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Bayesian mechanistic modelling of scRNAseq data using Stan

Requirements:

Description:

The folder stan contains the following models:

  • tg_zi_single: Telegraph model, with zero inflation, single allele
  • tg_zi_vol_single: Telegraph model, with zero inflation and cell volume, single allele
  • tg_zi_vol_pooled: Telegraph model, with zero inflation and cell volume, two alleles (pooled)

These models can be run using CmdStan.

Input

The input to Stan is provided as a JSON file containing the following fields:

  • int ncells: Number of cells
  • int ngenes: Number of genes
  • int N: Number of observations
  • int cells[N]: Cell corresponding to each observation
  • int genes[N]: Gene corresponding to each observation
  • int counts[N]: Measured mRNA numbers for each observation

The script convert_data.jl can be used to convert a count matrix (stored as a CSV file) into this format, skipping missing values.

Output

The sampler computes the posterior over the following quantities:

Gene-specific:

  • mu: Mean mRNA numbers
  • b: Mean burst size
  • dur: Mean burst duration
  • p0: Amount of zero inflation
  • alpha: Volume dependence (if present)

The following values are computed for convenience:

  • rho: Transcription rate
  • sigma_on: on switching rate
  • sigma_off: on switching rate

Cell-specific:

  • vol: Volume scaling factor (if present)

Note: The outputs also contains the latent variables raw_betas, one per observation. This requires a lot of memory or disk space for large batches. These, and other internal variables, can be removed using the script postprocess_csv.py.

Example usage:

  1. Convert mRNA count matrix to JSON:
julia scripts/convert_data.jl countmat.csv countmat.json

The input must be a CSV file containing mRNA counts. Each row represents a cell, and each column a gene (except for the first column, which is ignored). Missing values are allowed.

  1. Run Stan:
stan/tg_zi_vol_single sample data file=countmat.json output file=raw_samples.csv

See the CmdStan user guide for more information

  1. (Optional) Trim output files:
python scripts/postprocess_csv.py <raw_samples_1.csv >samples_1.csv
...