BatchCorrectedCounts {CountClust} | R Documentation

## Obtain Batch effect Corrected counts

### Description

This function first converts counts data to log CPM data , then apply a linear model with the batch effect as a factor. We take the sum of intercept, residuals and mean batch effect across all the batches and then inverse transform it back to counts to get rid of batch effects.

### Usage

    BatchCorrectedCounts(data, batch_lab, use_parallel = TRUE)

### Arguments

`data` | count matrix, with samples along the rows and features along the columns.
`batch_lab` | batch label vector.
`use_parallel` | if TRUE, we do a parallel analysis over features, else serial application.

### Value

Returns a counts data. with same dimension as the input data, but which is corrected for batch_lab.

### Examples

    # Simulation example
    N=500;
    K=4;
    G=100;
    Label.Batch=c(rep(1,N/4),rep(2,N/4),rep(3,N/4),rep(4,N/4));
    alpha_true=matrix(rnorm((K)*G,0.5,1),nrow=(K));
    library(gtools)
    tt <- 10;
    omega_true = matrix(rbind(rdirichlet(tt*10,c(3,4,2,6)),
                             rdirichlet(tt*10,c(1,4,6,3)),
                             rdirichlet(tt*10,c(4,1,2,2)),
                             rdirichlet(tt*10,c(2,6,3,2)),
                             rdirichlet(tt*10,c(3,3,5,4))), nrow=N);
    B=max(Label.Batch);
    sigmab_true=2;
    beta_true=matrix(0,B,G);
    for(g in 1:G)
    {
        beta_true[,g]=rnorm(B,mean=0,sd=sigmab_true);
    }
    read_counts=matrix(0,N,G);
    for(n in 1:N){
        for(g in 1:G)
        {
            read_counts[n,g]=rpois(1, omega_true[n,]%*%exp(alpha_true[,g]
                                                          + beta_true[Label.Batch[n],g]));
       }
    }

    batchcorrect_counts <- BatchCorrectedCounts(read_counts, Label.Batch,
                                         use_parallel=FALSE)

[Package CountClust version 1.10.1 Index]