BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm.

ABSTRACT:

Motivation

In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale.

Results

BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series

SUBMITTER: Papiez A 

PROVIDER: S-EPMC6546123 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature