SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

By Matthew D. Young, Sam Behjati. bioRxiv, 2018

1,280 citations recorded by the source corpus.

Droplet based single cell RNA sequence analyses assume all acquired RNAs are endogenous to cells. However, any cell free RNAs contained within the input solution are also captured by these assays. This sequencing of cell free RNA constitutes a background contamination that has the potential to confound the correct biological interpretation of single cell transcriptomic data. Here, we demonstrate that contamination from this “soup” of cell free RNAs is ubiquitous, experiment specific in its composition and magnitude, and can lead to erroneous biological conclusions. We present a method, SoupX, for quantifying the extent of the contamination and estimating “background corrected”, cell expression profiles that can be integrated with existing downstream analysis tools. We apply this method to two data-sets and show that the application of this method reduces batch effects, strengthens cell-specific quality control and improves biological interpretation

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e2af27bc9dc56dffc8b7cdf12c4bf22787407d0e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Malte D. Luecken
2. Aviv Regev
3. Naftali Kaminski
4. Fabian J Theis
5. R. Hamanaka
6. R. Guzy
7. Navdeep S. Chandel

In Molecular Biology

1. Malte D. Luecken
2. Aviv Regev
3. Fabian J Theis
4. H. Schiller
5. Alvis Brazma
6. Stephen R. Quake
7. Kerstin B. Meyer
8. Naftali Kaminski

Citations in the corpus

• SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues (2020, positive): “SoupX was utilized to reduce ambient RNA bias, using default parameters with pCut set to 0.3, and was applied to each sample before merging the count matrices (Young and Behjati, 2020).”
• Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis (2019, methodology): “Computational methods to address this problem are being developed, but are not yet validated (28).”
• Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “The recently developed SoupX (preprint: Young & Behjati, 2018) uses this approach to directly correct the count data.”