Solo

Solo [1] (Python class SOLO) posits a flexible generative model of scRNA-seq count data that can subsequently be used for many common downstream tasks.

The advantages of Solo are:

• Can perform doublet detection on pre-trained SCVI models

• Scalable to very large datasets (>1 million cells).

The limitations of Solo include:

• For an analysis seeking to only do doublet detection, Solo will be slower than other methods.

Overview

Solo starts with a trained SCVI instance. First Solo, simulates doublets using the original data and second Solo trains a classifer on the model latent space.

Doublet simulation

A simulated doublet \(d_n\) is generated via the following process:

\begin{align} d_n = x_{1} + x_{2}, \end{align}

where \(x_{1}\) and \(x_{2}\) are drawn i.i.d from the empirical data distribution \(p_{\textrm{data}}(x)\) over single-cell transcriptomes (count data).

The number of doublets to generate is controlled by the doublet_ratio parameter of from_scvi_model().

Classifier training

After doublet simulation, the doublets are encoded through the scVI encoder, which outputs latent representations \(z'_{1:D}\) if there are \(D\) doublets.

These vectors are assigned a label of 1, while the latent representations of the original data \(z_{1:N}\) are assigned a label of 0. A simple multilayer perceptron classifier (scvi.module.Classifier) is trained and the doublet score for each originally observed cell is the doublet probability according to this classifier.

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[1]

Nicholas J. Bernstein, , Nicole L. Fong, Irene Lam, Margaret A. Roy, David G. Hendrickson, and David R. Kelley (2020), Solo: doublet identification in single-cell RNA-Seq via semi-supervised deep learning, Cell Systems.