Package 'RChASM'

Title: Detection of Chromosomal Aneuploidies in Ancient DNA Studies
Description: An R implementation of ChASM (Chromosomal Aneuploidy Screening Methodology): a statistically rigorous Bayesian approach for screening data sets for autosomal and sex chromosomal aneuploidies. This package takes as input the number of (deduplicated) reads mapping to chromosomes 1-22 and the X and Y chromosomes, and models these using a Dirichlet-multinomial distribution. From this, This package returns posterior probabilities of sex chromosomal karyotypes (XX, XY, XXY, XYY, XXX and X) and full autosomal aneuploidies (trisomy 13, trisomy 18 and trisomy 21). This package also returns two diagnostic statistics: (i) a posterior probability addressing whether contamination between XX and XY may explain the observed sex chromosomal aneuploidy, and (ii) a chi-squared statistic measuring whether the observed read counts are too divergent from the underlying distribution (and may represent abnormal sequencing/quality issues).
Authors: Adam B. Rohrlach [aut, cph] (ORCID: <https://orcid.org/0000-0002-4204-5018>), Jono Tuke [aut, cre] (ORCID: <https://orcid.org/0000-0002-1688-8951>), Wolfgang Haak [aut] (ORCID: <https://orcid.org/0000-0003-2475-2007>)
Maintainer: Jono Tuke <[email protected]>
License: MIT + file LICENSE
Version: 1.0.1
Built: 2026-06-05 06:41:04 UTC
Source: https://github.com/jonotuke/rchasm

Help Index

Adjusts alpha vector for contamination karyotypes

Description

Adjusts alpha vector for contamination karyotypes

Usage

adjustA(a, c1, c2, g, correction = 1e-06)

Arguments

a

vector to change
c1

X multiplier for karyotype 1
c2

X multiplier for karyotype 2
g

mixing parameter
correction

error rate for Y mapping for non-Y karyotypes

Value

adjusted alpha vector

bounds number to be in [0,1] (contamination estimation)

Description

bounds number to be in [0,1] (contamination estimation)

Usage

bound(x)

Arguments

x

number

Value

bounded number

Take prior and observed counts and calls karyotypes and Z-scores

Description

Take prior and observed counts and calls karyotypes and Z-scores

Usage

callKaryotypes(indat, inDirichlet, p_contamination = 0.1)

Arguments

indat

full input tibble
inDirichlet

associated Dirichlet parameters
p_contamination

the assumed probability of contamination

Value

karyotype calls

combine data

Description

Combine both autosomal and sex chromosomal analyses into one output

Usage

combineData(calls.auto, calls.sca, z.scores, printMissingIDs = FALSE)

Arguments

calls.auto

a list or predefined piped string of sample IDs to look for
calls.sca

the autosomal karyotype calls for the samples
z.scores

the sex chromosomal karyotype calls for the samples
printMissingIDs

print the IDs of individuals not in all 3 input files (or just numbers)?

Value

combined data

calculate Dirichlet probabilities with filtering

Description

calculate Dirichlet probabilities with filtering

Usage

ddirichletultinomial(nvector, avector, cvector, log = TRUE, correction = 1e-06)

Arguments

nvector

observed read counts
avector

alpha vector to change
cvector

change vector for new karyotype
log

return value in log space
correction

error rate for Y mapping for non-Y karyotypes

Value

Dirichlet probabilities

calculate Dirichlet parameters with filtering

Description

calculate Dirichlet parameters with filtering

Usage

dirichlet.mle_filter(y, whichK)

Arguments

y

input values
whichK

which parameter to return

Value

parameters

Estimate contamination from XX and XY based on observed counts

Description

Estimate contamination from XX and XY based on observed counts

Usage

estimateGamma(nvector, avector, c1, c2, correction)

Arguments

nvector

observed read counts
avector

vector to change
c1

X multiplier for karyotype 1
c2

X multiplier for karyotype 2
correction

error rate for Y mapping for non-Y karyotypes

Value

estimated contamination

example_data

Description

An example data set for RChASM

Usage

example_data

Format

A data frame with 266 rows and 26 variables:

Source

"Ben Rohrlach"

filter for outliers

Description

filter for outliers

Usage

filterOutliers(v)

Arguments

v

vector to filter

Value

vector for filtering

make c vector for new karyotypes

Description

make c vector for new karyotypes

Usage

makeC(x, y, n)

Arguments

x

value to replace 1
y

location to make replacement
n

length of vector

Value

c vector

function to make a Dirichlet prior

Description

function to make a Dirichlet prior

Usage

makeDirichlet(
  indat,
  refType,
  min_reads = 30000,
  max_reads = 1e+09,
  show_plot = TRUE
)

Arguments

indat

full input tibble
refType

"auto"="autosomal"; "sca"="sex chromosomal"; "diagnostic"
min_reads

min number of reads per sample
max_reads

max number of reads per sample
show_plot

boolean to show plot

Value

Dirichlet prior

Calculates Z-scores per chromosome

Description

Calculates Z-scores per chromosome

Usage

makeZscores(indat, refType = "auto", min_reads = 30000, max_reads = 1e+09)

Arguments

indat

full input tibble
refType

"auto"="autosomal"; "sca"="sex chromosomal"; "diagnostic"
min_reads

min number of reads per sample
max_reads

max number of reads per sample

Value

Z-scores

Plots diagnostic plots

Description

Plots diagnostic plots

Usage

plot_diagnostic(IDs, inChASM, addLabels = FALSE)

Arguments

IDs

a list or predefined piped string of sample IDs to look for
inChASM

parameter object
addLabels

add "A", "B" and "C" to the plot panels?

Value

plots

Examples

example_calls <- runChASM(rawReadCountsIn = example_data)
plot_diagnostic(IDs = 'Ind_255_1', inChASM = example_calls, addLabels = TRUE)

A function to print the result of the combined analysis to screen

Description

A function to print the result of the combined analysis to screen

Usage

printChASM(inChASM, lines = 20)

Arguments

inChASM

the result of a full ChASM analysis (output from runChASM)
lines

the number of lines to print to screen

Value

print output

Examples

example_calls <- runChASM(rawReadCountsIn = example_data)
printChASM(inChASM = example_calls, lines = 10)

processReadCounts

Description

Task: takes read counts and makes useful file

Usage

processReadCounts(
  rawReadCountsIn,
  refType,
  minTotal = 1000,
  minSamplesPerProtocol = 30
)

Arguments

rawReadCountsIn

the reads counts via KP
refType

for capture, use only on-target (F)?
minTotal

minimum number of protocol counts to be included
minSamplesPerProtocol

"auto"="autosomal"; "sca"="sex chromosomal"

Value

outreadcounts

Title

Description

Title

Usage

runChASM(
  rawReadCountsIn,
  minSamplesPerProtocol = 30,
  min_reads = 60000,
  max_reads = 1e+09,
  p_contamination = 0.01,
  show_plot = TRUE,
  printMissingIDs = FALSE
)

Arguments

rawReadCountsIn

the reads counts for each chromosome
minSamplesPerProtocol

minimum number of reads per protocol for parameter estimation
min_reads

the minimum number of reads for Dirichlet parameter estimation
max_reads

the maximum number of reads for Dirichlet parameter estimation
p_contamination

the probability of a sample yielding significant contamination
show_plot

show the clustering plot for sex chromosomal aneuploidy Dirichlet estimation?
printMissingIDs

when combining karyotype calls, return names that are missing (or just the number of missing IDs)?

Value

summary

Examples

runChASM(rawReadCountsIn = example_data)

A function for saving the results of a ChASM analysis as a tsv

Description

A function for saving the results of a ChASM analysis as a tsv

Usage

saveChASM(inChASM, file, sort_by_samplename = FALSE)

Arguments

inChASM

the result of a full ChASM analysis (output from runChASM)
file

the full path and file name to place output
sort_by_samplename

reorder alphabetically by sample names?

Value

saves results

Examples

example_calls <- runChASM(rawReadCountsIn = example_data)
## Not run: 
saveChASM(inChASM = example_calls)

## End(Not run)

summary_calls

Description

Combine both autosomal and sex chromosomal analyses into one output

Usage

summary_calls(
  inChASM,
  minTotal = 60000,
  minPosterior = 0.95,
  ignoreUnusual = FALSE,
  printProtocol = FALSE
)

Arguments

inChASM

the result of combining both autosomal and sca analyses
minTotal

minimum total for autosomal or sca analyses
minPosterior

minimum value maxP can take (i.e. minimum posterior probability accepted)
ignoreUnusual

filter our unusual observations?
printProtocol

print protocol column?

Value

combined calls

Examples

example_calls <- runChASM(rawReadCountsIn = example_data)
summary_calls(inChASM = example_calls, minTotal = 6e4, minPosterior = 0.95)