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Abstract:
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Sequencing and microarray samples often need to be collected or processed in multiple batches or at different times. This often leads to known and unknown technical biases that unless properly adjusted for, can lead to incorrect results in the downstream analysis. There are several batch adjustment tools that are currently available, but none of them can indicate a prior whether batch adjustment need to be conducted and how it should be applied before proceeding with the analysis. We present a new software pipeline called BatchQC that addresses these issues by visually and statistically evaluating whether batch effects exist and can automatically adjust for these effects using the best possible methods. BatchQC can also perform differential expression analysis using LIMMA, Generalized Least Squares and Mixed Effects model to pick the top differentially expressed genes for a biological condition. We have used the BatchQC pipeline on both simulated data and real data samples to establish the efficiency of the software pipeline.
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