Characterizing contaminant noise in barcoded perturbation experiments

TL;DR

This paper analyzes contaminant noise in barcoded perturbation experiments, revealing how cell bursting constrains noise distribution and proposing methods for improved decontamination, validated across multiple biological replicates and sequencing protocols.

Contribution

It provides an analytical characterization of contaminant noise in perturbation experiments and introduces a noise-aware decontamination method that enhances data accuracy.

Findings

Decontaminated counts align with bulk genomic measurements.

The method corrects for sequencing protocol differences.

Cell bursting constrains the noise distribution.

Abstract

Bursting cells lead to ambient RNA that contaminates sequencing data. This process is especially problematic in perturbation experiments where transcription factors are implanted into cells to determine their effects. The presence of contaminants makes it difficult to determine whether a factor is truly expressed in the cell. This paper studies the properties of contaminant noise from an analytical perspective, showing that the cell bursting process constrains the form of the noise distribution across factors. These constraints can be leveraged to improve decontamination by removing counts that are more likely the result of noise than expression. In two biological replicates of a perturbation experiment, run across two sequencing protocols, decontaminated counts agree with bulk genomic measurements of the transduction rate and are automatically corrected for differences in sequencing.