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Exploring single-cell data with deep multitasking neural networks


It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE’s various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.

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Fig. 1: The pipeline for analyzing single-cell data in large cohorts with SAUCIE.
Fig. 2: Regularizations and architecture choices in SAUCIE.
Fig. 3: A comparison of the different analysis tasks performed by SAUCIE against other methods.
Fig. 4: Demonstration of SAUCIE’s batch correction abilities.
Fig. 5: SAUCIE produces patient manifolds from single-cell cluster signatures.
Fig. 6: SAUCIE identifies and characterizes cellular clusters, whose proportions can be used to compare patients.

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Data availability

Data for the dengue dataset is available at Cytobank, with accession number 82023.

Code availability

SAUCIE is written in Python using the TensorFlow library for deep learning. The source code is available at


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This research was supported in part by: the Indo-U.S. Vaccine Action Program, the National Institute of Allergy and Infectious Diseases of the NIH (Award no. AI089992 to R.R.M.); IVADO (L’institut de valorisation des données to G.W.) and the Chan–Zuckerberg Initiative (grant no. 182702 to S.K.).

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Authors and Affiliations



M.A., S.K., G.W. and D.v.D. envisioned the project. M.A., K.S. and D.v.D., implemented the model and performed the analyses. M.A., S.K., G.W. and D.v.D. wrote the paper. K.S., W.S.C., H.M., A.C. and K.R.M. provided assistance in writing and analysis. Y.Z., X.W., M.V., A.D., V.R., P.K. and R.M. were responsible for data acquisition and processing.

Corresponding author

Correspondence to Smita Krishnaswamy.

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Competing interests

The authors declare no competing interests.

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Peer review information Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Integrated supplementary information

Supplementary Figure 1 Comparison of runtimes on an increasing number of points.

The number of points is represented on the horizontal axis and the time in seconds the method took to complete is on the vertical axis. If a method ran out of resources and could not complete a run for a certain number of points, that is demarcated with an ‘x’ and no further time points were attempted for that method.

Supplementary Figure 2 A comparison of the SAUCIE clustering to other clustering methods on artificial and real data.

Rows show the different datasets. Along with the first artificial dataset, there are two CyTOF datasets and three scRNA-seq datasets, with samples of size 2000, 27499, 50000, 3005, 2730, and 2000 for the GMM, Shekhar et al, Chevrier et al, Ziesel et al, Paul et al, and Setty et al datasets, respectively. Columns show the different clustering methods. From left to right: True “ground truth” labels, SAUCIE, kmeans, Phenograph, scVI. In (b) and (c), we add the scores for the modularity and silhouette heuristics from Supplementary Table 1, respectively.

Supplementary Figure 3 A comparison of batch correction with SAUCIE to other methods.

Comparisons include an artificial dataset, two technical replicates from the dengue CyTOF data, non-technical replicates on scRNA-seq batches from mouse cortex, and then public data from Chevrier et al, Azizi et al, and Setty et al with samples of size 2000, 41721, 8530, 9998, 4376, 2000, and 24741, respectively. Rows show the different datasets. Columns show the different batch correction methods. From left to right: The original data prior to batch correction, SAUCIE, mutual nearest neighbors (MNN), canonical correlation analysis (CCA). In (b) and (c), we add graphs of the mixing score and shape preserving score results from Supplementary Table 2 for quantitative evaluation, respectively.

Supplementary Figure 4 A comparison of the SAUCIE visualization to other methods on a number of artificial and real datasets.

The columns show the different methods. From left to right: SAUCIE, PCA, Monocle2, Diffusion Maps, UMAP, tSNE, PHATE. The rows show the different datasets. From top to bottom: Artificially generated trees with varying amounts of noise, random tree generated with diffusion limited aggregation (DLA), intersecting half circles, Gaussian mixture model, scRNA-seq hematopoiesis from Paul et al, CyTOF T cell development from Setty et al, CyTOF ipsc from Zunder at al, scRNA-seq retinal bipolar cells from Shekhar et al, scRNA-seq mouse cortex from Zeisel et al with samples of size 1440, 1440, 1440, 2000, 1500, 2000, 55000, 2730, 250170, 220450, 27499, and 3005, respectively. In (b), we add a graph of the precision-recall metric results from Supplementary Table 3 for quantitative evaluation.

Supplementary Figure 5 A comparison of imputation methods including SAUCIE.

Several gene-gene associations are shown from the 10x mouse cortex dataset subset (4142 cells). From left to right: The original (sparse) data, data after imputation with SAUCIE, MAGIC, scImpute, and nearest neighbor completion.

Supplementary Figure 6 A comparison of imputation with SAUCIE to other methods on the simulated dropout experiment.

Increasing amounts of dropout are along the horizontal axis from left to right, and the accuracy of each method as measured by R2 is along the vertical axis. The time each method took to complete is in the legend in seconds.

Supplementary Figure 7 SAUCIE batch correction removes technical artifacts from CyTOF instrument.

Four select marker abundances with samples grouped by day they were run on the cytometry instrument, with each day having fourteen distinct samples in the group. For each marker, the fourteen samples before batch correction are shown to the left of the same fourteen samples after batch correction.

Supplementary Figure 8 SAUCIE batch correction preserves relative values in samples from dengue data.

Histograms of marker expression (top: IL-6, bottom: CD86) of samples run together on the cytometry instrument on day two, separated by sample. The twelve samples were of size 50988, 41212, 29337, 177804, 177492, 154054, 95476, 82782, 82194, 182486, 137240, and 113506, respectively. The values for each sample and marker are shown before SAUCIE batch correction (left) and after SAUCIE batch correction (right). The box plots depict the minimum and maximum (whiskers), median (center line), 25th and 75th percentiles (box limits) of marker values for all cells within the specified sample.

Supplementary Figure 9 Controlling the granularity of clustering with SAUCIE hyperparameters.

The granularity of the clustering, as measured by the total number of clusters found. Each line represents a fixed value of λd as λc increases from left to right.

Supplementary Figure 10 An illustration of the metaclustering process on the dengue dataset.

Metaclustering results from N=10 samples from the dengue dataset. Top left: cluster centroids embedded by tSNE and colored by metacluster, sized according to the number of cells in each cluster. Top right: cluster centroids colored by sample, also sized according to the number of cells in each cluster. Bottom left: a cell-level heatmap of expression grouped by metacluster. Bottom right: the composition of each metacluster by sample.

Supplementary Figure 11 An illustration of the SAUCIE pipeline on the dengue dataset.

Left: cell-level heatmap of expression grouped by cluster. Top right: cluster centroids embedded by tSNE, sized according to the number of cells in each cluster. Bottom right: the composition of each cluster by sample.

Supplementary information

Supplementary Information

Supplmentary Figs. 1–11.

Reporting Summary

Supplementary Tables

SAUCIE Tables 1–3

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Amodio, M., van Dijk, D., Srinivasan, K. et al. Exploring single-cell data with deep multitasking neural networks. Nat Methods 16, 1139–1145 (2019).

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