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HarmonizationSCANVI

  • Reproducing results in the "Harmonization and Annotation of Single-cell Transcriptomics data with Deep Generative Models" paper
  • Demonstration of how to use scVI and scANVI for the harmonization and annotation problem

Contact

chenlingantelope [at] berkeley [dot] edu

Datasets

Analysis Associated Script Datasets Technology Number of Cells Ref.
Figure 2: Benchmark PBMC8KCITE.py PBMC-8K; PBMC-CITE 10x 8,381; 7,667 10x DatasetsStoeckius, Marlon, et al. 2017
Supplementary Figure 2: UMAP Visualization PBMC8KCITE.py PBMC-8K; PBMC-CITE 10x 8,381; 7,667 10x Datasets; Stoeckius, Marlon, et al. 2017
Figure 2: Benchmark MarrowTM.py Tech1.pretty.ipynb MarrowTM-10x; MarrowTM-ss2 10x; SmartSeq2 4,112;5,351 Quake, Stephen R., et al. 2018
Supplementary Figure 1: Robustness Analysis for Hyperparameter Choice Robustness_study.ipynb MarrowTM-10x; MarrowTM-ss2 10x; SmartSeq2 4,112;5,351 Quake, Stephen R., et al. 2018
Supplementary Figure 3: UMAP Visualization MarrowTM.py MarrowTM-10x; MarrowTM-ss2 10x; SmartSeq2 4,112;5,351
Figure 2: Benchmark Pancreas.py Pancreas-InDrop; Pancreas-CEL-Seq2 inDrop; CEL-Seq2 8,569; 2,449 Baron, Maayan, et al. 2016; Muraro, Mauro J., et al. 2016
Supplementary Figure 4: UMAP Visualization Pancreas.py Pancreas-InDrop; Pancreas-CEL-Seq2 inDrop; CEL-Seq2 8,569; 2,449 Baron, Maayan, et al. 2016; Muraro, Mauro J., et al. 2016
Figure 2: Benchmark DentateGyrus.py DentateGyrus-10x; DentateGyrus-C1 10x; Fluidigm C1 5,454; 2,303 Hochgerner, Hannah, et al. 2018
Supplementary Figure 5: UMAP Visualization DentateGyrus.py DentateGyrus-10x; DentateGyrus-C1 10x; Fluidigm C1 5,454; 2,303 Hochgerner, Hannah, et al. 2018
Figure 3: Robustness Analysis by subsampling cells Supplementary Figure 10 NoOverlapSCANVI.py PopRemoveSCANVI.py SCANVI_posterior-NoOverlap.ipynb SCANVI_posterior_poprm.ipynb PBMC-8K; PBMC-CITE 10x 8,381; 7,667 10x Datasets; Stoeckius, Marlon, et al. 2017
Figure 4: Continuous Trajectory Supplementary Supplementary Figure 6: UMAP continuous.ipynb HEMATO-Tusi; HEMATO-Paul inDrop; MARS-seq 4,016 ; 2,730 Tusi, Betsabeh Khoramian, et al. 2018; Paul, Franziska, et al. 2015
Figure 5: External Validation by Experimentally Derived Labels, Supplementary Figure 11 harmonization-CitePure-SCANVI.ipynb PBMC-68K; PBMC-Sorted; PBMC-CITE 10x 68,579; 94,655; 7,667 Zheng, Grace XY, et al. 2017; Stoeckius, Marlon, et al. 2017
Figure 6: Semi-Supervised Annotation of T Cell Subtypes, Supplementary Figure 12 SCANVI-mild-annot-Clustering.ipynb PBMC-Sorted T cell Subtypes 10x 42919 Zheng, Grace XY, et al. 2017; Stoeckius, Marlon, et al. 2017
Hierarchical Semi-Supervised Annotation Hierarchical.ipynb CORTEX 10x 160,796 Zeisel, Amit, et al. "Molecular architecture of the mouse nervous system." bioRxiv (2018): 294918.
Supplementary Figure 7: Scalability Analysis scanorama.ipynb SCANORAMA Mixed 105,476 Hie, Brian L., Bryan Bryson, and Bonnie Berger. "Panoramic stitching of heterogeneous single-cell transcriptomic data." bioRxiv (2018): 371179.
Supplementary Figure 13: Differential Expression DE-final.ipynb PBMC-8K; PBMC-68K 10x 8,381; 68,579 10x Datasets; Zheng, Grace XY, et al. 2017
  • Supplemtary Figure 2,3,4,5,8,9 are generated using scripts in Additional_Scripts/ using output from the analysis python scripts including scanvi_acc.R, KNNcurves.py and BE_curves.py.
  • Boxplots for Figure 3 are generated using poprm_boxplot.R in Additional_Scripts/
  • The Additional_Scripts also contains code for running Seurat directly from commandline runSeurat.R and SeuratPCA.R.
  • All .gmt files in Additional_Scripts/ are gene signatures.

Installation

  • Clone the github repository, install the dependencies and call functions from the modules scVI
  • Install time (< 10 min)

Requirements

  • Pytorch V0.4.1
  • Python 3
  • scikit-learn V0.19.1

Instructions

  • To reproduce results from the paper, look up the relevant datasets, python notebooks (located in notebooks/), or python scripts (located in the root directory).
  • Download the relevant datasets except for the ones already wrapped for the scVI package (PBMC-8K, PBMC-CITE, PBMC-68K, PBMC-Sorted, MarrowTM-10x, MarrowTM-ss2 can be loaded directly with the dataloader functions)
  • Annotation files generated by us when the original study did not provide annotation (cite.seurat.labels) can be found in the scvi-data repository
  • Run the analysis and results should match those of the paper.
  • This repository contains functions written uniquely to produce some of the analysis in this paper. For more up-to-date package refer to main scVI repository