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Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis

A preprint version of the article is available at bioRxiv.


Clustering and cell type classification are important steps in single-cell RNA-seq (scRNA-seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell type classification methods that utilize external well-annotated source data start to gain popularity over unsupervised clustering algorithms; however, the performance of existing supervised methods is highly dependent on source data quality and they often have limited accuracy to classify cell types that are missing in the source data. We developed ItClust to overcome these limitations, a transfer learning algorithm that borrows ideas from supervised cell type classification algorithms, but also leverages information in target data to ensure sensitivity in classifying cells that are only present in the target data. Through extensive evaluations using data from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust considerably improves clustering and cell type classification accuracy over popular unsupervised clustering and supervised cell type classification algorithms.

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Fig. 1: Overview of the ItClust framework.
Fig. 2: Comparison of ItClust with unsupervised methods on human pancreatic islet datasets.
Fig. 3: Comparisons of ItClust with semi-supervised and supervised methods on human pancreatic islet datasets when source and target data are from the same species.
Fig. 4: Comparisons of ItClust with semi-supervised and supervised methods on mouse and human kidney datasets when source and target data are from different species.
Fig. 5: Comparisons of ItClust with semi-supervised and supervised methods on human pancreatic islet datasets to evaluate the impact of missing cell types in source data.
Fig. 6: Confidence scores in ItClust.

Data availability

We analysed multiple scRNA-seq datasets. Publicly available data were acquired from the access numbers provided by the original publications: Baron et al.19 (GSE84133), Xin et al.26 (GSE81608), Grün et al.17 (GSE81076), Muraro et al.18 (GSE85241), Lawlor et al.15 (GSE86469), Segerstolpe et al.16 (E-MTAB-5061), Park et al.24 (GSE107585), Peng et al.27 (GSE118480), Paul et al.33 (GSE727857) and Tusi et al.34 (GSE89754). Details of the datasets analysed in this paper were described in Supplementary Table 1.

Code availability

An open-source implementation of the ItClust algorithm can be downloaded from,


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This work was supported by the following grants: NIH R01GM108600, R01GM125301, R01HL113147, R01HL150359, R01EY030192 and R01EY031209 (to M.L.), and R01DK076077 (to. K.S.).

Author information

Authors and Affiliations



This study was conceived of and led by M.L.. J.H., X.L., G.H. and M.L. designed the model and algorithm. J.H. implemented the ItClust software and led the data analysis with input from M.L., X.L., G.H., Y.L. and K.S. J.H. and M.L. wrote the paper with feedback from X.L., G.H., Y.L. and K.S.

Corresponding author

Correspondence to Mingyao Li.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 scVI’s latent space of Lawlor et al. data.

UMAP plot of scVI’s latent space when Baron human data were used as source data and Lawlor et al. data were used as target data. The plot indicates that scVI failed to remove batch effect between the source and target data, which led to low cell type annotation accuracy in the target data.

Extended Data Fig. 2 Dot plots for human kidney data.

Dot plots of known marker genes used for cell type identification for the human kidney data (data generated ourselves together with data from Young et al.). The marker genes used to label the cell types are: SLC13A3 and SLC34A1 for PT (Proximal Tubule); CLDN16 and SLC12A for Loop of Henle; PTPRB and KDR for Endo_AVR_1 (Endothelial Ascending Vasa Recta); PTPRB and SLC14A1 for Endo_AVR_2; PTPRB, KDR, and SLC14A1 for Endo_DVR (Endothelial Descending Vasa Recta); KCNJ1 and SLC8A1 for Distal Tubules; SLC4A1 and CLCNKB for CD_IC_A; SLC26A4 and CLCNKB for CD_IC_B; GZMA and GZMB for NK_cells; CD3D, CD3E, and CD3G for T_cells; CD14, S100A8, and S100A9 for Macrophage_1; CD14 and FCER1A for Macrophage_2; CD79A and CD79B for B_cells.

Extended Data Fig. 3 Computing cost of ItClust.

Memory usage and CPU time for the kidney data analysis.

Extended Data Fig. 4 Analyzing Tusi et al. data using ItClust.

UMAP (a) and Sankey (b) plots of Tusi et al. data based on ItClust embedding and predicted cell types.

Extended Data Fig. 5 Classification accuracies for combined source data and read depth down sampling experiments.

(a) The classification accuracies of ItClust, Seurat 3.0, Moana, scmap, and scVI for the Segerstolpe human pancreatic islet data, using different source datasets as input. Source data 1 is the reduced Baron human pancreatic islet data as in Fig. 5(b) and source data 2 is the Xin human pancreatic islet data, which only include alpha, beta, gamma, and delta cells. (b) The classification accuracies of ItClust before and after fine-tuning, Seurat 3.0, Moana, scmap, and scVI for the macaque retina data across different down-sampling efficiencies. Cells from macaques 1, 2, and 3 were used as the source data, and cells from macaque 4 were used as the target data.

Extended Data Fig. 6 Sankey plots for Segerstolpe et al. data analysis.

The Sankey plots of ItClust, Seurat 3.0, Moana, scmap, and scVI cell type classification results for the Segerstolpe et al. dataset using the combined source data.

Supplementary information

Supplementary Information

Supplementary Tables 1–5 and Notes 1–4.

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Hu, J., Li, X., Hu, G. et al. Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. Nat Mach Intell 2, 607–618 (2020).

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