scLearn – learning for single-cell assignment

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets.

Researchers at Tongji University have developed scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. The researchers evaluated scLearn on a comprehensive set of publicly available benchmark datasets. They demonstrate that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.

The scLearn workflow

scLearn comprises three steps: data preprocessing, model learning, and cell assignment. (i) In the first step, the main processes comprise routine normalization, cell quality control, rare cell type filtering, and feature selection; nGene, number of genes; nUMI, number of unique molecular identifiers; P-mitGene, percentage of mitochondrial genes; and G, cell group. (ii) In the second step, for single-label single-cell assignment, discriminative component analysis (DCA) is applied to learn the transformation matrix. For multilabel single-cell assignment, MDDM (multilabel dimension reduction via dependence maximization) is applied to learn the transformation matrix. Then, with the learned transformation matrix, the transformed reference cell samples are obtained for the following assignment. The thresholds for labeling a cell as unassigned for each cell type are also automatically learned. LTM, learned transformation matrix, which can be calculated as the optimal transformation matrix for single-label single-cell assignment or by Eq. 6 for multilabel single-cell assignment, respectively (see Materials and Methods); TRCM, transformed reference cell matrix, which can be calculated using Eq. 1 (see Materials and Methods). (iii) In the third step, the transformed query cell samples are obtained on the basis of LTM with an available optional cell quality control procedure. The transformed query samples are compared against the TRCM to derive the measurement fulfilling the cell-type assignment with the rejection task. TQCM, transformed query cell matrix, which can be calculated using Eq. 2 (see Materials and Methods).

Availability – scLearn is developed as an R package available at https://github.com/bm2-lab/scLearn