Package 'CRMetrics'

Title: Cell Ranger Output Filtering and Metrics Visualization
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.
Authors: Rasmus Rydbirk [aut, cre], Fabienne Kick [aut], Henrietta Holze [aut], Xian Xin [ctb]
Maintainer: Rasmus Rydbirk <[email protected]>
License: GPL-3
Version: 0.3.2
Built: 2024-11-04 20:23:25 UTC
Source: https://github.com/khodosevichlab/crmetrics

Help Index

CRMetrics class object

Description

Functions to analyze Cell Ranger 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 count data, one directory per sample. If multiple paths, do c("path1","path2") (default = NULL)

cms

list List with count matrices (default = NULL)

cms.preprocessed

list List with preprocessed count matrices after $doPreprocessing() (default = NULL)

cms.raw

list List with raw, unfiltered count matrices, i.e., including all CBs detected also empty droplets (default = NULL)

summary.metrics

data.frame Summary metrics from Cell Ranger (default = NULL)

detailed.metrics

data.frame Detailed metrics, i.e., no. 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

Method new()

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

Usage
CRMetrics$new(
  data.path = NULL,
  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 Cell Ranger count data, one directory per sample (default = NULL).

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 is 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 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())

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

Method addDetailedMetrics()

Function to read in detailed metrics. This is not done upon 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()

Method 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")

Method plotSamples()

Plot the number of samples.

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 Comparison metric, must match a column name of metadata (default = self$comp.group).

h.adj

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

exact

logical Whether to calculate exact p values (default = FALSE).

metadata

data.frame Metadata for samples (default = self$metadata).

second.comp.group

character Second comparison metric, 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")

Method 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 Comparison metric (default = self$comp.group).

second.comp.group

character Second comparison metric, used for the metric "samples per group" or when "comp.group" is a numeric or an integer (default = NULL).

metrics

character Metrics to plot (default = NULL).

h.adj

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

plot.stat

logical Show statistics in plot. Will be FALSE if "comp.group" = "sample" or if "comp.group" is a numeric or an integer (default = TRUE)

stat.test

character Statistical test to perform to compare means. Can either be "non-parametric" or "parametric" (default = "non-parametric").

exact

logical Whether to calculate exact p values (default = FALSE).

metadata

data.frame Metadata for samples (default = self$metadata).

summary.metrics

data.frame Summary metrics (default = self$summary.metrics).

plot.geom

character Which geometric is used to plot the data (default = "point").

se

logical For regression lines, show SE (default = FALSE)

group.reg.lines

logical For regression lines, if FALSE show one line, if TRUE show line per group defined by second.comp.group (default = FALSE)

secondary.testing

logical Whether to show post hoc testing (default = TRUE)

pal

character Plotting palette (default = self$pal)

Returns

ggplot2 object

Examples
\donttest{
# 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 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
\donttest{
# 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 plotEmbedding()

Plot cells in embedding using Conos and color by depth and doublets.

Usage
CRMetrics$plotEmbedding(
  depth = FALSE,
  doublet.method = NULL,
  doublet.scores = FALSE,
  depth.cutoff = 1000,
  mito.frac = FALSE,
  mito.cutoff = 0.05,
  species = c("human", "mouse"),
  size = 0.3,
  sep = "!!",
  pal = NULL,
  ...
)
Arguments
depth

logical Plot depth or not (default = FALSE).

doublet.method

character Doublet detection method (default = NULL).

doublet.scores

logical Plot doublet scores or not (default = FALSE).

depth.cutoff

numeric Depth cutoff (default = 1e3).

mito.frac

logical Plot mitochondrial fraction or not (default = FALSE).

mito.cutoff

numeric Mitochondrial fraction cutoff (default = 0.05).

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
\donttest{
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 plotDepth()

Plot the sequencing depth in histogram.

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

numeric The depth cutoff to color the cells in the embedding (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
\donttest{
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 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
\donttest{
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 detectDoublets()

Detect doublet cells.

Usage
CRMetrics$detectDoublets(
  method = c("scrublet", "doubletdetection"),
  cms = self$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, either scrublet or doubletdetection (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 either DoubletDetection or scrublet. Please check the respective manuals.

export

boolean Export CMs in order to detect doublets 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
\dontrun{
# 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")
}

Method 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
\donttest{
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 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
\donttest{
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 filterCms()

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

Usage
CRMetrics$filterCms(
  depth.cutoff = NULL,
  mito.cutoff = NULL,
  doublets = NULL,
  species = c("human", "mouse"),
  samples.to.exclude = NULL,
  verbose = self$verbose,
  sep = "!!",
  raw = FALSE
)
Arguments
depth.cutoff

numeric Depth (transcripts per cell) cutoff (default = NULL).

mito.cutoff

numeric Mitochondrial fraction cutoff (default = NULL).

doublets

character Doublet detection method to use (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
\donttest{
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 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))

Method 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 = TRUE,
  depth.cutoff = 1000,
  doublet.method = NULL,
  mito.frac = TRUE,
  mito.cutoff = 0.05,
  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 or not (default = TRUE).

depth.cutoff

numeric Depth cutoff, either a single number or a vector with cutoff per sample and with sampleIDs as names (default = 1e3).

doublet.method

character Method to detect doublets (default = NULL).

mito.frac

logical Plot the mitochondrial fraction or not (default = TRUE).

mito.cutoff

numeric Mitochondrial fraction cutoff, either a single number or a vector with cutoff per sample and with sampleIDs as names (default = 0.05).

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
\donttest{
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 getDepth()

Extract sequencing depth from Conos object.

Usage
CRMetrics$getDepth(cms = self$cms)
Arguments
cms

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

Returns

data frame

Examples
\donttest{
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 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
\donttest{
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 prepareCellbender()

Create plots and script call for CellBender

Usage
CRMetrics$prepareCellbender(
  shrinkage = 100,
  show.expected.cells = TRUE,
  show.total.droplets = TRUE,
  expected.cells = NULL,
  total.droplets = NULL,
  cms.raw = self$cms.raw,
  umi.counts = self$cellbender$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 expected number of cells (default = TRUE)

show.total.droplets

logical Plot line depicting total droplets included for CellBender run (default = TRUE)

expected.cells

named numeric If NULL, expected cells will be deduced from the number of cells per sample identified by Cell Ranger. Otherwise, a named vector of expected cells with sample IDs as names. Sample IDs must match those in summary.metrics (default: stored named vector)

total.droplets

named numeric If NULL, total droplets included will be deduced from expected cells multiplied by 3. Otherwise, a named vector of total droplets included with sample IDs as names. Sample IDs must match those in summary.metrics (default: stored named vector)

cms.raw

list Raw count matrices from HDF5 Cell Ranger outputs (default = self$cms.raw)

umi.counts

list UMI counts calculated as column sums of raw count matrices from HDF5 Cell Ranger outputs (default: stored list)

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

ggplot2 object and bash script

Examples
\dontrun{
crm <- CRMetrics$new(data.path = "/path/to/count/data")
crm$prepareCellbender()
}

Method saveCellbenderScript()

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

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 If NULL, expected cells will be deduced from the number of cells per sample identified by Cell Ranger. Otherwise, a named vector of expected cells with sample IDs as names. Sample IDs must match those in summary.metrics (default: stored named vector)

total.droplets

named numeric If NULL, total droplets included will be deduced from expected cells multiplied by 3. Otherwise, a named vector of total droplets included with sample IDs as names. Sample IDs must match those in summary.metrics (default: stored named vector)

data.path

character Path to Cell Ranger 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
\dontrun{
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$prepareCellbender()
crm$saveCellbenderScript()
}

Method getExpectedCells()

Extract the expected number of cells per sample based on the Cell Ranger 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()

Method getTotalDroplets()

Get the total number of droplets included in the CellBender estimations. Based on the Cell Ranger summary metrics and multiplied by a preset multiplier.

Usage
CRMetrics$getTotalDroplets(samples = self$metadata$sample, multiplier = 3)
Arguments
samples

character Samples names to include (default = self$metadata$sample)

multiplier

numeric Number to multiply expected number of cells with (default = 3)

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)

# Add summary
crm$addSummaryFromCms()

# Get no. droplets
crm$getTotalDroplets()

Method addCms()

Add a list of count matrices to the CRMetrics object.

Usage
CRMetrics$addCms(
  cms = NULL,
  data.path = self$data.path,
  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 cellranger count data (default = self$data.path).

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 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
\dontrun{
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)
}

Method plotCbTraining()

Plot the results from the CellBender estimations

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

pal

character Plotting palette (default = self$pal)

Returns

A ggplot2 object

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

Method plotCbCellProbs()

Plot the CellBender assigned cell probabilities

Usage
CRMetrics$plotCbCellProbs(
  data.path = self$data.path,
  samples = self$metadata$sample,
  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)

low.col

character Color for low probabilities (default = "gray")

high.col

character Color for high probabilities (default = "red")

Returns

A ggplot2 object

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

Method 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
)
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)

Returns

A ggplot2 object

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

Method 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,
  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)

pal

character Plotting palette (default = self$pal)

Returns

A ggplot2 object

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

Method 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()

Method 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
\dontrun{
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$runSoupX()
}

Method 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
\dontrun{
crm <- CRMetrics$new(data.path = "/path/to/count/data/")
crm$runSoupX()
crm$plotSoupX()
}

Method 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,
  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)

pal

character Plotting palette (default = self$pal)

Returns

A ggplot2 object

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

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

Method 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$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$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$getTotalDroplets`
## ------------------------------------------------

# 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()

# Get no. droplets
crm$getTotalDroplets()

## ------------------------------------------------
## 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)

Read 10x HDF5 files

Description

Read 10x HDF5 files

Usage

read10xH5(
  data.path,
  samples = NULL,
  type = c("raw", "filtered", "cellbender", "cellbender_filtered"),
  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
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)