analyzeMPRA: Analyze MPRA data using specified tests
In redaq/atMPRA: Analysis Toolset for MPRA data

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/analyzeMPRA.R

This function pre-processes the given MPRA data, and analyzes it using specified tests.

1	analyzeMPRA(datt, nrepIn, rnaCol, nrepOut, nsim, ntag, method = c("MW", "Matching", "Adaptive", "Fisher", "QuASAR", "T-test", "mpralm", "edgeR", "DESeq2"), cutoff = -1, cutoffo = -1, matched=FALSE)

`datt`	A data frame containing the MPRA dataset. It should have nsimntag2 rows and 2+nrepIn+nrepOut columns. The first column should be named 'allele', and the second column should be named 'simN'. The 'allele' columns should contain only two possible values 'Ref' and 'Mut' to refer to the two versions of alleles for each SNP.
`nrepIn`	An integer indicating the number of DNA replicates.
`rnaCol`	An integer indicating the starting column of the RNA replicates in `datt`.
`nrepOut`	An integer indicating the number of RNA replicates.
`nsim`	An integer indicating the number of SNPs/comparisons in the MPRA data. A comparison refer to the unit with two alleles for testing allele-specific expression.
`ntag`	An integer indicating the number of tags/barcodes for each allele.
`method`	A vector of characters specifying the tests to be used. The possible options are: MW, Matching, Adaptive, Fisher, QuASAR, T-test, mpralm, edgeR and DESeq2.
`cutoff`	A numeric or integer value. Tags with DNA count less than or equal to `cutoff` in any of the DNA replicates will be removed.
`cutoffo`	A numeric or integer value. Tags with RNA count less than or equal to `cutoffo` in any of the RNA replicates will be removed.
`matched`	Whether the DNA samples are matching to the RNA samples in the correct order in the data frame `datt`.

This function first normalizes the replicates using the maximum depth across all replicates, and filters the tags according to the specified cutoffs. Then it analyzes the processed MPRA data using the specified tests.

results

The actual p-values of all the SNPs for the specified tests using the given dataset.

Dandi Qiao

Qiao, D., Zigler, C., Cho, M.H., Silverman, E.K., Zhou, X., et al. (2018). Statistical considerations for the analysis of massively parallel reporter assays data.

atMPRA atMPRA atMPRA atMPRA

nsim = 10
ntag = 10
slope=c(rep(1, ntag*nsim), rep(1.5, ntag*nsim)) 
nrep=5
data(datMean)
simData = sim_fixTotalD(datMean=datMean, totalDepth=200000, sigma2_DNA_a0=0.001, sigma2_DNA_a1=0.23, sigma2_RNA_a0=0.18, sigma2_RNA_a1=35, ntag=ntag, nsim=nsim, nrep=nrep, slope=slope)
results = analyzeMPRA(simData, nrep, rnaCol=2+nrep+1, nrep, nsim, ntag, method=c("MW","mpralm", "edgeR", "DESeq2"), cutoff=0, cutoffo=0)