Awesome

SCUBA (MATLAB version)

Note:

This package is no longer being maintained.

Overview

SCUBA stands for "Single-cell Clustering Using Bifurcation Analysis." SCUBA is a novel computational method for extracting lineage relationships from single-cell gene expression data, and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multi-lineage specifications.

Reference: Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L, Yuan GC. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A. 2014 Dec 30;111(52):E5643-50. doi:10.1073/pnas.1408993111

Systems Requirements

SCUBA is independent of operating systems because it is written in Matlab. Basic requirement for running SCUBA includes MATLAB and the Statistics toolbox. The pseudotime estimation step requires two external Matlab packages which are publicly available: drtoolbox (which can be downloaded from http://lvdmaaten.github.io/drtoolbox/), and ksegments (which can be downloaded from http://lear.inrialpes.fr/~verbeek/software.php). Another option is to utilize the R package princurve (which can be downloaded from http://cran.r-project.org/web/packages/princurve/index.html) for instead of ksegments for principal curve analysis. In this case, both R and Matlab are required for running SCUBA.

Usage

Unzip the package. Change the current directory in Matlab to the folder containing the scripts.

The data for SCUBA analysis has to be placed in the folder 'sample_data', in a folder specifying the dataset. The package comes with three datasets and their corresponding folders: 'guo2010', 'deng2014' and 'bendall2014'. Prepare the data in an appropriate format (.txt or .fcs) with a standardized name. See below for detailed description.

Preprocessing

Run one of the three preprocessing scripts:

PCR_preprocess.m — for qPCR data. Data are tab-delimited text format. First row contains the cell ID. Second row contains the cell-stage information. The rest contains the gene expression data matrix. Example: guo2010Data.txt

RNAseq_preprocess.m — for RNAseq data. Data are tab-delimited text format. First row contains the cell ID. Second row contains the cell-stage information. The rest contains the gene expression data matrix. By default, the sequence reads are log2-transformed. Example: deng2014Data.txt

MassCytometry_preprocess.m — for MassCytometry data. Data are in the binary .fcs format for flow cytometry experiments. This preprocessing step contains a pseudotime estimation algorithm. Example: bendall2014Data.fcs. Note that processing this dataset requires a machine with at least 40 Gb of memory.

Each script takes two inputs: 'dataset' and 'pseudotime_mode':

'dataset' is the name of the dataset, e.g. 'guo2010'. The preprocessed data are saved as a mat file in the intermediate directory.

'pseudotime_mode' is set to be 0 (default), if the temporal information is provided (i.e., the second row contains 'Stage'), or 1 if the temporal information needs to be inferred computationally. SCUBA uses the principal curve analysis to infer temporal information, but the results should be used with caution. Since principal curve analysis cannot identify the directionality of time, it may be worthwhile to run SCUBA on both the original and reversed time order.

Run SCUBA

SCUBA is the main function. It has two arguments: 'dataset' and 'cluster_mode'. 'dataset' refers to the name of the dataset, which is also the name of the data folder. 'cluster_mode' refers to the method for clustering. It can have three values. 'original' -- using the Euclidean distance; 'pca' -- convert the data to principal components then apply Euclidean distance; 'pca2' — similar to 'pca' but PCA analysis is based on samples in the final cell-stage (used in our paper).

SCUBA has two main steps. In the first step, we infer the cellular hierarchy, using a binary tree model. For simplicity, we only consider steady-state attractors. In the second step, we quantitatively model the dynamics in the reduced state space along each bifurcation direction, using a potential V(x) to characterize gene expression dynamics associated with each bifurcation event.

Step 1. Inference of cellular hierarchy using dynamic clustering

initial_tree.m — This function provides an initial estimate the cellular hierarchy, using a series of k-means clustering.

refine_tree.m — this function refines the tree structure by maximizing the penalized likelihood function (Equation 1 in the paper).

Step 2. Bifurcation analysis

bifurcation_direction.m — Infer the direction associated with each bifurcation and project data along the bifurcation directions.

bifurcation_analysis.m — Infer the dynamical changes of gene expression patterns along the bifurcation direction by fitting a Fokker-Planck equation.

reductionSimulations.m — Function to predict the effects of perturbing potential regulators in the lineage bias.

For each dataset, the results are deposited in the following three directories:

1. intermediate_files, containing intermediate results from the analysis. Please note the .mat files may contain additional information than the .txt files.

Important: the final tree structure is saved as 'T' in the file 'final_tree.mat'. Cell cluster assignment information is saved as the subfield: 'T.s'.

2. figures, containing jpg figures of the analysis.

3. results, containing the final results of the analysis.

Examples

Example 1: Analysis of qPCR data in Guo et al. "Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.". Dev Cell. 2010 Apr 20;18(4):675-85.

>> PCR_preprocess('guo2010');
>> SCUBA('guo2010')

Example 2: Analysis of RNAseq data in Deng et al. " Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells." Science. 2014 Jan 10;343(6167):193-6.

>> RNAseq_preprocess('deng2014');
>> SCUBA('deng2014')

Example 3: Analysis of Mass Cytometry data in Bendall et al. " Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development." Cell. 2014 Apr 24;157(3):714-25.

>> select_marker_names = {'CD10','CD117','CD179a','CD179b','CD19','CD20',...
    'CD24','CD34','CD38','CD45','CD72','CD79b','HLADR','IgD','IgM-i','IgM-s','Kappa','Lambda'};
>> MassCytometry_preprocess('bendall2014', select_marker_names, 'Rprincurve', 'CD34', 9)
>>  SCUBA('bendall2014')
>> plotGeneProfiles('bendall2014', {'CD19', 'CD20', 'CD34', 'CD10', 'CD38'})

Change log

July 17, 2015. Updated PCR_Preprocess.m and RNAseq_Preprocess.m. In the original version, running pseudotime estimate would cause an error because it assumes the input contains cell_stage information. This error is now fixed.

Updated bifurcation_analysis.m. The potential is calculated only if there are sufficient cells in all relevant clusters.

April 18, 2016. Updated README.md (this file).

Updated the 'EstimatePseudotime.m' to prevent an R runtime error.