Title:	DNA Copy Number Analysis for Genome-Wide Tapestri Panels
Version:	1.0.1
Description:	Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated 'Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.2.3
URL:	https://github.com/joeymays/karyotapR, http://joeymays.xyz/karyotapR/
BugReports:	https://github.com/joeymays/karyotapR/issues
Imports:	circlize, cli, ComplexHeatmap, dbscan, dplyr, fitdistrplus, GenomicRanges, ggplot2, gtools, IRanges, magrittr, methods, purrr, rhdf5, rlang, S4Vectors, stats, SummarizedExperiment, tibble, tidyr, umap, viridisLite
Depends:	R (≥ 3.6), SingleCellExperiment
Suggests:	Biostrings, knitr, rmarkdown, Rsamtools, testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2023-09-07 00:46:54 UTC; jcm844
Author:	Joseph Mays [aut, cre, cph]
Maintainer:	Joseph Mays <josephcmays@gmail.com>
Repository:	CRAN
Date/Publication:	2023-09-07 08:50:05 UTC

karyotapR: DNA Copy Number Analysis for Genome-Wide Tapestri Panels

Description

Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated 'Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs.

Author(s)

Maintainer: Joseph Mays josephcmays@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/joeymays/karyotapR

• http://joeymays.xyz/karyotapR/

• Report bugs at https://github.com/joeymays/karyotapR/issues

Getter and Setter functions for TapestriExperiment slots

Description

Get and set custom slots in TapestriExperiment. Slots include barcodeProbe for a sample barcode probe ID and grnaProbe for a gRNA-associated probe ID. These are used as shortcuts for moveNonGenomeProbes() and countBarcodedReads(). gmmParams holds parameters and metadata for GMM copy number calling models.

Usage

barcodeProbe(x)

## S4 method for signature 'TapestriExperiment'
barcodeProbe(x)

barcodeProbe(x) <- value

## S4 replacement method for signature 'TapestriExperiment'
barcodeProbe(x) <- value

grnaProbe(x)

## S4 method for signature 'TapestriExperiment'
grnaProbe(x)

grnaProbe(x) <- value

## S4 replacement method for signature 'TapestriExperiment'
grnaProbe(x) <- value

gmmParams(x)

## S4 method for signature 'TapestriExperiment'
gmmParams(x)

Arguments

Value

For the getter methods barcodeProbe, grnaProbe, and gmmParams, the value of the given slot is returned. For the setter methods barcodeProbe and grnaProbe, a TapestriExperiment object is returned with modifications made to the given slot.

Functions

• barcodeProbe(TapestriExperiment): barcodeProbe getter

• barcodeProbe(TapestriExperiment) <- value: barcodeProbe setter

• grnaProbe(TapestriExperiment): grnaProbe getter

• grnaProbe(TapestriExperiment) <- value: grnaProbe setter

• gmmParams(TapestriExperiment): gmmParams getter

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
barcodeProbe(tap.object) <- "Probe01"
barcodeProbe(tap.object)

grnaProbe(tap.object) <- "Probe02"
grnaProbe(tap.object)

gmmParams(tap.object)

Plot of PCA proportion of variance explained

Description

Draws "knee plot" of PCA proportion of variance explained to determine which principal components (PCs) to include for downstream applications e.g. clustering. Variance explained for each PC is indicated by the line. Cumulative variance explained is indicated by the bars.

Usage

PCAKneePlot(TapestriExperiment, alt.exp = "alleleFrequency", n.pcs = 10)

Arguments

Value

ggplot2 object, combined line plot and bar graph

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- runPCA(tap.object, alt.exp = "alleleFrequency")
PCAKneePlot(tap.object, n.pcs = 5)

TapestriExperiment Class Definition

Description

TapestriExperiment Class Definition

Usage

## S4 method for signature 'TapestriExperiment'
show(object)

Arguments

Value

TapestriExperiment object

Methods (by generic)

• show(TapestriExperiment): Show method for TapestriExperiment

Slots

barcodeProbe

character.

grnaProbe

character.

gmmParams

list.

Examples

tapExpObject <- new("TapestriExperiment")

Generate a box plot from assay data

Description

Draws box plot of data from indicated TapestriExperiment assay slot. This is especially useful for visualizing altExp count data, such as counts from probes on chrY or barcode probe counts.

Usage

assayBoxPlot(
  TapestriExperiment,
  alt.exp = NULL,
  assay = NULL,
  log.y = TRUE,
  split.features = FALSE,
  split.x.by = NULL,
  split.y.by = NULL
)

Arguments

Value

ggplot object, box plot

See Also

ggplot2::geom_boxplot()

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
assayBoxPlot(tap.object, alt.exp = "chrYCounts", split.features = TRUE, split.x.by = "test.cluster")

Generate heatmap of assay data

Description

Creates a heatmap of data from the indicated TapestriObject assay slot using the ComplexHeatmap package. Heatmaps are generated as transposed (i.e. x-y flipped) representations of the assay matrix. Additional ComplexHeatmap::Heatmap() parameters can be passed in to overwrite defaults.

Usage

assayHeatmap(
  TapestriExperiment,
  alt.exp = NULL,
  assay = NULL,
  split.col.by = NULL,
  split.row.by = NULL,
  annotate.row.by = NULL,
  color.preset = NULL,
  color.custom = NULL,
  ...
)

Arguments

Value

A ComplexHeatmap object

Options for color.preset

"copy.number"

Blue-white-red gradient from 0-2-4. 4 to 8+ is red-black gradient.

circlize::colorRamp2(c(0,1,2,3,4,8),
c('#2c7bb6','#abd9e9','#ffffff','#fdae61','#d7191c', "black"))

"copy.number.denoise"

Similar to 'copy.number' present, but white range is from 1.5-2.5 to reduce the appearance of noise around diploid cells.

circlize::colorRamp2(c(0,1,1.5,2,2.5,3,4,8),
c('#2c7bb6','#abd9e9','#ffffff','#ffffff','#ffffff','#fdae61','#d7191c', "black"))

See Also

Heatmap

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
assayHeatmap(tap.object,
  assay = "counts", split.row.by = "test.cluster",
  annotate.row.by = "test.cluster", split.col.by = "chr"
)

Calculate relative copy number value for each cell-probe unit using reference sample

Description

calcCopyNumber() transforms the normalized count matrix normcounts of a TapestriExperiment object into copy number values based on a set of reference cell barcodes and given copy number value (e.g. 2 for diploid). This is practically used to set the median copy number of a usually diploid reference cell population to a known copy number value, e.g. 2, and then calculate the copy number for all the cells relative to that reference population. This occurs individually for each probe, such that the result is one copy number value per cell barcode per probe (cell-probe unit). control.copy.number is a data.frame lookup table used to indicate the copy number value and cell barcodes to use as the reference. A template for control.copy.number can be generated using generateControlCopyNumberTemplate(), which will have a row for each chromosome arm represented in TapestriExperiment.

The control.copy.number data.frame should include 3 columns named arm, copy.number, and sample.label. arm is chromosome arm names from chr1p through chrXq, copy.number is the reference copy number value (2 = diploid), and sample.label is the value corresponding to the colData column given in sample.feature to indicate the set of reference cell barcodes to use to set the copy number. This is best used in a workflow where the cells are clustered first into their respective samples, and then one cluster is used as the reference population the other clusters. This also allows for the baseline copy number to be set for each chromosome arm individually in the case where the reference population is not completely diploid.

Usage

calcCopyNumber(
  TapestriExperiment,
  control.copy.number,
  sample.feature = "cluster",
  remove.bad.probes = FALSE
)

generateControlCopyNumberTemplate(
  TapestriExperiment,
  copy.number = 2,
  sample.feature.label = NA
)

Arguments

Value

TapestriExperiment object with cell-probe copy number values in copyNumber assay slot.

data.frame with 3 columns named arm, copy.number, and sample.label

Functions

• generateControlCopyNumberTemplate(): generates a data.frame template for control.copy.number in calcCopyNumber().

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- calcNormCounts(tap.object)
control.copy.number <- generateControlCopyNumberTemplate(tap.object,
  copy.number = 2,
  sample.feature.label = "cellline1"
)
tap.object <- calcCopyNumber(tap.object,
  control.copy.number,
  sample.feature = "test.cluster"
)
tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
control.copy.number <- generateControlCopyNumberTemplate(tap.object,
  copy.number = 2,
  sample.feature.label = "cellline1"
)

Call copy number for each cell-chromosome using Gaussian mixture models

Description

Uses control cells to simulate expected smoothed copy number distributions for all chromosomes across each of model.components (copy number level). Then uses the distributions to calculate posterior probabilities for each cell-chromosome belonging to each of copy number level. Each cell-chromosome is assigned the copy number value for which its posterior probability is highest. This is done for both whole chromosomes and chromosome arms.

Usage

calcGMMCopyNumber(
  TapestriExperiment,
  cell.barcodes,
  control.copy.number,
  model.components = 1:5,
  model.priors = NULL,
  ...
)

Arguments

Value

TapestriExperiment object with copy number calls based on the calculated GMMs, saved to gmmCopyNumber slot of smoothedCopyNumberByChr and smoothedCopyNumberByArm altExps. GMM parameters for each feature.id are saved to the metadata slot.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- calcNormCounts(tap.object)
control.copy.number <- generateControlCopyNumberTemplate(tap.object,
  copy.number = 2,
  sample.feature.label = "cellline1"
)
tap.object <- calcCopyNumber(tap.object,
  control.copy.number,
  sample.feature = "test.cluster"
)
tap.object <- calcSmoothCopyNumber(tap.object)
tap.object <- calcGMMCopyNumber(tap.object,
  cell.barcodes = colnames(tap.object),
  control.copy.number = control.copy.number,
  model.components = 1:5
)

Normalize raw counts

Description

Normalizes raw counts from counts slot in TapestriExperiment and returns the object with normalized counts in the normcounts slot. Also calculates the standard deviation for each probe using normalized counts and adds it to rowData.

Usage

calcNormCounts(TapestriExperiment, method = "mb", scaling.factor = NULL)

Arguments

Details

"mb" method performs the same normalization scheme as in Mission Bio's mosaic package for python: Counts for each barcode are normalized relative to their barcode's mean and probe counts are normalized relative to their probe's median. "libNorm" method preforms library size normalization, returning the proportion of counts of each probe within a cell. The proportion is multiplied by scaling.factor if provided.

Value

TapestriExperiment object with normalized counts added to normcounts slot.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- calcNormCounts(tap.object)

Smooth copy number values across chromosomes and chromosome arms

Description

calcSmoothCopyNumber() takes copyNumber slot values for probes on a chromosome and smooths them by median (default) for each chromosome and chromosome arm, resulting in one copy number value per chromosome and chromosome arm for each cell barcode. Cell-chromosome values are then discretized into integers by conventional rounding (1.5 <= x < 2.5 rounds to 2). Smoothed copy number and discretized smoothed copy number values are stored as smoothedCopyNumber and discreteCopyNumber assays, in altExp slots smoothedCopyNumberByChr for chromosome-level smoothing, and smoothedCopyNumberByArm for chromosome arm-level smoothing.

Usage

calcSmoothCopyNumber(TapestriExperiment, method = "median")

Arguments

Value

TapestriExperiment with smoothedCopyNumber and discreteCopyNumber assays in altExp slots smoothedCopyNumberByChr and smoothedCopyNumberByArm.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- calcNormCounts(tap.object)
control.copy.number <- generateControlCopyNumberTemplate(tap.object,
  copy.number = 2,
  sample.feature.label = "cellline1"
)
tap.object <- calcCopyNumber(tap.object,
  control.copy.number,
  sample.feature = "test.cluster"
)
tap.object <- calcSmoothCopyNumber(tap.object)

Call sample labels based on feature counts

Description

callSampleLables() assigns labels (stored as colData column) to cells using feature count data in colData. This is most useful for assigning barcode labels based on barcoded reads (see countBarcodedReads). For method = max, labels are dictated by whichever input.features column has the highest number of counts. By default, ties are broken by choosing whichever label has the lowest index position (ties.method = "first"). Samples with 0 counts for all input.features columns are labeled according to neg.label. If only one feature column is used, labels are assigned to cells with counts > min.count.threshold, and neg.label otherwise.

Usage

callSampleLables(
  TapestriExperiment,
  input.features,
  output.feature = "sample.call",
  return.table = FALSE,
  neg.label = NA,
  method = "max",
  ties.method = "first",
  min.count.threshold = 1
)

Arguments

Value

A TapestriExperiment object with sample calls added to colData column sample.name. If return.table == TRUE, a data.frame of sample calls.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
colData(tap.object)$gRNA1 <- 2 # example barcode counts
colData(tap.object)$gRNA2 <- 10 # example barcode counts
tap.object <- callSampleLables(tap.object,
  input.features = c("gRNA1", "gRNA2"),
  output.feature = "sample.grna"
)

Print the top-left corner of a matrix

Description

Outputs up to 5 rows and columns of the input matrix object (with rownames and colnames) to get a quick look without filling the console.

Usage

corner(input.mat)

Arguments

Value

A matrix-like object matching input class, subset to a maximum of 5 rows and columns.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
corner(assay(tap.object, "counts"))

Get read counts from barcoded reads

Description

countBarcodedReads() and countBarcodedReadsFromContig() match exogenous DNA barcode sequences to their associated cell barcodes and saves them to the colData (cell barcode metadata) of TapestriExperiment. countBarcodedReads() is a shortcut for countBarcodedReadsFromContig(), allowing the user to specify 'gRNA' or 'barcode' to use the grnaCounts or barcodeCounts altExp slots. The entries in the barcode.lookup table do not have to be present in the sample, allowing users to keep one master table/file of available barcode sequences for use in all experiments. The Rsamtools and Biostrings packages must be installed to use these functions.

Usage

countBarcodedReads(
  TapestriExperiment,
  bam.file,
  barcode.lookup,
  probe,
  return.table = FALSE,
  max.mismatch = 2,
  with.indels = FALSE,
  ...
)

countBarcodedReadsFromContig(
  bam.file,
  barcode.lookup,
  contig,
  cell.barcode.tag = "RG",
  max.mismatch = 2,
  with.indels = FALSE
)

Arguments

Value

TapestriExperiment with barcoded read counts added to colData.

A data.frame of read counts for each specified barcode.

See Also

Rsamtools::indexBam()

Biostrings::matchPattern()

Examples

## Not run: 
counts <- countBarcodedReads(
  TapestriExperiment,
  bam.file, barcode.lookup, "gRNA"
)

## End(Not run)
## Not run: 
counts <- countBarcodedReadsFromContig(bam.file, barcode.lookup, "virus_ref2")

## End(Not run)

Create TapestriExperiment object from Tapestri Pipeline output

Description

createTapestriExperiment() constructs a TapestriExperiment container object from data stored in the .h5 file output by the Tapestri Pipeline. Read count matrix (probe x cell barcode) is stored in the "counts" assay slot of the top-level experiment. Allele frequency matrix (variant x cell barcode) is stored in the "alleleFrequency" assay slot of the "alleleFrequency" altExp (alternative experiment) slot. panel.id is an optional shortcut to set special probe identities for specific custom panels.

Usage

createTapestriExperiment(
  h5.filename,
  panel.id = NULL,
  get.cytobands = TRUE,
  genome = "hg19",
  move.non.genome.probes = TRUE,
  filter.variants = TRUE,
  verbose = TRUE
)

Arguments

Value

TapestriExperiment object containing data from Tapestri Pipeline output.

Panel ID Shortcuts

panel.id is an optional shortcut to set the barcodeProbe and grnaProbe slots in TapestriExperiment for specific custom Tapestri panels.

CO261

• barcodeProbe = "not specified"

• grnaProbe = "not specified"

CO293

• barcodeProbe = "AMPL205334"

• grnaProbe = "AMPL205666"

CO610

• barcodeProbe = "CO610_AMP351"

• grnaProbe = "CO610_AMP350"

Automatic Operations

Raw Data

Read count and allele frequency matrices are imported to their appropriate slots as described above. filter.variants == TRUE (default) only loads allele frequency variants that have passed internal filters in the Tapestri Pipeline. This greatly reduces the number of variants from tens of thousands to hundreds of likely more consequential variants, saving RAM and reducing operation time.

Metadata

Several metadata sets are copied or generated and then stored in the appropriate TapestriExperiment slot during construction.

• Probe panel metadata stored in the .h5 file are copied to rowData.

• Basic QC stats (e.g. total number of reads per probe) are added to rowData.

• Basic QC stats (e.g. total number of reads per cell barcode) are added to colData.

• Experiment-level metadata is stored in metadata.

Optional Operations

Two additional major operations are called by default during TapestriExperiment construction for convenience. get.cytobands == TRUE (default) calls getCytobands(), which retrieves the chromosome arm and cytoband for each probe based on stored positional data and saves them in rowData. Some downstream smoothing and plotting functions may fail if chromosome arms are not present in rowData, so this generally should always be run. move.non.genome.probes calls moveNonGenomeProbes(), which moves probes corresponding to the specified tags to altExp (alternative experiment) slots in the TapestriExperiment object. The exception is probes on chromosome Y; CNVs of chrY are more rare, so we move it to an altExp for separate analysis. Probes corresponding to the barcodeProbe and grnaProbe slots, which are specified by the panel.id shortcut or manually (see Custom Slot Getters and Setters), are automatically moved to altExp by this operation as well. If such probes are not present, the function will only generate a warning message, so it is always safe (and recommended) to run by default. Any remaining probes that are not targeting a human chromosome and are not specified by the shortcut tags are moved to the otherProbeCounts slot.

See Also

moveNonGenomeProbes(), getCytobands(), which are run as part of this function by default.

Examples

## Not run: 
tapExperiment <- createTapestriExperiment("myh5file.h5", "CO293")

## End(Not run)

Get chromosome order from a string of chromosome/contig names

Description

getChrOrder() takes a string of chromosome or contig names and returns the indices of the string in typical chromosome order, i.e. 1 through 22, X, Y. Contig names that do not match 1:22, X, or Y are sorted numerically and alphabetically (with numbers coming first), and added to the end of the order. The output string can then be used to sort the input string into typical chromosome order.

Usage

getChrOrder(chr.vector)

Arguments

Value

A numerical vector of the input vectors indices in chromosome order.

Examples

chr.order <- getChrOrder(c(1, "virus", 5, "X", 22, "plasmid", "Y"))
ordered.vector <- c(1, "virus", 5, "X", 22, "plasmid", "Y")[chr.order]

Add chromosome cytobands and chromosome arms to TapestriExperiment

Description

getCytobands() retrieves the chromosome arm and cytoband for each probe based on stored positional data and saves them in rowData. This is run automatically as part of createTapestriExperiment(). Note: Some downstream smoothing and plotting functions may fail if chromosome arms are not present in rowData.

Usage

getCytobands(TapestriExperiment, genome = "hg19", verbose = TRUE)

Arguments

Value

TapestriExperiment object with rowData updated to include chromosome arms and cytobands.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- getCytobands(tap.object, genome = "hg19")

Calculate decision boundaries between components of copy number GMMs

Description

Calculate decision boundaries between components of copy number GMMs

Usage

getGMMBoundaries(TapestriExperiment, chromosome.scope = "chr")

Arguments

Value

tibble containing boundary values of GMMs for each feature.id.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- calcNormCounts(tap.object)
control.copy.number <- generateControlCopyNumberTemplate(tap.object,
  copy.number = 2,
  sample.feature.label = "cellline1"
)
tap.object <- calcCopyNumber(tap.object,
  control.copy.number,
  sample.feature = "test.cluster"
)
tap.object <- calcSmoothCopyNumber(tap.object)
tap.object <- calcGMMCopyNumber(tap.object,
  cell.barcodes = colnames(tap.object),
  control.copy.number = control.copy.number,
  model.components = 1:5
)

boundaries <- getGMMBoundaries(tap.object,
  chromosome.scope = "chr"
)

Get tidy-style data from TapestriExperiment objects

Description

getTidyData() pulls data from the indicated assay and/or altExp slot(s), and rearranges it into tidy format. colData (cell metadata) from the top-level/main experiment is included. rowData (probe metadata) from the indicated assay and/or altExp slot(s) is included. Attempts are made to sort by "chr" and "start.pos" columns if they are present to simplify plotting and other downstream operations.

Usage

getTidyData(
  TapestriExperiment,
  alt.exp = NULL,
  assay = NULL,
  feature.id.as.factor = TRUE
)

Arguments

Value

A tibble of tidy data with corresponding metadata from colData and rowData.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tidy.data <- getTidyData(tap.object, alt.exp = "alleleFrequency")

Move non-genome probes counts and metadata to altExp slots

Description

moveNonGenomeProbes() takes the probe IDs corresponding to grnaProbe and barcodeProbe slots of the TapestriExperiment object, as well as probes on chrY, and moves them to their own altExp slots in the object. This allows those counts and associated metadata to be manipulated separately without interfering with the probes used for CNV measurements which target the endogenous genome. SingleCellExperiment::splitAltExps() can be used for manual specification of probes to move to altExp slots if the shortcut slots are not used.

Usage

moveNonGenomeProbes(TapestriExperiment)

Arguments

Details

moveNonGenomeProbes() moves probes corresponding to the specified tags to altExp (alternative experiment) slots in the TapestriExperiment object. These probes should be those which do not correspond to a chromosome and therefore would not be used to call copy number variants. The exception is probes on chromosome Y; CNVs of chrY are more rare, so we move it to an altExp for separate analysis. Probes corresponding to the barcodeProbe and grnaProbe slots, which are specified by the panel.id shortcut or manually (see Custom Slot Getters and Setters), are automatically moved to altExp by this operation as well. If such probes are not present, the function will only generate a warning message, so it is always safe (and recommended) to run by default. Any remaining probes that are not targeting a human chromosome and are not specified by the shortcut tags are moved to the otherProbeCounts slot. This function is run automatically by default and with default behavior as part of createTapestriExperiment().

Value

TapestriExperiment with altExp slots filled with counts and metadata for non-genomic probes.

See Also

SingleCellExperiment::splitAltExps() for manual specification of probes to move to altExp slots.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment
tap.object <- moveNonGenomeProbes(tap.object)

Create Example TapestriExperiment

Description

Creates a TapestriExperiment object for demonstration purposes, which includes 240 probes across the genome, and 300 cells of 3 types. Raw counts are generated randomly. Type 1 has 75 cells, all XY, all diploid. Type 2 has 100 cells, all XX, with 3 copies of chr 7, otherwise diploid. Type 3 has 125 cells, all XY, with 1 copy of chr 1p, otherwise diploid.

Usage

newTapestriExperimentExample()

Value

TapestriExperiment object with demo data.

Examples

tapExperiment <- newTapestriExperimentExample()

Plot copy number GMM components

Description

Plots the probability densities of GMM components for given chromosome or chromosome arm, store in a TapestriExperiment. calcGMMCopyNumber() must be run first.

Usage

plotCopyNumberGMM(
  TapestriExperiment,
  feature.id = 1,
  chromosome.scope = "chr",
  draw.boundaries = FALSE
)

Arguments

Value

ggplot object, density plot

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- calcNormCounts(tap.object)
control.copy.number <- generateControlCopyNumberTemplate(tap.object,
  copy.number = 2,
  sample.feature.label = "cellline1"
)
tap.object <- calcCopyNumber(tap.object,
  control.copy.number,
  sample.feature = "test.cluster"
)
tap.object <- calcSmoothCopyNumber(tap.object)
tap.object <- calcGMMCopyNumber(tap.object,
  cell.barcodes = colnames(tap.object),
  control.copy.number = control.copy.number,
  model.components = 1:5
)

tap.object <- plotCopyNumberGMM(tap.object,
  feature.id = 7,
  chromosome.scope = "chr",
  draw.boundaries = TRUE
)

Scatter plot for dimensional reduction results

Description

Plots a scatter plot of the indicated dimensional reduction results.

Usage

reducedDimPlot(
  TapestriExperiment,
  alt.exp = "alleleFrequency",
  dim.reduction,
  dim.x = 1,
  dim.y = 2,
  group.label = NULL
)

Arguments

Value

ggplot2 object, scatter plot

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- runPCA(tap.object, alt.exp = "alleleFrequency")
reducedDimPlot(tap.object, dim.reduction = "pca")

Cluster 2D data

Description

Clusters data using dbscan method and saves cluster assignments for each cell barcode to colData. Generally used to assign clusters to UMAP projection after PCA and UMAP dimensional reduction.

Usage

runClustering(
  TapestriExperiment,
  alt.exp = "alleleFrequency",
  dim.reduction = "UMAP",
  eps = 0.8,
  dim.1 = 1,
  dim.2 = 2,
  ...
)

Arguments

Value

TapestriExperiment object with updated colData containing cluster assignments.

See Also

dbscan::dbscan()

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- runPCA(tap.object, alt.exp = "alleleFrequency")
tap.object <- runUMAP(tap.object, pca.dims = 1:3)
tap.object <- runClustering(tap.object, dim.reduction = "UMAP", eps = 0.8)

Cluster assay data by Principal Components Analysis

Description

Analyzes assay data by Principal Components Analysis (PCA) and saves results to reducedDims slot of TapestriObject.

Usage

runPCA(
  TapestriExperiment,
  alt.exp = "alleleFrequency",
  assay = NULL,
  sd.min.threshold = NULL,
  center = TRUE,
  scale. = TRUE
)

Arguments

Value

TapestriExperiment with PCA results saved to reducedDims slot of altExp, and proportion of variance explained by each PC saved to metadata slot of altExp.

See Also

stats::prcomp() for PCA method details.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment
tap.object <- runPCA(tap.object, alt.exp = "alleleFrequency")

Cluster matrix data by UMAP

Description

Analyzes matrix data by UMAP and saves results to reducedDims slot of TapestriObject.

Usage

runUMAP(
  TapestriExperiment,
  alt.exp = "alleleFrequency",
  assay = NULL,
  use.pca.dims = TRUE,
  pca.dims = NULL,
  ...
)

Arguments

Value

TapestriExperiment with UMAP embeddings saved to reducedDims slot of altExp.

Examples

tap.object <- newTapestriExperimentExample() # example TapestriExperiment object
tap.object <- runPCA(tap.object, alt.exp = "alleleFrequency")
tap.object <- runUMAP(tap.object, pca.dims = 1:3)