README.md

sceb: 'single-cell empirical Bayes'

• Empirical Bayes estimators for single-cell RNA-Seq analysis, accompanying the paper "Determining sequencing depth in a single-cell RNA-seq experiment". Previous biorxiv version: "One read per cell per gene is optimal for single-cell RNA-Seq"
• Installation: pip install sceb
• See ./examples/example_pbmc_4k.ipynb for an example for estimating the Pearson correlation.

./experiments:

The code that reproduces all figures in the paper.

• Fig. 1b-c, Fig. 2b top, Supp. Figs. 1-3: figure_gamma_schema.ipynb

• Fig. 2a: The simulation is done using compute_figure_tradeoff_curve.ipynb and the figures are generated using figure_tradeoff_curve.ipynb

• Supp. Fig. 4: The simulation is done using tradeoff_simu.py called by call_tradeoff_simu.sh. The figures are generated using figure_tradeoff_simu.ipynb

• Fig. 2b: Supp. Figs. 5-6: The simulations are done using tradeoff_posthoc_guide_pbmc.py (Supp. Fig. 5) and tradeoff_posthoc_guide_brain.py (Supp. Fig. 6). The figures are generated using figure_tradeoff_posthoc.ipynb

• Fig. 3a top, Supp. Fig. 7: figure_subsample_specturm.ipynb

• Fig. 3a middle, Supp. Figs. 8-9: figure_consistency.ipynb

• Fig. 3a bottom, Supp. Fig. 10: figure_dist_reconstruction.ipynb

• Fig. 3b: figure_feature_selection.ipynb

• Fig. 4a: figure_gene_module.ipynb

• Fig. 4b-c: The network data is generated using figure_gene_network.ipynb and analyzed using Gephi (an external software). The examples are generated using figure_network_example.ipynb

• Fig. 5a-b, Supp. Fig. 13, 14: Comparison between Dropseq data and smFISH data (Figs. 5a-b, Supp. Fig. 14) was done in figure_Dropseq_smFISH.ipynb. Comparison between CEL-seq data and smFISH data (Supp. Fig. 13) was done in figure_CELseq_smfish.ipynb.

• Supp. Figs. 15-17: figure_ercc.ipynb

• Supp. Fig. 18: clean_PBMC_Ding_Levin_2019.ipynb for curating the data from "Ding et al. 2019" and figure_sensitivity_analysis.ipynb for the analysis

./figures:

The figures that appeared in the paper as well as the simulated data to generate them.

DATASETS

The datasets are publicly available and their local path are specified inside ./sceb/data_loader. See ./examples/PC_estimation_pbmc_4k.ipynb for an example of defining a data loader function.

10x datasets

The datasets that we use are from 10x genomics v2 chemistry "Zheng et al. 2017". pbmc_4k, pbmc_8k contain peripheral blood mononuclear cells (PBMCs) from a healthy donor (the same donor). brain_1k, brain_2k, brain_9k, brain_1.3m contain cells from a combined cortex, hippocampus and sub ventricular zone of an E18 mouse. The pair of 293T_1k/3T3_1k contain 1:1 mixture of fresh frozen human (HEK293T) and mouse (NIH3T3) cells. So are the pair 293T_6k/3T3_6k, and the pair 293T_12k/3T3_12k. The data were downloaded from the 10x website and the links are provided as beblow. We used the filtered gene/cell matrix Gene / cell matrix (filtered) from the links.

• pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k
• pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k
• brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900
• brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000
• brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k
• brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons
• 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k
• 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k
• 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k

Other datasets

• Dropseq data and the corresponding smFISH data: from "Wang et al. 2018"
• CEL-seq data and the corresonding smFISH data: the CEL-seq data can be found from "Grün et al. 2014". The smFISH can be found by contacting the author of the paper (e.g., Dr. Grün).
• The three ERCC datasets (Zheng, Klein, Svensson): from "Wang et al. 2018"
• The Klein dataset with the pure RNA controls: from "Svensson et al. 2017"
• The data for sensitivity analysis: from "Ding et al. 2019"

./test: unit tests