# Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach

## TL;DR

This paper introduces a new method called scDDI to better detect and fill in missing gene expression data in single-cell RNA sequencing.

## Contribution

The novel scDDI method combines a Poisson–negative binomial mixture model with decision tree regression for improved dropout detection and imputation.

## Key findings

- scDDI outperforms existing methods in dropout detection and imputation on both simulated and real datasets.
- Improved imputation leads to better performance in downstream tasks like clustering and subpopulation identification.

## Abstract

Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to capture gene expression patterns within individual cells. However, due to the limited RNA content within cells, dropout events occur, resulting in a substantial number of zero counts in the single-cell expression matrix. To address this issue, we propose a novel method called single-cell dropout detection and imputation (scDDI). This method identifies dropout events using a Poisson–negative binomial mixture model and subsequently imputes the missing values using a decision tree regression model. We evaluate the performance of scDDI on both simulated and real scRNA-seq datasets, demonstrating its superiority over established single-cell imputation techniques. Notably, scDDI significantly improves dropout detection, leading to enhanced performance in various downstream analysis tasks like gene expression recovery, cell clustering, and cell subpopulation identification.