Single-Cell Remover of Doublets
Python code for identifying doublets in single-cell RNA-seq data. For details and validation of the method, see our paper in Cell Systems or the preprint on bioRxiv.
For a typical workflow, including interpretation of predicted doublet scores, see the example notebook.
Given a raw (unnormalized) UMI counts matrix counts_matrix with cells as rows and genes as columns, calculate a doublet score for each cell:
import scrublet as scr
scrub = scr.Scrublet(counts_matrix)
doublet_scores, predicted_doublets = scrub.scrub_doublets()scr.scrub_doublets() simulates doublets from the observed data and uses a k-nearest-neighbor classifier to calculate a continuous doublet_score (between 0 and 1) for each transcriptome. The score is automatically thresholded to generate predicted_doublets, a boolean array that is True for predicted doublets and False otherwise.
- When working with data from multiple samples, run Scrublet on each sample separately. Because Scrublet is designed to detect technical doublets formed by the random co-encapsulation of two cells, it may perform poorly on merged datasets where the cell type proportions are not representative of any single sample.
- Check that the doublet score threshold is reasonable (in an ideal case, separating the two peaks of a bimodal simulated doublet score histogram, as in this example), and adjust manually if necessary.
- Visualize the doublet predictions in a 2-D embedding (e.g., UMAP or t-SNE). Predicted doublets should mostly co-localize (possibly in multiple clusters). If they do not, you may need to adjust the doublet score threshold, or change the pre-processing parameters to better resolve the cell states present in your data.
To install with PyPI:
pip install scrubletTo install from source:
git clone https://github.com/AllonKleinLab/scrublet.git
cd scrublet
pip install -r requirements.txt
pip install --upgrade .Previous versions can be found here.