README.md

In jeremymcrae/cifer: CNV Inheritance from Exome Read-Depths

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CIFER: CNV of inheritance from exome read-depth

This package attempts to predict the inheritance state of CNV calls generated by ConVex. This package compares parental log2-ratio scores within the CNV call to a model constructed from the log2-ratio scores in a population of unrelated trios (specifically, the parents of those trios).

This package determines the parameters of the null model, and estimates the probability that each parent's log-2 ratio data belongs to the null model. The estimated probabilities are used to assign both parents into inheritance categories, which are then used to assign an inheritance state for the proband.

The code is available at: https://github.com/jeremymcrae/cifer, and can be obtained with: git clone https://github.com/jeremymcrae/cifer.git

Installation

The package can be installed or updated using R 3.1.0 or greater with:

library(devtools) # if necessary install with install.packages("devtools")
devtools::install_github("jeremymcrae/cifer")

# Alternatively, clone the repository, run R 3.1 from within the top level of
# the repository and use the devtools::build() to build the package for other R
# versions.

Predicting inheritance

Using the installed package should involve loading the library and calling the classification function. Here's a somewhat artificial example:

library(cifer)

# define a cohort of 500 individuals
cohort_n <- 500
sample_ids <- paste("sample", 1:cohort_n, sep="_")

# define the sample IDs for the cohort, and whether those individuals are
# probands (since we determine the null distribution only from parents).
samples <- data.frame("individual_id"=sample_ids,
    "is_proband"=c(rep(FALSE, length(sample_ids) - 1), TRUE))

# define the population as having probes values cenetred around zero
probes <- data.frame(matrix(rnorm(length(sample_ids) * 5), nrow=5))
names(probes) <- sample_ids

# set the child probe values to distant from the population values
probes[sample_ids[length(sample_ids)]] <- rnorm(5, mean=10, sd=1)

# define the sample IDs for the trio members
child_id <- sample_ids[length(sample_ids)]
mom_id <- sample_ids[1]
dad_id <- sample_ids[2]

# classify the inheritance state of this CNV call
inheritance <- process_cnv_call(samples, probes, child_id, mom_id, dad_id)

# access the inheritance prediction
inheritance <- prediction$inheritance

It's up to you to load the probe data for your CNV call, and to define your cohort sample IDs and trio IDs for the proband being analysed.

jeremymcrae/cifer documentation built on May 19, 2019, 5:08 a.m.