Uncovering the consequences of batch effect associated missing values in omics data analysis

ABSTRACTStatistical analyses in high-dimensional omics data are often hampered by the presence of batch effects (BEs) and missing values (MVs), but the interaction between these two issues is not well-studied nor understood. MVs may manifest as a BE when their proportions differ across batches. These are termed as Batch-Effect Associated Missing values (BEAMs). We hypothesized that BEAMs in data may introduce bias which can impede the performance of missing value imputation (MVI). To test this, we simulated data with two batches, then introduced over 100 iterations, either 20% and 40% MVs in each batch (BEAMs) or 30% in both (control). K-nearest neighbours (KNN) was then used to perform MVI, in a typical global approach (M1) and a supposed superior batch-sensitized approach (M2). BEs were then corrected using ComBat. The effectiveness of the MVI was evaluated by its imputation accuracy and true and false positive rates. Notably, when BEAMs existed, M2 was generally undesirable as the differing application of MV filtering in M1 and M2 strategies resulted in an overall coverage deficiency. Additionally, both M1 and M2 strategies suffered in the presence of BEAMs, highlighting the need for a novel approach to handle MVI in data with BEAMs.Author summaryData in high-throughput omics data are often combined from different sources (batches), which creates batch effects in the data. Missing values are a common occurrence in these data, and their proportions are assumed to be equal across batches. However, instances exist when these proportions vary between batches, such as one batch having more missing values than another, resulting in batch effect associated missing values. Missing values are often dealt with through missing value imputation, but whether the variation in missing value proportions across batches affects imputation outcomes is unknown. In this paper, we investigate the consequence of performing imputation when this issue persists. We simulated data with equal and unequal missing value proportions, then assessed the performance of k-nearest neighbours imputation by its imputation accuracy and downstream analysis outcomes. This revealed that unequal missing value proportions worsens imputation and establishes the need for smarter imputation strategies to handle this complication.