| Title: | Detection of Chromosomal Aneuploidies in Ancient DNA Studies |
| Version: | 1.0.0 |
| 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). |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Imports: | checkmate, cowplot, dplyr, EnvStats, ggplot2, ggrepel, ggsci, ggstar, magrittr, matrixStats, mclust, rstatix, sirt, stringr, tibble, tidyr, readr |
| LazyData: | true |
| Suggests: | knitr, rmarkdown, extraDistr |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 3.5.0) |
| URL: | https://jonotuke.github.io/RChASM/ |
| NeedsCompilation: | no |
| Packaged: | 2026-02-26 00:12:52 UTC; jonathantuke |
| Author: | Adam B. Rohrlach 
[aut, cph],
Jono Tuke [aut,
cre],
Wolfgang Haak
[aut] |
| Maintainer: | Jono Tuke <simon.tuke@adelaide.edu.au> |
| Repository: | CRAN |
| Date/Publication: | 2026-03-03 21:20:42 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs).
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)