DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

By Christopher S. McGinnis, Lyndsay M. Murrow, Zev J. Gartner. bioRxiv, 2018

3,498 citations recorded by the source corpus.

Single-cell RNA sequencing (scRNA-seq) using droplet microfluidics occasionally produces transcriptome data representing more than one cell. These technical artifacts are caused by cell doublets formed during cell capture and occur at a frequency proportional to the total number of sequenced cells. The presence of doublets can lead to spurious biological conclusions, which justifies the practice of sequencing fewer cells to limit doublet formation rates. Here, we present a computational doublet detection tool – DoubletFinder – that identifies doublets based solely on gene expression features. DoubletFinder infers the putative gene expression profile of real doublets by generating artificial doublets from existing scRNA-seq data. Neighborhood detection in gene expression space then identifies sequenced cells with increased probability of being doublets based on their proximity to artificial doublets. DoubletFinder robustly identifies doublets across scRNA-seq datasets with variable numbers of cells and sequencing depth, and predicts false-negative and false-positive doublets defined using conventional barcoding approaches. We anticipate that DoubletFinder will aid in scRNA-seq data analysis and will increase the throughput and accuracy of scRNA-seq experiments.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/64977a3fee694997b4751706878d9001ac32f334

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. Vladimir Yu. Kiselev
2. Martin Hemberg
3. Tallulah S. Andrews

In Molecular Biology

1. Ernest Arenas
2. Zizhen Yao
3. Hongkui Zeng
4. Vladimir Yu. Kiselev
5. Susan Sunkin
6. Bosiljka Tasic
7. Martin Hemberg
8. Tallulah S. Andrews

Citations in the corpus

• Challenges in unsupervised clustering of single-cell RNA-seq data (2019, methodology): “Several tools have been developed whereby syn­ thetic doublets are generated computationally for a given data set, and an algorithm is trained to identify them and is then applied to the original data 66–68 .”
• Transcriptomic diversity of cell types across the adult human brain (2022, methodology): “All remaining samples were pooled with their replicates and analyzed with “cytograph qc” (commit 5f07e59768844e3c84fceb8fd7d1993059e13a7e), which uses a modified version of DoubletFinder to calculate a doublet score for each cell (28).”
• A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2020, positive): “Doublets were identified using a modified version of the DoubletFinder algorithm (McGinnis et al., 2019) and removed when doublet score > 0.”