Package 'umiAnalyzer'

Title: Tools for Analyzing Sequencing Data with Unique Molecular Identifiers
Description: Tools for analyzing sequencing data containing unique molecular identifiers generated by 'UMIErrorCorrect' (<https://github.com/stahlberggroup/umierrorcorrect>).
Authors: Stefan Filges [aut, cre] , Gustav Johansson [ctb]
Maintainer: Stefan Filges <[email protected]>
License: GPL-3
Version: 1.0.0
Built: 2025-03-13 05:48:44 UTC
Source: https://github.com/sfilges/umianalyzer

Help Index

Add metaData

Description

Add metaData

Usage

addMetaData(object, attributeName, attributeValue)

Arguments

object

R object to which meta data should be added
attributeName

Name of the meta data attribute.
attributeValue

Meta data to be saved.

Value

A UMIexperiment object

Examples

library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)

metaData <- system.file("extdata", "metadata.txt", package = "umiAnalyzer")

simsen <- addMetaData(simsen,'metaData',metaData)

Add UMI sample to an existing experiment object

Description

Add UMI sample to an existing experiment object

Usage

addUmiSample(object, sampleName, sampleDir, clearData = FALSE)

Arguments

object

UMIexperiment object
sampleName

Name of new sample
sampleDir

Directory to new sample
clearData

Should other data in UMIexperiment be cleared

Value

A UMIexperiment object

Amplicon heatmap

Description

Generates a heatmap of mutations with sample clustering using pheatmap.

Usage

AmpliconHeatmap(
  object,
  filter.name = "default",
  cut.off = 5,
  left.side = "columns",
  amplicons = NULL,
  samples = NULL,
  abs.count = FALSE,
  font.size = 10
)

Arguments

object

Requires a UMI sample or UMI experiment object
filter.name

Name of the filter to be plotted.
cut.off

How many variant reads are necessary to consider a variant above background? Default is 5 reads.
left.side

Show assays or sample on the left side of the heatmap. Default is assays
amplicons

(Optional) character vector of amplicons to be plotted.
samples

(Optional) character vector of samples to be plotted.
abs.count

Logical. Should absolute counts be used instead of frequencies?
font.size

Font size to use for sample labels

Value

A graphics object

Examples

## Not run: 
library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')
samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)
simsen <- createUmiExperiment(experimentName = 'example',mainDir = main,sampleNames = samples)
simsen <- filterUmiObject(simsen)

hmap <- AmpliconHeatmap(simsen)

## End(Not run)

Generate Amplicon plots

Description

Plots variant allele frequencies or alternate allele counts for chosen samples and assays.

Usage

AmpliconPlot(
  object,
  filter.name = "default",
  cut.off = 5,
  min.count = 0,
  min.vaf = 0,
  amplicons = NULL,
  samples = NULL,
  abs.count = FALSE,
  y_min = 0,
  y_max = NULL,
  theme = "classic",
  option = "default",
  direction = "default",
  plot.text = FALSE,
  plot.ref = TRUE,
  stack.plot = FALSE,
  classic.plot = FALSE,
  fdr = 0.05,
  font.size = 6,
  angle = 45,
  use.caller = FALSE,
  use.plotly = TRUE
)

Arguments

object

Requires a UMI sample or UMI experiment object
filter.name

Name of the filter to be plotted.
cut.off

How many variant reads are necessary to consider a variant above background? Default is 5 reads.
min.count

Minimum variants counts to plot, default is 0.
min.vaf

Minimum variants allele frequency to plot, default is 0.
amplicons

(Optional) character vector of amplicons to be plotted.
samples

(Optional) character vector of samples to be plotted.
abs.count

Should absolute counts be plotted instead of frequencies? Default is FALSE.
y_min

Minimum y-axis value, default is 0
y_max

Maximum y-axis value, default is NULL (autoscale)
theme

Plotting theme to use, default is classic.
option

Color palette to use.
direction

Orientation of the color palette.
plot.text

Should non-references bases be indicated above the bar?
plot.ref

If true show reference base instead of position on x-axis.
stack.plot

Show all variant alleles in a stacked bar plot.
classic.plot

Show classical debarcer amplicon plot with raw error.
fdr

False-discovery-rate cut-off for variants.
font.size

Font size
angle

Font angle
use.caller

Should data from variant caller be used? Default is FALSE
use.plotly

Should plotly be used instead of the regular ggplot device? Default is TRUE

Value

A UMIexperiment object containing a ggplot object with the amplicon plot.

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')
samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)
simsen <- createUmiExperiment(experimentName = 'example',mainDir = main,sampleNames = samples)
simsen <- filterUmiObject(simsen)

amplicon_plot <- AmpliconPlot(simsen)

Consensus depth histograms

Description

Generate histograms for the frequency of barcode family depths.

Usage

BarcodeFamilyHistogram(
  object,
  xMin = 0,
  xMax = 100,
  samples = NULL,
  option = "viridis",
  direction = 1,
  theme = "classic"
)

Arguments

object

Requires a UMI sample or UMI experiment object
xMin

Minimum consensus family size to plot, default is 0.
xMax

Maximum consensus family size to plot. Default is 100.
samples

List of samples to be shown.
option

Color scheme to use
direction

If using viridis colors sets the orientation of color scale.
theme

ggplot theme to use. Defaults to classic.

Value

A ggplot object

Examples

library(umiAnalyzer)
main = system.file('extdata', package = 'umiAnalyzer')
simsen <- createUmiExperiment(main, importBam = TRUE)
barcode_dist <- BarcodeFamilyHistogram(simsen)

Beta binomial model

Description

Code was obtained from VGAM package function VGAM::rbetabinom.ab. The VGAM package is available under the GPL-3 license and maintained by Thomas Yee <t.yee at auckland.ac.nz>. Source code of the function is identical to rbetabinom.ab, but the function name was changed to beta_binom.

Usage

beta_binom(n, size, shape1, shape2, limit.prob = 0.5, .dontuse.prob = NULL)

Arguments

n

n
size

size
shape1

alpha
shape2

beta
limit.prob

0.5
.dontuse.prob

NULL

Value

Numeric

References

Yee TW (2015). Vector Generalized Linear and Additive Models: With an Implementation in R. Springer, New York, USA.

Examples

beta_binom(10,5, 0.5, 1)
beta_binom(10,2, 0.5, 1)

callVariants using beta binomial distribution

Description

Calculate variant p-values using permutation-based testing. A prior is fitted to model the background error using maximum likelihood estimation of a beta distribution. The maximum likelihood estimate of the beta distribution is then used to define the shape of a beta-binomial distribution used to estimate variant P-Values. This can be interpreted as a probability for a variant to not have arisen by chance.

Usage

callVariants(object, minDepth = 3, minCoverage = 100, computePrior = FALSE)

Arguments

object

A UMIErrorCorrect object.
minDepth

Minimum consensus depth required default is 3
minCoverage

Minimum Coverage to use, default is 100 reads.
computePrior

Should a new distribution be derived from data? Default is FALSE.

Value

Object containing raw and FDR-adjusted P-Values

See Also

filterVariants on how to filter variants.

Examples

library(umiAnalyzer)
main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)
simsen <- filterUmiObject(simsen)
simsen <- callVariants(simsen, computePrior = FALSE)

Method for creating a UMI experiment object

Description

Method for creating a UMI experiment object

Usage

createUmiExperiment(
  mainDir,
  experimentName = NULL,
  sampleNames = NULL,
  importBam = FALSE,
  as.shiny = FALSE
)

Arguments

mainDir

Main experiment directory
experimentName

Name of the experiment
sampleNames

List of sample names. Can be either NULL or list. If NULL all subdirectories of mainDir will be searched.
importBam

Logical. Should bam files be imported on creation? Default is False.
as.shiny

Set to TRUE if run within a shiny::withProgress environment

Value

An object of class UMIexperiment

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')

samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)

exp1 <- createUmiExperiment(experimentName = 'exp1',mainDir = main,sampleNames = samples)

Method for creating a UMI experiment object

Description

Method for creating a UMI experiment object

Usage

createUMIexperiment_Debarcer(experiment.name, main.dir, dir.names)

Arguments

experiment.name

Name of the experiment
main.dir

Main experiment directory
dir.names

List of sample names

Value

A UMIexperiment object

createUmiSample

Description

Method for creating a UMI sample from UMIErrorCorrect output.

Usage

createUmiSample(sampleName, sampleDir, importBam = FALSE)

Arguments

sampleName

UMI sample object name
sampleDir

Path to UMI sample folders. Must be a folder generated by UMIErrorCorrect
importBam

Logical. Should BAM files be imported at object initialization? Default is False.

Value

An object of class UMIsample

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')
samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)
s1 <- createUmiSample('s1',sampleDir = paste(main,"/",samples[1],sep=""))

Method for creating a UMIsample object

Description

Method for creating a UMIsample object

Usage

createUMIsample_Debarcer(sample.name, sample.dir, cons = "10")

Arguments

sample.name

UMI sample object name
sample.dir

Path to UMI sample
cons

Consensus depth. Needs to be string; default is 10.

Value

A UMIsample object

Download meta data template

Description

Function for downloading a template file containing metadata.

Usage

download_template(object)

Arguments

object

A UMIexperiment object

Value

A tibble containing a metadata template

Examples

library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)

download_template(simsen)

Method for filtering UMIexperiment and sample objects

Description

Method for filtering UMIexperiment and sample objects

Usage

filterUmiObject(
  object,
  name = "default",
  minDepth = 3,
  minCoverage = 100,
  minFreq = 0,
  minCount = 0
)

Arguments

object

Requires a UMI sample or UMI experiment object.
name

String. Name of the filter. Default is "default".
minDepth

Consensus depth to analyze. Default is 3.
minCoverage

Minimum coverage required for amplicons. Default is 1.
minFreq

Minimum variant allele frequency to keep. Default is 0.
minCount

Minimum variant allele count to keep. Default is 3.

Value

A UMI sample or UMI experiment object.

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')

samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)

simsen <- createUmiExperiment(experimentName = 'simsen',mainDir = main,sampleNames = samples)

simsen <- filterUmiObject(simsen)

Filter variants based on p values or depth

Description

You can filter variants called with the the "callVariants" function based on adjusted p-value, minimum variant allele count and supply a list of assays and samples to plot.

Usage

filterVariants(
  object,
  p.adjust = 0.2,
  minVarCount = 5,
  amplicons = NULL,
  samples = NULL
)

Arguments

object

A UMIexperiment object
p.adjust

Numeric. Adjusted p value (FDR). Default is 0.2.
minVarCount

Integer. Minimum variant allele count. Default is 5.
amplicons

NULL or list of assays to plot. NULL uses all.
samples

NULL or list of samples to plot. NULL uses all.

Value

A UMIexperiment object with filtered variants. Can be used to generate VCF files.

See Also

callVariants on how to call variants.

Examples

## Not run: 
library(umiAnalyzer)
main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)
simsen <- filterUmiObject(simsen)
simsen <- callVariants(simsen, computePrior = FALSE)
simsen <- filterVariants(simsen, p.adjust = 0.05)

## End(Not run)

Find consensus reads A function to analyze consensus read tables generated with parseBamFiles or a UMIexperiment object containing reads.

Description

Find consensus reads A function to analyze consensus read tables generated with parseBamFiles or a UMIexperiment object containing reads.

Usage

findConsensusReads(
  object,
  consDepth = 0,
  groupBy = c("none", "sample", "position", "both"),
  pattern = NULL
)

Arguments

object

Either a tibble generated with parseBamFiles or a UMIexperiment object
consDepth

Minimum consensus depth to keep. Default is 0.
groupBy

Should data be grouped by position, sample, both or not at all.
pattern

Regular expression

Value

A data table

Examples

library(umiAnalyzer)
main <- system.file("extdata", package = "umiAnalyzer")
simsen <- createUmiExperiment(main, importBam = TRUE)

reads <- findConsensusReads(simsen)
reads

Generate VCF file from UMI sample or UMI experiment object

Description

Generate VCF file from UMI sample or UMI experiment object

Usage

generateVCF(object, outDir = getwd(), outFile, printAll = FALSE)

Arguments

object

Requires a UMI sample or UMI experiment object
outDir

String. Output directory, defaults to working directory.
outFile

String. Name of the output file
printAll

Logical. Should all or only trusted variant be printed?

Value

A VCF file

Examples

## Not run: 
library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)

simsen <- filterUmiObject(simsen)

generateVCF(simsen,'simsen.vcf', printAll = FALSE, save = FALSE)

## End(Not run)

Method for retrieving filtered data

Description

Method for retrieving filtered data

Usage

getFilteredData(
  object,
  name = "default",
  save = FALSE,
  outDir = getwd(),
  fileName = NULL,
  delim = ";"
)

Arguments

object

Requires a UMI sample or UMI experiment object.
name

String. Name of the filter. Default is "default".
save

Logical, should data be saved as csv file? Default is FALSE.
outDir

Output directory
fileName

Filename to be used, default is the same as 'name'
delim

Character string denoting delimiter to be used, default is ';'.

Value

A filtered consensus table, as a tibble.

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')

samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)

simsen <- createUmiExperiment(experimentName = 'simsen',mainDir = main,sampleNames = samples)
simsen <- filterUmiObject(simsen)

myfilter <- getFilteredData(simsen)
myfilter

Retrieve meta data by name.

Description

Retrieve meta data by name.

Usage

getMetaData(object, attributeName)

Arguments

object

R object from which to get meta data.
attributeName

Name of the meta data attribute.

Value

Metadata

Examples

library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)

metaData <- system.file("extdata", "metadata.txt", package = "umiAnalyzer")

simsen <- importDesign(object = simsen,file = metaData)
design <- getMetaData(object = simsen, attributeName = "design")

Import bed file

Description

Import bed file

Usage

importBedFile(path)

Arguments

path

path to bed file

Value

A table containing genome positions

Examples

library(umiAnalyzer)

bed_dir <- system.file("extdata", "simple.bed", package = "umiAnalyzer")
bed <- importBedFile(path = bed_dir)

Import experimental design meta data such as replicates, treatments, categorical variables.

Description

Import experimental design meta data such as replicates, treatments, categorical variables.

Usage

importDesign(object, file, delim = NULL)

Arguments

object

UMI.experiment to which to add metadata
file

File containing meta data
delim

Column separator. Default is NULL (automatically determine delimiter)

Value

A UMIexperiment object

Examples

library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)

metaData <- system.file("extdata", "metadata.txt", package = "umiAnalyzer")

simsen <- importDesign(object = simsen,file = metaData)

# Retrieve meta data
design <- getMetaData(object = simsen, attributeName = "design")
design

Merge assays

Description

Merge assays together by name. Requires a name of the new assay and a list of assays that will be merged.

Usage

mergeAssays(object, name, assay.list)

Arguments

object

A UMIexperiment object
name

Name of the new assay
assay.list

List of assays to merge

Value

merged consensus data

Examples

library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")

simsen <- createUmiExperiment(main)

simsen <- mergeAssays(object = simsen,name = "new",assay.list = c("PIK3CA_123", "PIK3CA_234"))

Function to parse bam files

Description

Function to parse bam files

Usage

parseBamFiles(mainDir, sampleNames = NULL, consDepth = 0, as.shiny = FALSE)

Arguments

mainDir

Directory containing UMIErrorCorrect output folders.
sampleNames

A list of sample names.
consDepth

Only retain consensus reads of at least cons.depth. Default is 0.
as.shiny

Set to TRUE if run within a shiny::withProgress environment

Value

A data table

Examples

library(umiAnalyzer)
main <- system.file("extdata", package = "umiAnalyzer")
simsen <- createUmiExperiment(main)

reads <- parseBamFiles(main, consDepth = 10)

Generate QC plots

Description

Visualize the UMI count for each selected assay and sample for a given consensus depth. This is useful to detect differences in coverage, especially for multiplexed assays.

Usage

QCplot(
  object,
  group.by = "sample",
  plotDepth = 3,
  assays = NULL,
  samples = NULL,
  theme = "classic",
  option = "viridis",
  direction = "default",
  toggle_mean = TRUE,
  center = "mean",
  line_col = "blue",
  angle = 0,
  plotly = FALSE
)

Arguments

object

Requires a UMI sample or UMI experiment object
group.by

String. Which variable should be used as a factor on the x-axis. Default is sample
plotDepth

Which consensus depth to plot
assays

(Optional) user-supplied list of assays to plot. Default is all.
samples

(Optional) user-supplied list of samples to plot. Default is all.
theme

ggplot theme to use.
option

Color palette to use, either ggplot default or viridis colors.
direction

If viridis colors are used, choose orientation of color scale.
toggle_mean

Show mean or median
center

Choose mean or median
line_col

Choose color for mean/median line
angle

Angle of labels on x-axis.
plotly

Should plotly be used for rendering?

Value

A ggplot object

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')
samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)
simsen <- createUmiExperiment(experimentName = 'example',mainDir = main,sampleNames = samples)

depth_plot <- QCplot(simsen)

Function to run the umiVisualizer shiny app

Description

Function to run the umiVisualizer shiny app

Usage

runUmiVisualizer()

Value

Opens the umiVisualizer app

Examples

## Not run: 
library(umiAnalyzer)

runUmiVisualizer()

## End(Not run)

Save consensus data

Description

If save is set to TRUE data will be written to a csv file otherwise consensus data will be returned as a tibble.

Usage

saveConsData(
  object,
  save = FALSE,
  fileName = "consensus_data.csv",
  outDir = getwd(),
  delim = ";"
)

Arguments

object

UMIexperiment object
save

Logical. Should data be saved to file? Default is FALSE.
fileName

String. Name of the file to be saved. Default is 'consensus_data.csv'
outDir

output directory, defaults to working directory
delim

Single character string, either ';' or ',' or tab

Value

A data table

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')

samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)

example <- createUmiExperiment(experimentName = 'example',mainDir = main,sampleNames = samples)

consensus_data <- saveConsData(object = example)
consensus_data

UMIexperiment data generated with SiMSen-Seq

Description

UMIexperiment data generated with SiMSen-Seq

Format

An object of class "UMIexperiment"

Plot time series data

Description

Function for plotting time series or other meta data. Uses facet wrap to display user-provided categorical variables.

Usage

timeSeriesGrid(
  object,
  filter.name = "default",
  cut.off = 5,
  min.count = 0,
  min.vaf = 0,
  amplicons = NULL,
  samples = NULL,
  x_variable = NULL,
  y_variable = "Max Non-ref Allele Frequency",
  columns = "Sample Name",
  rows = "Name",
  color_by = "Name",
  fdr = 0.05,
  use.caller = TRUE,
  bed_positions = NULL
)

Arguments

object

A consensus data table
filter.name

"default"
cut.off

5
min.count

0
min.vaf

0
amplicons

NULL
samples

NULL
x_variable

NULL
y_variable

"Max Non-ref Allele Frequency"
columns

"Sample Name"
rows

"Name"
color_by

"Name"
fdr

0.05
use.caller

TRUE
bed_positions

NULL

Value

A ggplot object.

Examples

library(umiAnalyzer)

main <- system.file("extdata", package = "umiAnalyzer")
simsen <- createUmiExperiment(main)
simsen <- filterUmiObject(simsen)

metaData <- system.file("extdata", "metadata.txt", package = "umiAnalyzer")
simsen <- importDesign(object = simsen,file = metaData)

bed_dir <- system.file("extdata", "simple.bed", package = "umiAnalyzer")
bed <- importBedFile(path = bed_dir)

time_plot <- timeSeriesGrid(simsen, x_variable = "time", bed_positions = bed)

Plot UMI counts

Description

Visualize the number detected UMI for each consensus depth cut-off. This may may helpful in choosing the right consensus depth for your analysis, by checking the number of reads still available for each assay and sample for your chosen cut-off.

Usage

UmiCountsPlot(
  object,
  amplicons = NULL,
  samples = NULL,
  theme = "classic",
  option = "viridis",
  direction = 1
)

Arguments

object

Requires a UMI sample or UMI experiment object
amplicons

(Optional) user-supplied list of assays to plot. Default is all.
samples

(Optional) user-supplied list of samples to plot. Default is all.
theme

Plotting theme, default is classic
option

Color palette. Default uses ggplot standard, otherwise viridis options.
direction

If using viridis colors should the scale be inverted or default?

Value

A UMIexperiment object

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')
samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)
simsen <- createUmiExperiment(experimentName = 'example',mainDir = main,sampleNames = samples)
simsen <- filterUmiObject(simsen)

count_plot <- UmiCountsPlot(simsen)

UMIexperiment class

Description

The UMIexperiment is the core data object, storing all data and relevant analysis data associated with your experiment. Each object has number of slots storing raw data, graphs and processed data.

Value

An object of class UMIexperiment

Slots

name

Optional project name for record keeping.

cons.data

The raw consensus data supplied by the user.

summary.data

Summary data from UMIErrorCorrect

raw.error

Cons0 error profile

reads

Consensus reads imported using the parseBamFiles function.

meta.data

Sample data optionally supplied by the user.

filters

A list of filtered cons.data, which can be accessed separately.

plots

A list of generated plots.

variants

Consensus table generated with the umiAnalyzer variant caller.

merged.data

Data generated using the mergeTechnicalReplicates function.

UMIsample class

Description

UMIsample class

Value

An object of class UMIsample

Slots

name

Sample name

cons.data

Raw consensus data

summary.data

Summary data from UMIErrorCorrect

reads

Consensus reads imported from a bam file.