Package 'scafari'

Function: annotateAmplicons This function takes a SingleCellExperiment object as input and annotates the stored amplicons.

Description

Function: annotateAmplicons This function takes a SingleCellExperiment object as input and annotates the stored amplicons.

Usage

annotateAmplicons(sce, known.canon, shiny = FALSE)

Arguments

sce	SingleCellExperiment object containing the single-cell data.
known.canon	Path to jnown canonicals (see vignette)
shiny	If TRUE messages are shown

Value

A dataframe containing annotated amplicons.

Examples

# Assume `sce` is a SingleCellExperiment object with a 'counts' assay
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))

annotated <- annotateAmplicons(
    sce_filtered,
    system.file("extdata", "UCSC_hg19_knownCanonical_mock.txt",
        package = "scafari"
    )
)

---

Function: annotateVariants This function takes a SingleCellExperiment object as input and performs variant annotation.

Description

Function: annotateVariants This function takes a SingleCellExperiment object as input and performs variant annotation.

Usage

annotateVariants(sce, shiny = FALSE, max.var = 50)

Arguments

sce	SingleCellExperiment object containing the single-cell data to be annotated.
shiny	A logical flag indicating whether the function is being run in a Shiny application context. Default is FALSE.
max.var	Maximum number of variants to annotate. By default this is 50 to avoid long runtime.

Value

The function returns an annotated SingleCellExperiment object.

References

https://missionbio.github.io/mosaic/, https://github.com/rachelgriffard/optima

Examples

# Assume `sce` is a SingleCellExperiment object with variants in altExp()
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
sce <- annotateVariants(sce_filtered, shiny = FALSE)

---

Function: clusterVariantSelection This function takes selected variants and performs clustering on them.

Description

Function: clusterVariantSelection This function takes selected variants and performs clustering on them.

Usage

clusterVariantSelection(
  sce,
  variants.of.interest,
  n.clust,
  method = "k-means",
  eps.value = 0.2,
  resolution = NULL,
  min.pts = NULL
)

Arguments

sce	A SingleCellExperiment object containing the single-cell data on which clustering will be performed.
variants.of.interest	A vector or list specifying the variants of interest to be selected for clustering.
n.clust	An integer specifying the number of clusters.
method	Clustering method. Either k-means, dbscan or leiden.
eps.value	Size (radius) of the epsilon neighborhood. Can be omitted if x is a frNN object.
resolution	The resolution parameter to use. Higher resolutions lead to more smaller communities, while lower resolutions lead to fewer larger communities.
min.pts	Number of minimum points required in the eps neighborhood for core points (including the point itself). By default cell number/100.

Value

A list with clustering results and a ggplot-object.

References

https://cran.r-project.org/web/packages/dbscan/readme/ README.html#ref-hahsler2019dbscan

Examples

# Assume `sce` is a SingleCellExperiment object with variants in altExp()
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
clusterplot <- clusterVariantSelection(
    sce = sce_filtered,
    variants.of.interest = c(
        "FLT3:chr13:28610183:A/G",
        "KIT:chr4:55599436:T/C",
        "TP53:chr17:7577427:G/A",
        "TET2:chr4:106158216:G/A"
    ),
    n.clust = 4
)

---

Function: filterVariants ——————————- This function takes a SingleCellExperiment object as input and performs variant filtering

Description

Function: filterVariants ——————————- This function takes a SingleCellExperiment object as input and performs variant filtering

Usage

filterVariants(
  depth.threshold = numeric(),
  genotype.quality.threshold = numeric(),
  vaf.ref = numeric(),
  vaf.het = numeric(),
  vaf.hom = numeric(),
  min.cell = numeric(),
  min.mut.cell = numeric(),
  se.var,
  sce,
  shiny = FALSE
)

Arguments

depth.threshold	A numeric value specifying the minimum read depth required.
genotype.quality.threshold	A numeric value specifying the minimum genotype quality score.
vaf.ref	A numeric value specifying the variant allele frequency threshold for wild-type alleles.
vaf.het	A numeric value specifying the variant allele frequency threshold for heterozygous variants.
vaf.hom	A numeric value specifying the variant allele frequency threshold for homozygous variants.
min.cell	A numeric value indicating the minimum number of cells with a genotype other than "missing".
min.mut.cell	A numeric value indicating the minimum number of mutated (genotype either "homozygous" or "heterozygous") cells.
se.var	The SummarizedExperiment object containing variant data which will be filtered.
sce	SingleCellExperiment object containing the single-cell data TODO.
shiny	A logical flag indicating whether the function is being run in a Shiny application context. Default is FALSE.

Value

A list containing the following elements:

vaf.matrix.filtered

Variant allele frequencies after filtering.

genotype.matrix.filtered

Genotype information after filtering.

read.counts.df.norm.filtered

Normalized read counts for variants retained after filtering.

variant.ids.filtered

A vector of the variant IDs that were retained after filtering.

genoqual.matrix.filtered

Genotype qualities for variants retained after filtering.

cells.keep

A vector of cell identifiers for those cells retaine d after filtering.

References

https://missionbio.github.io/mosaic/, https://github.com/rachelgriffard/optima

Examples

library(SummarizedExperiment)
h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")
h5 <- h5ToSce(h5_file_path)
sce <- h5$sce_amp
se.var <- h5$se_var
sce <- normalizeReadCounts(sce = sce)
filteres <- filterVariants(
    depth.threshold = 10,
    genotype.quality.threshold = 30,
    vaf.ref = 5,
    vaf.het = 35,
    vaf.hom = 95,
    min.cell = 50,
    min.mut.cell = 1,
    se.var = se.var,
    sce = sce,
    shiny = FALSE
)
se.f <- SummarizedExperiment(
    assays = list(
        VAF = t(filteres$vaf.matrix.filtered),
        Genotype = t(filteres$genotype.matrix.filtered),
        Genoqual = t(filteres$genoqual.matrix.filtered)
    ),
    rowData = filteres$variant.ids.filtered,
    colData = filteres$cells.keep
)

# Filter out cells in sce object
# Find the indices of the columns to keep
indices_to_keep <- match(filteres$cells.keep,
    SummarizedExperiment::colData(sce)[[1]],
    nomatch = 0
)

# Subset the SCE using these indices
sce_filtered <- sce[, indices_to_keep]

---

Function: h5ToSce This function takes the path of an h5 file and reads this into an object of the SingleCellExperiment class.

Description

Function: h5ToSce This function takes the path of an h5 file and reads this into an object of the SingleCellExperiment class.

Usage

h5ToSce(h5_file)

Arguments

h5_file

path of an h5 file

Value

A list containing the following elements:

sce_amp

SingleCellExperiment class object containing read count information.

se_var

SummarizedExperiment class object containing variant information..

A list with the SingleCellExperiment and the SummarizedExperiment object representing the amplicon and the variant analysis.

sce_amp

SingleCellExperiment with amplicon experiment.

se_var

SummarizedExperiment with variants eexperiment

Examples

h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")

# Read the h5ToSce using readH5File
result <- h5ToSce(h5_file_path)

# Display the result
result

---

Launch Scafari Shiny Application

Description

Launches the Scafari Shiny application for data visualization.

Usage

launchScafariShiny()

Value

shiny app

Examples

if (interactive()) {
    launchScafariShiny()
}

---

Plot a log-log plot for read counts

Description

This function generates a log-log plot of read counts using the data from a 'SingleCellExperiment' object.

Usage

logLogPlot(sce)

Arguments

sce	A SingleCellExperiment object that contains read count data to be plotted. The read counts are extracted from an associated h5 file or similar data structure within the object.

Value

A ggplot object representing the log-log plot of read counts.

Examples

# Assume `sce` is a SingleCellExperiment object with a 'counts' assay
h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")
h5 <- h5ToSce(h5_file_path)
sce <- h5$sce_amp
plot <- logLogPlot(sce)
print(plot)

---

This function normalizes the read counts contained within a 'SingleCellExperiment' object.

Description

This function normalizes the read counts contained within a 'SingleCellExperiment' object.

Usage

normalizeReadCounts(sce)

Arguments

sce	A SingleCellExperiment object that includes the assay data with read counts to be normalized. The metadata within the object may also be utilized for normalization purposes.

Value

SingleCellExperiment object with normalized read counts.

References

https://missionbio.github.io/mosaic/, https://github.com/rachelgriffard/optima

Examples

# Assume `sce` is a SingleCellExperiment object with 'counts' assay.
h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")
h5 <- h5ToSce(h5_file_path)
sce <- h5$sce_amp
sce <- normalizeReadCounts(sce)

---

Plot Distribution of Amplicons

Description

This function generates a plot to visualize the distribution of amplicons within a 'SingleCellExperiment' object.

Usage

plotAmpliconDistribution(sce)

Arguments

sce	A SingleCellExperiment object that contains the assay data, including amplicon information to be plotted.

Value

A ggplot object representing the distribution of amplicons.

Examples

# Assume `sce` is a SingleCellExperiment object with 'counts' assay.
h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")
h5 <- h5ToSce(h5_file_path)
sce <- h5$sce_amp
plotAmpliconDistribution(sce)

---

Plot Genotype Clusters

Description

This function generates a plot to visualize genotype in clusters based on selected variants of interest.

Usage

plotClusterGenotype(sce, variants.of.interest, gg.clust)

Arguments

sce	A SingleCellExperiment object containing the relevant data.
variants.of.interest	A vector specifying the variants of interest.
gg.clust	An object containing clustering information.

Value

A ggplot object that visually represents the clustering of genotypes based on the specified variants and clustering information.

Examples

# Assume `sce` is a SingleCellExperiment object with variants in altExp()
# and clusterplot is the output of clusterVariantSleection().
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
clusterplot <- readRDS(system.file("extdata", "clusterplot.rds",
    package = "scafari"
))
plotClusterGenotype(
    sce = sce_filtered,
    variants.of.interest = c(
        "FLT3:chr13:28610183:A/G",
        "KIT:chr4:55599436:T/C",
        "TP53:chr17:7577427:G/A",
        "TET2:chr4:106158216:G/A"
    ),
    gg.clust = clusterplot$clusterplot
)

---

plot Cluster VAF This function generates a plot to visualize variant allele frequency (VAF) in clusters based on selected variants of interest.

Description

plot Cluster VAF This function generates a plot to visualize variant allele frequency (VAF) in clusters based on selected variants of interest.

Usage

plotClusterVAF(sce, variants.of.interest, gg.clust)

Arguments

sce	A SingleCellExperiment object containing the relevant data.
variants.of.interest	A vector specifying the variants of interest.
gg.clust	An object containing clustering information.

Value

A ggplot object that visually represents the VAF in the clusters.

Examples

# Assume `sce` is a SingleCellExperiment object with variants in altExp() and
# clusterplot is the output of clusterVariantSleection().
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
clusterplot <- readRDS(system.file("extdata", "clusterplot.rds",
    package = "scafari"
))
plotClusterVAF(
    sce = sce_filtered,
    variants.of.interest = c(
        "FLT3:chr13:28610183:A/G",
        "KIT:chr4:55599436:T/C",
        "TP53:chr17:7577427:G/A",
        "TET2:chr4:106158216:G/A"
    ),
    gg.clust = clusterplot$clusterplot
)

---

plot Cluster VAF Map This function generates a plot to visualize variant allele frequency (VAF) in clusters based on selected variants of interest with clusters in the background.

Description

plot Cluster VAF Map This function generates a plot to visualize variant allele frequency (VAF) in clusters based on selected variants of interest with clusters in the background.

Usage

plotClusterVAFMap(sce, variants.of.interest, gg.clust)

Arguments

sce	A SingleCellExperiment object containing the relevant data.
variants.of.interest	A vector specifying the variants of interest.
gg.clust	An object containing clustering information.

Value

A ggplot object that visually represents the VAF in the clusters with clusters in the background.

Examples

# Assume `sce` is a SingleCellExperiment object with variants in altExp() and
# clusterplot is the output of clusterVariantSleection().
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
clusterplot <- readRDS(system.file("extdata", "clusterplot.rds",
    package = "scafari"
))
plotClusterVAFMap(
    sce = sce_filtered,
    variants.of.interest = c(
        "FLT3:chr13:28610183:A/G",
        "KIT:chr4:55599436:T/C",
        "TP53:chr17:7577427:G/A",
        "TET2:chr4:106158216:G/A"
    ),
    gg.clust = clusterplot$clusterplot
)

---

This function takes a SingleCellExperiment object and variants of interest as input and plots an elbow plot to perform k-means later.

Description

This function takes a SingleCellExperiment object and variants of interest as input and plots an elbow plot to perform k-means later.

Usage

plotElbow(sce, variants.of.interest)

Arguments

sce	A SingleCellExperiment object containing the relevant data.
variants.of.interest	A vector specifying the variants of interest.

Value

ggplot object with elbow plot.

Examples

# Assume `sce` is a SingleCellExperiment object with variants in altExp().
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
package = "scafari"))
plotElbow(
    sce = sce_filtered,
    variants.of.interest = c(
        "FLT3:chr13:28610183:A/G",
        "KIT:chr4:55599436:T/C",
        "TP53:chr17:7577427:G/A",
        "TET2:chr4:106158216:G/A"
    )
)

---

Plot Genotype Quality per Genotype

Description

This function generates a plot to assess the quality of genotypes within a ‘SingleCellExperiment' object. It uses the ’genotype_quality' assay or any relevant assay to visualize the distribution of genotype quality metrics across different genotypes.

Usage

plotGenotypequalityPerGenotype(sce)

Arguments

sce	A 'SingleCellExperiment' object containing the single-cell data. This object should include a 'genotype_quality' assay or similar data which provides quality metrics for each genotype.

Value

A 'ggplot' object visualizing the distribution of genotype quality across different genotypes.

Examples

# Assume `sce` is a SingleCellExperiment object with 'genotype_quality'
# assay.
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
genotype_quality_plot <- plotGenotypequalityPerGenotype(sce_filtered)
print(genotype_quality_plot)

---

Plot normalized read counts

Description

This function plots the normalize read counts in a bar chart.

Usage

plotNormalizedReadCounts(sce)

Arguments

sce	A SingleCellExperiment object containing the relevant data.

Value

ggplot object visualizing the normalized read counts in a bar chart.

Examples

# Assume `sce` is a SingleCellExperiment object with 'counts' assay.
h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")
h5 <- h5ToSce(h5_file_path)
sce <- h5$sce_amp
sce <- normalizeReadCounts(sce = sce)
plotNormalizedReadCounts(sce)

---

Plot Panel Uniformity

Description

This function generates a plot to assess the uniformity of panel reads in a 'SingleCellExperiment' object. It uses read counts stored in the 'counts' assay to visualize the distribution and variability of reads.

Usage

plotPanelUniformity(sce, interactive = FALSE)

Arguments

sce	A 'SingleCellExperiment' object that contains single-cell data with a 'counts' assay. This object should be pre-processed to ensure the data is appropriate for uniformity analysis.
interactive	in interactive mode an plotly is returned.

Value

A 'ggplot' object visualizing the uniformity of panel reads.

Examples

# Assume `sce` is a SingleCellExperiment object with 'counts' assay.
h5_file_path <- system.file("extdata", "demo.h5", package = "scafari")
h5 <- h5ToSce(h5_file_path)
sce <- h5$sce_amp
sce <- normalizeReadCounts(sce = sce)
uniformity_plot <- plotPanelUniformity(sce)
print(uniformity_plot)

---

Plot Variant Heatmap

Description

This function generates a heatmap to visualize variant data within a 'SingleCellExperiment' object. The heatmap provides insights into the distribution and frequency of variants across different samples or cells.

Usage

plotVariantHeatmap(sce)

Arguments

sce	A 'SingleCellExperiment' object containing single-cell variant data. The object should include an assay that holds variant information suitable for visualization in a heatmap.

Value

A 'ggplot' object representing the heatmap of variant frequencies or presence across cells or groups.

Examples

# Assume `sce` is a SingleCellExperiment object with an appropriate variant
# assay.
sce_filtered <- readRDS(system.file("extdata", "sce_filtered_demo.rds",
    package = "scafari"
))
variant_heatmap <- plotVariantHeatmap(sce_filtered)
print(variant_heatmap)

---

scafari: analyzing scDNA-seq data

Description

scafari is an R Bioconductor package for single-cell DNA-seq (scDNA-seq) analysis.

Details

scafari works on .h5 files, the standard output of the Tapestri pipeline. It offers easy-to-use data quality control as well as explorative variant analyses and visualization for scDNA-seq data.

Main Functions

• h5ToSce(): Reads in .h5 files and writes them into a SingleCellExperiment object.

• normalizeReadCounts(): Normalizes the read counts.

• annotateAmplicons(): Annotates the amplicons present in the .h5 file.

• filterVariants(): Filters the variants present in the .h5 file.

• annotateVariants(): Annotates the filtered variants.

• clusterVariantSelection(): Clusteres variants of special interest.

Installation

To install this package, use: BiocManager::install("scafari")

Author(s)

Maintainer: Sophie Wind [email protected] (ORCID)

See Also

Useful links:

• https://github.com/sophiewind/scafari

• Report bugs at https://github.com/sophiewind/scafari/issues

---