R Codebase for BISCUIT: Infinite Mixture Model to cluster and impute single cells.
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Readme.md

Readme

This repository contains source code for the paper: Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data by Sandhya Prabhakaran*, Elham Azizi*, Ambrose J Carr, and Dana Pe’er in ICML 2016

Software provided in R.

Dependencies

  1. R - install R from https://cran.r-project.org/. A version higher than 2.12.0 is recommended. sudo yum install R
  2. Once installed, open a terminal and at the command prompt, type R.
  3. At the R prompt: Install the following R packages by issuing command:

install.packages(c("MCMCpack","mvtnorm","ellipse","coda","Matrix","Rtsne","gtools","foreach","doParallel","doSNOW","snow","lattice","MASS","bayesm","robustbase","chron","mnormt","schoolmath","devtools","RColorBrewer"))

Note: Until here, this is a one-time activity.

How to run code

  1. Clone/Download code repository.
  2. At the R prompt: issue command

rm(list=ls()); graphics.off()

  1. Issue setwd() to point to the path where the code repository resides. eg. if the code is downloaded at “/User/Downloads/BISCUIT/", then type

working_path <- "/Users/Downloads/BISCUIT/"; setwd(working_path);

  1. In start_file.R:

    a. input_file_name: is the name of your input data available as counts. File must be of the form cells x genes. You can also download data used in the ICML paper from here. (Data source: http://linnarssonlab.org/cortex/).

    input_file_name <- ‘expression_mRNA_17-Aug-2014.txt’

    b. input_data_tab_delimited: Set to TRUE if the input data is tab-delimited

    c. is_format_genes_cells: Set to TRUE if the input data has rows as genes and columns as cells.

    d. choose_cells: choose the number of cells or comment out to use all the cells in the input dataset.

    e. choose_genes: choose the number of genes or comment out to use all the genes in the input dataset. This will select genes based on the ordered Fiedler vector. For eg., choose_genes <- 20, for the top 20 genes ordered by the magnitude of the Fiedler vector.

    f. gene_batch: set it such that 20 <= gene_batch <= 150. (This will create gene batches across which the Infinite Mixture model will run in parallel)

    g. num_iter: Maximum number of MCMC iterations for convergence. Set this such that 15 <= num_iter <= ~50.

    h. num_cores: Set this to a value lesser than the total number of cores in your device. For eg, in R, type detectCores(). If this returns a value greater than 1 then set num_cores <- detectCores() - 1, else set num_cores <- 1.

    i. z_true_labels_avl: set to TRUE if true labels of cells are available, else set to FALSE.

    j. num_cells_batch: required to split the data in feasible sets for parallel processing the confusion matrix. Set this to 1000 if input number of cells is in the 1000s, else set it to 100.

    k. alpha: DPMM dispersion parameter. A higher value spins more clusters whereas a lower value spins lesser clusters. For the Zeisel et al. data, alpha = 0.005 or less.

    l. output_folder_rename: give a prefix to rename your existing /output/ folder, if any.

  2. At the R prompt: Issue command

source("start_file.R")

Output


  1. Output folder gets created which has the run log.txt and log_CM.txt files. For resolving any discrepancies while parallel processing of your data, check debug.txt and debug_CM.txt.
  2. output/plots/ folder gives the various plots
  3. In the R terminal, the latent variables of interest can be obtained by issuing:
  • z_inferred_final ## (class assignment of the cells)
  • alpha_inferred_final ## (inferred cell-specific alphas)
  • beta_inferred_final ## (inferred cell-specific betas)
  • mu_final ## (inferred mus. dim(mu_final) <- numgenes x K)
  • Sigma_final ## (inferred Sigmas)
  • total_clusters ## (total inferred clusters)
  1. The following are also saved as .txt files for further analysis
  • ~/output/plots/extras/pre_imputed_tSNE_coord.txt (tSNE coordinates of data pre-imputation)
  • ~/output/plots/extras/post_imputed_tSNE_coord.txt (tSNE coordinates of data post-imputation)
  • ~/output/plots/Inferred_Sigmas/Sigma_final_k.txt (Inferred Sigma matrices per cluster k)
  • ~/output/plots/Inferred_means/mu_final (Inferred mean matrix; rows are numgenes, columns are number of total_clusters)
  • ~/output/plots/Inferred_Sigmas/Genes_selected.csv (Genes selected based on the global co-expression/gene disparity/Fiedler vector)
  • ~/output/plots/Inferred_means/Genes_selected.csv (Genes selected based on the global co-expression/gene disparity/Fiedler vector)
  • ~/output/plots/Inferred_alphas_betas/Final_alphas.csv (Inferred alpha values)
  • ~/output/plots/Inferred_alphas_betas/Final_betas.csv (Inferred beta values)
  • ~/output/plots/extras/Imputed_Y_countspace.txt (Imputed data in count space)
  • ~/output/plots/extras/Imputed_Y_logspace.txt (Imputed data in log space)
  • ~/output/plots/extras/Input_parms_used.txt
  • ~/output/plots/extras/Output_values.txt (total number of clusters, cluster proportions)
  • ~/output/plots/extras/cluster_posterior_probabilities.csv (posterior probabilities of every cell to every inferred cluster)

Opening an issue/contacting the developers

  1. Under the repository, click Issues.
  2. Click New issue.
  3. Type a title and description for your issue, new feature you wish to see added etc.
  4. When you are finished, click Submit new issue.