = self$technology,
  umi.counts = self$umi.counts,
  data.path = self$data.path,
  samples = self$metadata$sample,
  verbose = self$verbose,
  n.cores = self$n.cores,
  unique.names = FALSE,
  sep = "!!"
)

Arguments

shrinkage: integer Select every nth UMI count per cell for plotting. Improves plotting speed drastically. To plot all cells, set to 1 (default = 100)
show.expected.cells: logical Plot line depicting called cells by Parse or Cell Ranger pipeline (default = TRUE)
cms.raw: list Raw count matrices from HDF5 Cell Ranger or Parse outputs (default = self$cms.raw)
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))
umi.counts: list UMI counts calculated as column sums of raw count matrices from HDF5 Cell Ranger or Parse outputs (default: stored list)
data.path: character Path to Cell Ranger or Parse outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
verbose: logical Show progress (default: stored vector)
n.cores: integer Number of cores (default: stored vector)
unique.names: logical Create unique cell names (default = FALSE)
sep: character Separator for creating unique cell names (default = "!!")

Returns

ggplot2 object and bash script

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data")
crm$prepareCellbender()

`CRMetrics$getExpectedCells()`

Extract the expected number of cells per sample based on the Cell Ranger or Parse's split-pipe summary metrics.

Usage

CRMetrics$getExpectedCells(samples = self$metadata$sample)

Arguments

samples: character Sample names to include (default = self$metadata$sample)

Returns

A numeric vector

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Get summary
crm$addSummaryFromCms()

# Get no. cells
crm$getExpectedCells()

`CRMetrics$addComparison()`

Add comparison group for statistical testing.

Usage

CRMetrics$addComparison(comp.group, metadata = self$metadata)

Arguments

comp.group: character Comparison metric (default = self$comp.group).
metadata: data.frame Metadata for samples (default = self$metadata).

Returns

Vector

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add metadata
crm$metadata <- data.frame(sex = c("male","female"))

# Add comparison group
crm$addComparison(comp.group = "sex")

`CRMetrics$plotSamples()`

Plot the number of samples per group based on metadata.

Usage

CRMetrics$plotSamples(
  comp.group = self$comp.group,
  h.adj = 0.05,
  exact = FALSE,
  metadata = self$metadata,
  second.comp.group = NULL,
  pal = self$pal
)

Arguments

comp.group: character Metadata column used as the primary grouping variable. Must match a column name of metadata (default = self$comp.group).
h.adj: numeric Relative vertical offset for displaying the statistical test p-value in the plot, expressed as a % of the maximum y-value (default = 0.05).
exact: logical Whether to display exact p-values instead of rounded values (default = FALSE).
metadata: data.frame Metadata for samples (default = self$metadata).
second.comp.group: character Optional second metadata column used for grouping and coloring bars in the plot. Must match a column name of metadata (default = NULL).
pal: character Plotting palette (default = self$pal)

Returns

ggplot2 object

Examples

samples <- c("sample1", "sample2")

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})
names(testdata.cms) <- samples

# Create metadata
metadata <- data.frame(sample = samples,
sex = c("male","female"),
condition = c("a","b"))

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, metadata = metadata, n.cores = 1)

# Plot
crm$plotSamples(comp.group = "sex", second.comp.group = "condition")

`CRMetrics$plotSummaryMetrics()`

Plot all summary stats or a selected list.

Usage

CRMetrics$plotSummaryMetrics(
  comp.group = self$comp.group,
  second.comp.group = NULL,
  metrics = NULL,
  h.adj = 0.05,
  plot.stat = TRUE,
  stat.test = c("non-parametric", "parametric"),
  exact = FALSE,
  metadata = self$metadata,
  summary.metrics = self$summary.metrics,
  plot.geom = "bar",
  se = FALSE,
  group.reg.lines = FALSE,
  secondary.testing = TRUE,
  pal = self$pal
)

Arguments

comp.group: character Defines the groups to compare (categorical) or the variable for regression analysis (continuous).Must match a column name of metadata (default = self$comp.group).
second.comp.group: character Optional metadata column used for grouping, colouring or stratifying the plot. Usually categorical. Used for the metric "samples per group" and for grouped regression when "comp.group" is continuous Does not affect statistical testing (default = NULL).
metrics: character Vector of metrics to plot. If NULL, all available metrics are plotted (default = NULL).
h.adj: numeric Relative vertical offset for displaying the statistical test p-value in the plot, expressed as % of the maximum y-value (default = 0.05)
plot.stat: logical Whether to show statistical test results in the plot. Automatically set to FALSE if comp.group is "sample" or continuous (default = TRUE)
stat.test: character Statistical test to compare group means. Can either be "non-parametric" or "parametric" (default = "non-parametric").
exact: logical Whether to display exact p-values instead of rounded values (default = FALSE).
metadata: data.frame Metadata for samples (default = self$metadata).
summary.metrics: data.frame Data frame of precomputed summary metrics for the samples (default = self$summary.metrics).
plot.geom: character Geometric used to plot the data. Options include "bar", "point", "violin", "histogram" (default = "bar").
se: logical For regression lines, whether to display standard errors (default = FALSE)
group.reg.lines: logical For regression lines, if FALSE show a single line, if TRUE show one line per group defined by second.comp.group (default = FALSE)
secondary.testing: logical Whether to show post hoc testing which is performed in case of multiple-group comparisons (default = TRUE)
pal: character Plotting palette (default = self$pal)

Returns

ggplot2 object

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add summary metrics
crm$addSummaryFromCms()

crm$plotSummaryMetrics(plot.geom = "point")

`CRMetrics$plotDetailedMetrics()`

Plot detailed metrics from the detailed.metrics object

Usage

CRMetrics$plotDetailedMetrics(
  comp.group = self$comp.group,
  detailed.metrics = self$detailed.metrics,
  metadata = self$metadata,
  metrics = NULL,
  plot.geom = "violin",
  hline = TRUE,
  pal = self$pal
)

Arguments

comp.group: character Comparison metric (default = self$comp.group).
detailed.metrics: data.frame Object containing the count matrices (default = self$detailed.metrics).
metadata: data.frame Metadata for samples (default = self$metadata).
metrics: character Metrics to plot. NULL plots both plots (default = NULL).
plot.geom: character How to plot the data (default = "violin").
hline: logical Whether to show median as horizontal line (default = TRUE)
pal: character Plotting palette (default = self$pal)
data.path: character Path to Cell Ranger count data (default = self$data.path).

Returns

ggplot2 object

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add detailed metrics
crm$addDetailedMetrics()

# Plot
crm$plotDetailedMetrics()

`CRMetrics$plotEmbedding()`

Plot cells in an embedding using Conos, with coloring based on depth (= total UMI count per cell), doublet status, or mitochondrial gene fraction. Users can either specify cutoff thresholds to highlight filtered cells or plot the raw continuous values on the UMAP.

Usage

CRMetrics$plotEmbedding(
  doublet.method = NULL,
  doublet.scores = FALSE,
  doublet.score.threshold = NULL,
  depth = FALSE,
  depth.cutoff = NULL,
  depth.cutoff.upper = NULL,
  mito.frac = FALSE,
  mito.cutoff = NULL,
  species = c("human", "mouse"),
  size = 0.3,
  sep = "!!",
  pal = NULL,
  ...
)

Arguments

doublet.method: character Colour cells based on doublet calling result of respective doublet detection method (default = NULL).
doublet.scores: logical Colour cells by doublet score. Applied doublet method must be supplied in parameter 'doublet.method' (default = FALSE).
doublet.score.threshold: numeric Set manual threshold for doublets. If NULL and doublet.scores = TRUE, continuous scores are plotted. Applied doublet method must be supplied in parameter 'doublet.method' (default = NULL).
depth: logical Colour cells by depth. If TRUE and no cutoffs are supplied, continuous depth values are plotted (default = FALSE).
depth.cutoff: numeric Lower depth cutoff. Can be a single value or a named numeric vector specifying cutoffs per sample (default = NULL).
depth.cutoff.upper: numeric Upper depth cutoff. Can be a single value or a named numeric vector specifying cutoffs per sample (default = NULL).
mito.frac: logical Plot mitochondrial fraction. If TRUE and mito.cutoff is NULL, continuous values are plotted (default = FALSE).
mito.cutoff: numeric Mitochondrial fraction cutoff (default = NULL).
species: character Species to calculate the mitochondrial fraction for (default = c("human","mouse")).
size: numeric Dot size (default = 0.3)
sep: character Separator for creating unique cell names (default = "!!")
pal: character Plotting palette (default = NULL)
...: Plotting parameters passed to sccore::embeddingPlot.

Returns

ggplot2 object

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding() 

crm$plotEmbedding()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$plotDepth()`

Plot per-sample density histograms of cell depths (=total UMI counts per cell). The plot highlights cells below or above a specified UMI cutoff with distinct colors, helping to assess data quality and filtering thresholds.

Usage

CRMetrics$plotDepth(
  cutoff = 1000,
  samples = self$metadata$sample,
  sep = "!!",
  keep.col = "#E7CDC2",
  filter.col = "#A65141"
)

Arguments

cutoff: numeric Depth cutoff (lower). Can be a single value or a named numeric vector specifying cutoffs per sample (default = 1e3).
samples: character Sample names to include for plotting (default = $metadata$sample).
sep: character Separator for creating unique cell names (default = "!!")
keep.col: character Color for density of cells that are kept (default = "#E7CDC2")
filter.col: Character Color for density of cells to be filtered (default = "#A65141")

Returns

ggplot2 object

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Plot
crm$plotDepth()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$plotRawDepth()`

Plot per-sample histograms of total UMI counts per cell (= cell depth), allowing visualization of sequencing depth distributions across samples.

Usage

CRMetrics$plotRawDepth(
  cutoff = 1000,
  samples = self$metadata$sample,
  sep = "!!",
  binwidth = 10,
  xupper = 20000
)

Arguments

cutoff: numeric Depth cutoff (lower) used to distinguish filtered and retained cells. Can be a single value or a named numeric vector specifying cutoffs per sample (Default = 1e3).
samples: character Vector of sample names to include in the plot (default = self$metadata$sample).
sep: character Separator for creating unique cell names (default = "!!")
binwidth: numeric Width of the histogram bins (default = 10).
xupper: numeric Optional upper x-axis limit (default = 2e4).

Returns

A combined ggplot object showing UMI count histograms per sample.

`CRMetrics$plotMitoFraction()`

Plot the mitochondrial fraction in histogram.

Usage

CRMetrics$plotMitoFraction(
  cutoff = 0.05,
  species = c("human", "mouse"),
  samples = self$metadata$sample,
  sep = "!!",
  keep.col = "#E7CDC2",
  filter.col = "#A65141"
)

Arguments

cutoff: numeric The mito. fraction cutoff to color the embedding (default = 0.05)
species: character Species to calculate the mitochondrial fraction for (default = "human")
samples: character Sample names to include for plotting (default = $metadata$sample)
sep: character Separator for creating unique cell names (default = "!!")
keep.col: character Color for density of cells that are kept (default = "#E7CDC2")
filter.col: Character Color for density of cells to be filtered (default = "#A65141")

Returns

ggplot2 object

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Plot
crm$plotMitoFraction()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$detectDoublets()`

Run doublet detection methods.

Usage

CRMetrics$detectDoublets(
  method = c("scrublet", "doubletdetection", "scdblfinder"),
  cms = self$cms,
  samples = self$metadata$sample,
  env = "r-reticulate",
  conda.path = system("whereis conda"),
  n.cores = self$n.cores,
  verbose = self$verbose,
  args = list(),
  export = FALSE,
  data.path = self$data.path
)

Arguments

method: character Which method to use, scrublet, doubletdetection or scdblfinder (default="scrublet").
cms: list List containing the count matrices (default=self$cms).
samples: character Vector of sample names. If NULL, samples are extracted from cms (default = self$metadata$sample)
env: character Environment to run python in (default="r-reticulate").
conda.path: character Path to conda environment (default=system("whereis conda")).
n.cores: integer Number of cores to use (default = self$n.cores)
verbose: logical Print messages or not (default = self$verbose)
args: list A list with additional arguments for DoubletDetection, scrublet or scDblFinder. Please check the respective manuals.
export: boolean Export count matrices as transposed .mtx files and save python script to run doublet detection outside R (default = FALSE)
data.path: character Path to write data, only relevant if export = TRUE. Last character must be / (default = self$data.path)

Returns

data.frame

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)


# Detect doublets
crm$detectDoublets(method = "scrublet", 
conda.path = "/opt/software/miniconda/4.12.0/condabin/conda")

`CRMetrics$doPreprocessing()`

Perform conos preprocessing.

Usage

CRMetrics$doPreprocessing(
  cms = self$cms,
  preprocess = c("pagoda2", "seurat"),
  min.transcripts.per.cell = 100,
  verbose = self$verbose,
  n.cores = self$n.cores,
  get.largevis = FALSE,
  tsne = FALSE,
  make.geneknn = FALSE,
  cluster = FALSE,
  ...
)

Arguments

cms: list List containing the count matrices (default = self$cms).
preprocess: character Method to use for preprocessing (default = c("pagoda2","seurat")).
min.transcripts.per.cell: numeric Minimal transcripts per cell (default = 100)
verbose: logical Print messages or not (default = self$verbose).
n.cores: integer Number of cores for the calculations (default = self$n.cores).
get.largevis: logical For Pagoda2, create largeVis embedding (default = FALSE)
tsne: logical Create tSNE embedding (default = FALSE)
make.geneknn: logical For Pagoda2, estimate gene kNN (default = FALSE)
cluster: logical For Seurat, estimate clusters (default = FALSE)
...: Additional arguments for Pagaoda2::basicP2Proc or conos:::basicSeuratProc

Returns

Conos object

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Perform preprocessing
crm$doPreprocessing(preprocess = "pagoda2")
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$createEmbedding()`

Create Conos embedding.

Usage

CRMetrics$createEmbedding(
  cms = self$cms.preprocessed,
  verbose = self$verbose,
  n.cores = self$n.cores,
  arg.buildGraph = list(),
  arg.findCommunities = list(),
  arg.embedGraph = list(method = "UMAP")
)

Arguments

cms: list List containing the preprocessed count matrices (default = self$cms.preprocessed).
verbose: logical Print messages or not (default = self$verbose).
n.cores: integer Number of cores for the calculations (default = self$n.cores).
arg.buildGraph: list A list with additional arguments for the buildGraph function in Conos (default = list())
arg.findCommunities: list A list with additional arguments for the findCommunities function in Conos (default = list())
arg.embedGraph: list A list with additional arguments for the embedGraph function in Conos (default = list(method = "UMAP"))

Returns

Conos object

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$filterCms()`

Filter cells based on depth, mitochondrial fraction and doublets from the count matrix.

Usage

CRMetrics$filterCms(
  depth.cutoff = NULL,
  depth.cutoff.upper = NULL,
  mito.cutoff = NULL,
  doublet.method = NULL,
  doublet.score.threshold = NULL,
  species = c("human", "mouse"),
  samples.to.exclude = NULL,
  verbose = self$verbose,
  sep = "!!",
  raw = FALSE
)

Arguments

depth.cutoff: numeric Lower depth (total UMI counts per cell) cutoff (default = NULL).
depth.cutoff.upper: numeric Upper depth (total UMI counts per cell) cutoff (default = NULL).
mito.cutoff: numeric Mitochondrial fraction cutoff (default = NULL).
doublet.method: character Doublet detection method to use (default = NULL).
doublet.score.threshold: Manually set threshold for doublets across all samples. Cells with a doublet score above the threshold are declared doublets (default = NULL).
species: character Species to calculate the mitochondrial fraction for (default = "human").
samples.to.exclude: character Sample names to exclude (default = NULL)
verbose: logical Show progress (default = self$verbose)
sep: character Separator for creating unique cell names (default = "!!")
raw: boolean Filter on raw, unfiltered count matrices. Usually not intended (default = FALSE)

Returns

list of filtered count matrices

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()


# Filter CMs
crm$filterCms(depth.cutoff = 1e3, mito.cutoff = 0.05)
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$selectMetrics()`

Select metrics from summary.metrics

Usage

CRMetrics$selectMetrics(ids = NULL)

Arguments

ids: character Metric id to select (default = NULL).

Returns

vector

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Select metrics
crm$selectMetrics()
selection.metrics <- crm$selectMetrics(c(1:4))

`CRMetrics$plotFilteredCells()`

Plot filtered cells in an embedding, in a bar plot, on a tile or export the data frame

Usage

CRMetrics$plotFilteredCells(
  type = c("embedding", "bar", "tile", "export"),
  depth = FALSE,
  depth.cutoff = NULL,
  depth.cutoff.upper = NULL,
  doublet.method = NULL,
  doublet.score.threshold = NULL,
  mito.frac = FALSE,
  mito.cutoff = NULL,
  species = c("human", "mouse"),
  size = 0.3,
  sep = "!!",
  cols = c("grey80", "red", "blue", "green", "yellow", "black", "pink", "purple"),
  ...
)

Arguments

type: character The type of plot to use: embedding, bar, tile or export (default = c("embedding","bar","tile","export")).
depth: logical Plot the depth (= total amount of UMI counts per cell) or not (default = FALSE).
depth.cutoff: numeric Lower depth cutoff, either a single number or a vector with cutoff per sample and with sample IDs as names (default = NULL).
depth.cutoff.upper: numeric Upper depth cutoff, either a single number or a vector with cutoff per sample and with sample IDs as names (default = NULL).
doublet.method: character Method to detect doublets (default = NULL).
doublet.score.threshold: Manually set threshold for doublets across all samples. Cells with a doublet score above the threshold are declared doublets. (default = NULL)
mito.frac: logical Plot the mitochondrial fraction or not (default = FALSE).
mito.cutoff: numeric Mitochondrial fraction cutoff, either a single number or a vector with cutoff per sample and with sample IDs as names (default = NULL).
species: character Species to calculate the mitochondrial fraction for (default = c("human","mouse")).
size: numeric Dot size (default = 0.3)
sep: character Separator for creating unique cell names (default = "!!")
cols: character Colors used for plotting (default = c("grey80","red","blue","green","yellow","black","pink","purple"))
...: Plotting parameters passed to sccore::embeddingPlot.

Returns

ggplot2 object or data frame

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Plot and extract result
crm$plotFilteredCells(type = "embedding")
filtered.cells <- crm$plotFilteredCells(type = "export")
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$getDepth()`

Extract sum of UMI counts per cell (= cell depth)

Usage

CRMetrics$getDepth(cms = self$cms)

Arguments

cms: list List of (sparse) count matrices (default = self$cms)

Returns

data frame

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Get depth
crm$getDepth()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$getMitoFraction()`

Calculate the fraction of mitochondrial genes.

Usage

CRMetrics$getMitoFraction(species = c("human", "mouse"), cms = self$cms)

Arguments

species: character Species to calculate the mitochondrial fraction for (default = "human").
cms: list List of (sparse) count matrices (default = self$cms)

Returns

data frame

Examples

if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Get mito. fraction
crm$getMitoFraction(species = c("human", "mouse"))
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}

`CRMetrics$prepareCellbender()`

Generates input files for CellBender from Parse's split-pipe output.

Usage

CRMetrics$prepareCellbender(
  technology = self$technology,
  data.path = self$data.path,
  samples = self$metadata$sample,
  verbose = self$verbose,
  n.cores = self$n.cores,
  unique.names = FALSE,
  sep = "!!"
)

Arguments

technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))
data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
verbose: logical Show progress (default: stored vector)
n.cores: integer Number of cores (default: stored vector)
unique.names: logical Create unique cell names (default = FALSE)
sep: character Separator for creating unique cell names (default = "!!")

Returns

Saves files to disc.

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data")
crm$prepareCellbender()

`CRMetrics$saveCellbenderScript()`

Create CellBender script

Usage

CRMetrics$saveCellbenderScript(
  file = "cellbender_script.sh",
  technology = self$technology,
  fpr = 0.01,
  epochs = 150,
  use.gpu = TRUE,
  expected.cells = NULL,
  total.droplets = NULL,
  data.path = self$data.path,
  samples = self$metadata$sample,
  args = NULL
)

Arguments

file: character File name for CellBender script. Will be stored in data.path (default: "cellbender_script.sh")
technology: character Applied single cell technology (default = self$technology).
fpr: numeric False positive rate for CellBender (default = 0.01)
epochs: integer Number of epochs for CellBender (default = 150)
use.gpu: logical Use CUDA capable GPU (default = TRUE)
expected.cells: named numeric vector By default, the –expected-cells argument is omitted in the generated script. Otherwise, a named vector of expected cell numbers with sample IDs as names. Sample IDs must match those in summary.metrics (default = NULL).
total.droplets: named numeric vector By default, the –total-droplets-included argument is omitted in the generated script. Otherwise, a named vector of total droplets included with sample IDs as names. Sample IDs must match those in summary.metrics (default = NULL).
data.path: character Path to Cell Ranger or Parse's split-pipe outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
args: character (optional) Additional parameters for CellBender

Returns

bash script

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()

`CRMetrics$addCms()`

Add a list of count matrices to the CRMetrics object.

Usage

CRMetrics$addCms(
  cms = NULL,
  data.path = self$data.path,
  technology = self$technology,
  samples = self$metadata$sample,
  cellbender = FALSE,
  raw = FALSE,
  symbol = TRUE,
  unique.names = TRUE,
  sep = "!!",
  add.metadata = TRUE,
  n.cores = self$n.cores,
  verbose = self$verbose
)

Arguments

cms: list List of (sparse) count matrices (default = NULL)
data.path: character Path to Cell Ranger or Parse's split-pipe count data (default = self$data.path).
technology: character Applied single cell technology (default = self$technology).
samples: character Vector of sample names. If NULL, samples are extracted from cms (default = self$metadata$sample)
cellbender: logical Add CellBender filtered count matrices in HDF5 format. Requires that "cellbender" is in the names of the files (default = FALSE)
raw: logical Add raw count matrices from Cell Ranger or Parse's split-pipe output. Cannot be combined with cellbender=TRUE. (default = FALSE)
symbol: character The type of gene IDs to use, SYMBOL (TRUE) or ENSEMBLE (default = TRUE)
unique.names: logical Make cell names unique based on sep parameter (default = TRUE)
sep: character Separator used to create unique cell names (default = "!!")
add.metadata: boolean Add metadata from cms or not (default = TRUE)
n.cores: integer Number of cores to use (default = self$n.cores)
verbose: boolean Print progress (default = self$verbose)

Returns

Add list of (sparse) count matrices to R6 class object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

crm$addCms(cms = testdata.cms)

`CRMetrics$plotCbTraining()`

Plot the results from the CellBender estimations

Usage

CRMetrics$plotCbTraining(
  data.path = self$data.path,
  samples = self$metadata$sample,
  technology = self$technology,
  pal = self$pal
)

Arguments

data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))
pal: character Plotting palette (default = self$pal)

Returns

A ggplot2 object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbTraining()

`CRMetrics$plotCbCellProbs()`

Plot the CellBender assigned cell probabilities

Usage

CRMetrics$plotCbCellProbs(
  data.path = self$data.path,
  samples = self$metadata$sample,
  technology = self$technology,
  low.col = "gray",
  high.col = "red"
)

Arguments

data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))
low.col: character Color for low probabilities (default = "gray")
high.col: character Color for high probabilities (default = "red")

Returns

A ggplot2 object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run the CellBender script
crm$plotCbCellProbs()

`CRMetrics$plotCbAmbExp()`

Plot the estimated ambient gene expression per sample from CellBender calculations

Usage

CRMetrics$plotCbAmbExp(
  cutoff = 0.005,
  data.path = self$data.path,
  samples = self$metadata$sample,
  technology = self$technology
)

Arguments

cutoff: numeric Horizontal line included in the plot to indicate highly expressed ambient genes (default = 0.005)
data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))

Returns

A ggplot2 object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbAmbExp()

`CRMetrics$plotCbAmbGenes()`

Plot the most abundant estimated ambient genes from the CellBender calculations

Usage

CRMetrics$plotCbAmbGenes(
  cutoff = 0.005,
  data.path = self$data.path,
  samples = self$metadata$sample,
  technology = self$technology,
  pal = self$pal
)

Arguments

cutoff: numeric Cutoff of ambient gene expression to use to extract ambient genes per sample
data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse"))
pal: character Plotting palette (default = self$pal)

Returns

A ggplot2 object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbAmbGenes()

`CRMetrics$addSummaryFromCms()`

Add summary metrics from a list of count matrices

Usage

CRMetrics$addSummaryFromCms(
  cms = self$cms,
  n.cores = self$n.cores,
  verbose = self$verbose
)

Arguments

cms: list A list of filtered count matrices (default = self$cms)
n.cores: integer Number of cores to use (default = self$n.cores)
verbose: logical Show progress (default = self$verbose)

Returns

data.frame

Examples

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add summary
crm$addSummaryFromCms()

`CRMetrics$runSoupX()`

Run SoupX ambient RNA estimation and correction

Usage

CRMetrics$runSoupX(
  data.path = self$data.path,
  samples = self$metadata$sample,
  n.cores = self$n.cores,
  verbose = self$verbose,
  arg.load10X = list(),
  arg.autoEstCont = list(),
  arg.adjustCounts = list()
)

Arguments

data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
n.cores: numeric Number of cores (default = self$n.cores)
verbose: logical Show progress (default = self$verbose)
arg.load10X: list A list with additional parameters for SoupX::load10X (default = list())
arg.autoEstCont: list A list with additional parameters for SoupX::autoEstCont (default = list())
arg.adjustCounts: list A list with additional parameters for SoupX::adjustCounts (default = list())

Returns

List containing a list with corrected counts, and a data.frame containing plotting estimations

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$runSoupX()

`CRMetrics$plotSoupX()`

Plot the results from the SoupX estimations

Usage

CRMetrics$plotSoupX(plot.df = self$soupx$plot.df)

Arguments

plot.df: data.frame SoupX estimations (default = self$soupx$plot.df)

Returns

A ggplot2 object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$runSoupX()
crm$plotSoupX()

`CRMetrics$plotCbCells()`

Plot CellBender cell estimations against the estimated cell numbers from Cell Ranger

Usage

CRMetrics$plotCbCells(
  data.path = self$data.path,
  samples = self$metadata$sample,
  technology = self$technology,
  pal = self$pal
)

Arguments

data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
technology: character applied single cell technology (default = c("10x", "10xflex", "10xmultiome", "parse")) description
pal: character Plotting palette (default = self$pal)

Returns

A ggplot2 object

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbCells()

`CRMetrics$addDoublets()`

Add doublet results created from exported Python script

Usage

CRMetrics$addDoublets(
  method = c("scrublet", "doubletdetection"),
  data.path = self$data.path,
  samples = self$metadata$sample,
  cms = self$cms,
  verbose = self$verbose
)

Arguments

method: character Which method to use, either scrublet or doubletdetection (default is both).
data.path: character Path to Cell Ranger outputs (default = self$data.path)
samples: character Sample names to include (default = self$metadata$sample)
cms: list List containing the count matrices (default = self$cms).
verbose: boolean Print progress (default = self$verbose)

Returns

List of doublet results

Examples

crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$detectDoublets(export = TRUE)
## Run Python script
crm$addDoublets()

`CRMetrics$clone()`

The objects of this class are cloneable with this method.

Usage

CRMetrics$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `CRMetrics$new()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$addDetailedMetrics()`
## ------------------------------------------------

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Run function
crm$addDetailedMetrics()

## ------------------------------------------------
## Method `CRMetrics$plotBarcodeRankPlot()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data")
crm$prepareCellbender()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$getExpectedCells()`
## ------------------------------------------------

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Get summary
crm$addSummaryFromCms()

# Get no. cells
crm$getExpectedCells()

## ------------------------------------------------
## Method `CRMetrics$addComparison()`
## ------------------------------------------------

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add metadata
crm$metadata <- data.frame(sex = c("male","female"))

# Add comparison group
crm$addComparison(comp.group = "sex")

## ------------------------------------------------
## Method `CRMetrics$plotSamples()`
## ------------------------------------------------

samples <- c("sample1", "sample2")

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})
names(testdata.cms) <- samples

# Create metadata
metadata <- data.frame(sample = samples,
sex = c("male","female"),
condition = c("a","b"))

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, metadata = metadata, n.cores = 1)

# Plot
crm$plotSamples(comp.group = "sex", second.comp.group = "condition")

## ------------------------------------------------
## Method `CRMetrics$plotSummaryMetrics()`
## ------------------------------------------------


# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add summary metrics
crm$addSummaryFromCms()

crm$plotSummaryMetrics(plot.geom = "point")


## ------------------------------------------------
## Method `CRMetrics$plotDetailedMetrics()`
## ------------------------------------------------


# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add detailed metrics
crm$addDetailedMetrics()

# Plot
crm$plotDetailedMetrics()


## ------------------------------------------------
## Method `CRMetrics$plotEmbedding()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding() 

crm$plotEmbedding()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$plotDepth()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Plot
crm$plotDepth()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$plotMitoFraction()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Plot
crm$plotMitoFraction()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$detectDoublets()`
## ------------------------------------------------

## Not run: 
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)


# Detect doublets
crm$detectDoublets(method = "scrublet", 
conda.path = "/opt/software/miniconda/4.12.0/condabin/conda")

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$doPreprocessing()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Perform preprocessing
crm$doPreprocessing(preprocess = "pagoda2")
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$createEmbedding()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$filterCms()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()


# Filter CMs
crm$filterCms(depth.cutoff = 1e3, mito.cutoff = 0.05)
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$selectMetrics()`
## ------------------------------------------------

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Select metrics
crm$selectMetrics()
selection.metrics <- crm$selectMetrics(c(1:4))

## ------------------------------------------------
## Method `CRMetrics$plotFilteredCells()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Plot and extract result
crm$plotFilteredCells(type = "embedding")
filtered.cells <- crm$plotFilteredCells(type = "export")
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$getDepth()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Get depth
crm$getDepth()
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$getMitoFraction()`
## ------------------------------------------------


if (requireNamespace("pagoda2", quietly = TRUE)) {
if (requireNamespace("conos", quietly = TRUE)) {
# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Create embedding
crm$doPreprocessing()
crm$createEmbedding()

# Get mito. fraction
crm$getMitoFraction(species = c("human", "mouse"))
} else {
message("Package 'conos' not available.")
}
} else {
message("Package 'pagoda2' not available.")
}


## ------------------------------------------------
## Method `CRMetrics$prepareCellbender()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data")
crm$prepareCellbender()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$saveCellbenderScript()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$addCms()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

crm$addCms(cms = testdata.cms)

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$plotCbTraining()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbTraining()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$plotCbCellProbs()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run the CellBender script
crm$plotCbCellProbs()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$plotCbAmbExp()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbAmbExp()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$plotCbAmbGenes()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbAmbGenes()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$addSummaryFromCms()`
## ------------------------------------------------

# Simulate data
testdata.cms <- lapply(seq_len(2), \(x) {
out <- Matrix::rsparsematrix(2e3, 1e3, 0.1)
out[out < 0] <- 1
dimnames(out) <- list(sapply(seq_len(2e3), \(x) paste0("gene",x)),
sapply(seq_len(1e3), \(x) paste0("cell",x)))
return(out)
})

# Initialize
crm <- CRMetrics$new(cms = testdata.cms, samples = c("sample1", "sample2"), n.cores = 1)

# Add summary
crm$addSummaryFromCms()

## ------------------------------------------------
## Method `CRMetrics$runSoupX()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$runSoupX()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$plotSoupX()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$runSoupX()
crm$plotSoupX()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$plotCbCells()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
## Run CellBender script
crm$plotCbCells()

## End(Not run)

## ------------------------------------------------
## Method `CRMetrics$addDoublets()`
## ------------------------------------------------

## Not run: 
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$detectDoublets(export = TRUE)
## Run Python script
crm$addDoublets()

## End(Not run)

Add detailed metrics (= total UMI count and gene count per cell).

Description

Add detailed metrics, uses not preprocessed count matrices.

Usage

addDetailedMetricsInner(cms, verbose = TRUE, n.cores = 1)

Arguments

cms

List containing the count matrices.

verbose

Print messages (default = TRUE).

n.cores

Number of cores for the calculations (default = 1).

Value

data frame

Add statistics to plot

Description

Use ggpubr to add statistics to plots.

Usage

addPlotStats(
  p,
  comp.group,
  metadata,
  h.adj = 0.05,
  primary.test,
  secondary.test,
  exact = FALSE
)

Arguments

p

Plot to add statistics to.

comp.group

Comparison metric.

metadata

Metadata for samples.

h.adj

Position of statistics test p value as % of max(y) (default = 0.05).

primary.test

Primary statistical test, e.g. "anova", "kruskal.test".

secondary.test

Secondary statistical test, e.g. "t-test", "wilcox.test"

exact

Whether to display exact p-values instead of rounded values (default = FALSE).

Value

ggplot2 object

Add statistics to plot

Description

Use ggpubr to add statistics to samples or plot

Usage

addPlotStatsSamples(
  p,
  comp.group,
  metadata,
  h.adj = 0.05,
  exact = FALSE,
  second.comp.group
)

Arguments

p

Plot to add statistics to.

comp.group

Metadata column used as the primary grouping variable. Must match a column name of metadata (default = self$comp.group).

metadata

Metadata for samples.

h.adj

Relative vertical offset for displaying the statistical test p-value in the plot, expressed as a % of the maximum y-value (default = 0.05).

exact

Whether to display exact p-values instead of rounded values (default = FALSE).

second.comp.group

Second comparison metric.

Value

ggplot2 object

Add summary metrics

Description

Add summary metrics by reading Cell Ranger or Parse's split-pipe metrics summary files.

Usage

addSummaryMetrics(data.path, metadata, n.cores = 1, verbose = TRUE)

Arguments

data.path

Path to count data.

metadata

Metadata for samples.

n.cores

Number of cores for the calculations (default = 1).

verbose

Print messages (default = TRUE).

Value

data frame

Set correct 'comp.group' parameter

Description

Set comp.group to 'category' if null.

Usage

checkCompGroup(comp.group, category, verbose = TRUE)

Arguments

comp.group

Comparison metric.

category

Comparison metric to use if comp.group is not provided.

verbose

Print messages (default = TRUE).

Value

vector

Check whether 'comp.group' is a column name in metadata

Description

Checks whether 'comp.group' is any of the column names in metadata.

Usage

checkCompMeta(comp.group, metadata)

Arguments

comp.group

Grouping variable.

metadata

Metadata for samples.

Value

nothing or stop

Check data path

Description

Helper function to check that data.path is not NULL

Usage

checkDataPath(data.path)

Arguments

data.path

character Path to be checked

Create unique cell names

Description

Create unique cell names from sample IDs and cell IDs

Usage

createUniqueCellNames(cms, samples, sep = "!!")

Arguments

cms

list List of count matrices, should be named (optional)

samples

character Optional, list of sample names

sep

character Separator between sample IDs and cell IDs (default = "!!")

Create filtering vector

Description

Create logical filtering vector based on a numeric vector and a (sample-wise) cutoff

Usage

filterVector(num.vec, name, filter, samples, sep = "!!")

Arguments

num.vec

numeric Numeric vector to create filter on

name

character Name of filter

filter

numeric Either a single numeric value or a numeric value with length of samples

samples

character Sample IDs

sep

character Separator to split cells by into sample-wise lists (default = "!!")

Get H5 file paths

Description

Get file paths for H5 files

Usage

getH5Paths(data.path, samples = NULL, type = NULL, technology = NULL)

Arguments

data.path

character Path for directory containing sample-wise directories with Cell Ranger count/Parse outputs

samples

character Sample names to include (default = NULL)

type

character Type of H5 files to get paths for, one of "raw", "filtered" (Cell Ranger count outputs, does not work for Parse), "cellbender" (raw CellBender outputs), "cellbender_filtered" (CellBender filtered outputs) (default = "type")

technology

character Used technology ("parse", "10x", "10xflex", "10xmultiome") (default = NULL)

Get labels for percentage of filtered cells

Description

Labels the percentage of filtered cells based on mitochondrial fraction, sequencing depth and doublets as low, medium or high

Usage

labelsFilter(filter.data)

Arguments

filter.data

Data frame containing the mitochondrial fraction, depth and doublets per sample.

Value

data frame

Calculate percentage of filtered cells

Description

Calculate percentage of filtered cells based on the filter

Usage

percFilter(filter.data, filter = "mito", no.vars = 1)

Arguments

filter.data

Data frame containing the mitochondrial fraction, depth and doublets per sample.

filter

The variable to filter (default = "mito")

no.vars

numeric Number of variables (default = 1)

Value

vector

Plot the data as points, as bars as a histogram, or as a violin

Description

Plot the data as points, barplot, histogram or violin

Usage

plotGeom(g, plot.geom, col, pal = NULL)

Arguments

g

ggplot2 object

plot.geom

The plot.geom to use, "point", "bar", "histogram", or "violin".

pal

character Palette (default = NULL)

Value

geom

Load 10x count matrices

Description

Load gene expression count data

Usage

read10x(
  data.path,
  samples = NULL,
  raw = FALSE,
  symbol = TRUE,
  sep = "!!",
  unique.names = TRUE,
  n.cores = 1,
  verbose = TRUE
)

Arguments

data.path

Path to cellranger count data.

samples

Vector of sample names (default = NULL)

raw

logical Add raw count matrices (default = FALSE)

symbol

The type of gene IDs to use, SYMBOL (TRUE) or ENSEMBLE (default = TRUE).

sep

Separator for cell names (default = "!!").

n.cores

Number of cores for the calculations (default = 1).

verbose

Print messages (default = TRUE).

Value

data frame

Examples

## Not run: 
cms <- read10x(data.path = "/path/to/count/data", 
samples = crm$metadata$samples, 
raw = FALSE, 
symbol = TRUE, 
n.cores = crm$n.cores)

## End(Not run)

Read 10x or cellbenders HDF5 files

Description

Read 10x or cellbenders HDF5 files

Usage

read10xH5(
  data.path,
  samples = NULL,
  type = c("raw", "filtered", "cellbender", "cellbender_filtered"),
  technology = c("10x", "10xmultiome", "flex"),
  symbol = TRUE,
  sep = "!!",
  n.cores = 1,
  verbose = TRUE,
  unique.names = FALSE
)

Arguments

data.path

character

samples

character vector, select specific samples for processing (default = NULL)

type

name of H5 file to search for, "raw" and "filtered" are Cell Ranger count outputs, "cellbender" is output from CellBender after running script from saveCellbenderScript

technology

character Used single-cell technology (c("10x", "10xmultiome", "flex"))

symbol

logical Use gene SYMBOLs (TRUE) or ENSEMBL IDs (FALSE) (default = TRUE)

sep

character Separator for creating unique cell names from sample IDs and cell IDs (default = "!!")

n.cores

integer Number of cores (default = 1)

verbose

logical Print progress (default = TRUE)

unique.names

logical Create unique cell IDs (default = FALSE)

Value

list with sparse count matrices

Examples

## Not run: 
cms.h5 <- read10xH5(data.path = "/path/to/count/data")

## End(Not run)

Load 10x flex count matrices

Description

Load gene expression count data

Usage

readFlex(
  data.path,
  samples = NULL,
  raw = FALSE,
  symbol = TRUE,
  sep = "!!",
  unique.names = TRUE,
  n.cores = 1,
  verbose = TRUE
)

Arguments

data.path

Path to cellranger count data.

samples

Vector of sample names (default = NULL)

raw

logical Add raw count matrices (default = FALSE)

symbol

The type of gene IDs to use, SYMBOL (TRUE) or ENSEMBLE (default = TRUE).

sep

Separator for cell names (default = "!!").

n.cores

Number of cores for the calculations (default = 1).

verbose

Print messages (default = TRUE).

Value

data frame

Examples

## Not run: 
cms <- readFlex(data.path = "/path/to/count/data", 
samples = crm$metadata$samples, 
raw = FALSE, 
symbol = TRUE, 
n.cores = crm$n.cores)

## End(Not run)

Load Parse count matrices

Description

Load gene expression count data

Usage

readParse(
  data.path,
  samples = NULL,
  raw = FALSE,
  sep = "!!",
  unique.names = TRUE,
  n.cores = 1,
  verbose = TRUE
)

Arguments

data.path

Path to Parse count data.

samples

Vector of sample names (default = NULL)

raw

logical Add raw count matrices (default = FALSE)

sep

Separator for cell names (default = "!!").

n.cores

Number of cores for the calculations (default = 1).

verbose

Print messages (default = TRUE).

Value

data frame

Package {CRMetrics}

CRMetrics class object

Description

Public fields

Methods

Public methods

CRMetrics$new()

Usage

Arguments

Returns

Examples

CRMetrics$addDetailedMetrics()

Usage

Arguments

Returns

Examples

CRMetrics$plotBarcodeRankPlot()

Usage

Arguments

Returns

Examples

CRMetrics$getExpectedCells()

Usage

Arguments

Returns

Examples

CRMetrics$addComparison()

Usage

Arguments

Returns

Examples

CRMetrics$plotSamples()

Usage

Arguments

Returns

Examples

CRMetrics$plotSummaryMetrics()

Usage

Arguments

Returns

Examples

CRMetrics$plotDetailedMetrics()

Usage

Arguments

Returns

Examples

CRMetrics$plotEmbedding()

Usage

Arguments

Returns

Examples

CRMetrics$plotDepth()

Usage

Arguments

Returns

Examples

CRMetrics$plotRawDepth()

Usage

Arguments

Returns

CRMetrics$plotMitoFraction()

Usage

Arguments

Returns

Examples

CRMetrics$detectDoublets()

Usage

Arguments

Returns

Examples

CRMetrics$doPreprocessing()

Usage

Arguments

Returns

Examples

CRMetrics$createEmbedding()

Usage

Arguments

Returns

Examples

`CRMetrics$new()`

`CRMetrics$addDetailedMetrics()`

`CRMetrics$plotBarcodeRankPlot()`

`CRMetrics$getExpectedCells()`

`CRMetrics$addComparison()`

`CRMetrics$plotSamples()`

`CRMetrics$plotSummaryMetrics()`

`CRMetrics$plotDetailedMetrics()`

`CRMetrics$plotEmbedding()`

`CRMetrics$plotDepth()`

`CRMetrics$plotRawDepth()`

`CRMetrics$plotMitoFraction()`

`CRMetrics$detectDoublets()`

`CRMetrics$doPreprocessing()`

`CRMetrics$createEmbedding()`

`CRMetrics$filterCms()`

`CRMetrics$selectMetrics()`

`CRMetrics$plotFilteredCells()`

`CRMetrics$getDepth()`

`CRMetrics$getMitoFraction()`

`CRMetrics$prepareCellbender()`

`CRMetrics$saveCellbenderScript()`

`CRMetrics$addCms()`

`CRMetrics$plotCbTraining()`

`CRMetrics$plotCbCellProbs()`

`CRMetrics$plotCbAmbExp()`

`CRMetrics$plotCbAmbGenes()`

`CRMetrics$addSummaryFromCms()`

`CRMetrics$runSoupX()`