The task

Single-cell RNA-Seq protocols only detect a small fraction of the mRNA molecules present in each cell. As a result, the measurements (UMI counts) observed for each gene and each cell are associated with generally high levels of technical noise (Grün et al., 2014). Denoising describes the task of estimating the true expression level of each gene in each cell. In the single-cell literature, this task is also referred to as imputation, a term which is typically used for missing data problems in statistics. Similar to the use of the terms “dropout”, “missing data”, and “technical zeros”, this terminology can create confusion about the underlying measurement process (Sarkar and Stephens, 2020).

A key challenge in evaluating denoising methods is the general lack of a ground truth. A recent benchmark study (Hou et al., 2020) relied on flow-sorted datasets, mixture control experiments (Tian et al., 2019), and comparisons with bulk RNA-Seq data. Since each of these approaches suffers from specific limitations, it is difficult to combine these different approaches into a single quantitative measure of denoising accuracy. Here, we instead rely on an approach termed molecular cross-validation (MCV), which was specifically developed to quantify denoising accuracy in the absence of a ground truth (Batson et al., 2019). In MCV, the observed molecules in a given scRNA-Seq dataset are first partitioned between a training and a test dataset. Next, a denoising method is applied to the training dataset. Finally, denoising accuracy is measured by comparing the result to the test dataset. The authors show that both in theory and in practice, the measured denoising accuracy is representative of the accuracy that would be obtained on a ground truth dataset.

Metrics for data denoising aim to