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UNCURL_release

UNCURL is an unified framework for unsupervised / semi-supervised learning using Single Cell RNA-Seq data. This is the Matlab release (for the Python release visit https://github.com/yjzhang/uncurl_python. The python repository is the more current repository with ability to scale to really large datasets.). More details about UNCURL can be found in the cited paper.

Note: This repository is no longer maintained. The Python repository (https://github.com/yjzhang/uncurl_python) is much more current and the recommended repository as of 03/07/2018.

Contributors:

Sumit Mukherjee (PhD Candidate, UW Electrical Engineering),
Yue Zhang (PhD student, UW Computer Science & Engineering),
Sreeram Kannan (Assistant Professor, UW Electrical Engineering),
Georg Seelig (Associate Professor, UW Electrical Engineering / UW Computer Science & Engineering)

Functionalities of UNCURL:

  1. Clustering
  2. Transcriptomic state estimation from highly sampled single cell sequenced data
  3. Dimensionality reduction
  4. Lineage estimation
  5. Semi-supervision

References:

Please cite the following paper in case you use UNCURL for your work:
S. Mukherjee, Y. Zhang, S. Kannan, G. Seelig, 'Prior knowledge and sampling model informed learning with single cell RNA-Seq data' (http://biorxiv.org/content/early/2017/05/31/142398).

Dependencies:

To the best of our knowledge, UNCURL only needs a working version of MATLAB with the optimization toolbox installed. If you discover any other dependencies, please report them to mukhes3@uw.edu or yjzhang@cs.washington.edu.

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Quick tutorial:

Installation

Please run the file 'Install_uncurl.m', before you start using the functions. This file will add the UNCURL folder and it's sub-folders to your MATLAB search path. This step is required for UNCURL to function properly. A simple way to do this is to type the following into your MATLAB workspace (after you are in UNCURL's folder):

Install_uncurl

Getting high variance genes

It has been our experience that removing house keeping genes from the dataset helps improve the performance of UNCURL for various tasks. Thus we recommend the user to do so (unless using an already reduced gene list) using the function GetHighVarGenes(). The function divides the average expression range of the genes into several logarithmic bins (the number is specified by the user) and then keeps the top x percent (also specified by the user) high variance genes from each bin. The binning is done to ensure that only the high mean genes don't get selected. The function is specified as follows:

Inputs:
X - Dataset of dimension 'genes X cells'
NoBins - No. of bins
frac - fraction of high variance genes to keep for each bin

Outputs:
Dat_new - New data matrix with only high variance genes

Syntax:

Dat_new = GetHighVarGenes(Dat,NoBins,x);

Semi-supervision

UNCURL has a framework (qualNorm) which converts cell type specific qualitative information into good initialization points for the various downstream algorithms. The qualitative information is expected to be binarized. For bulk datasets, this can be done by performing one-vs-all differential expression analaysis on the different cell types (followed by thresholding). This step is left to the user. The function is specified as follows:

Inputs:
X - Dataset of dimension 'genes X cells'
B - Binarized qualitative information matrix (of dimension 'genes X subset of cell types'). If there are genes for which qualitative information is not known, set the entire row to -1. Otherwise all entries should be 1 or 0.
k - No. of cell types expected in the dataset

Outputs:
M - Matrix of quantiative means of dimension 'genes X cell types'

Synthax:

M = QualNorm(X, B, k)

Note: The user does not need to provide information about all cell types. Once the means for the known cell types are estimated, our algorithm approximates the location of the missing cell types using the Poisson Kmeans++ algorithm.

State estimation

UNCURL estimates the approximate transcriptomic state from the observed single cell sequenced data, using an algorithm called 'Sampled Matrix Factorization'. The state estimation is specified as follows:

Inputs:
X - Dataset of dimension 'genes X cells'
k - No. of cell types expected in the dataset
Dist - Sampling distribution of the dataset. The current options are 'Poiss' (Poisson),'NB' (Negative Binomial) and 'ZIP' (Zero Inflated Poisson) but 'ZIP' is still in beta mode. The default is 'NB'.
M0 - Initial guess for means (dimension 'genes X cell types'), if available. Enter [] if you don't know any.
Reps - No. of unsupervised repeats to perform. The parameters with the lowest log-likelihood value are chosen. In case of semi-supervision this can be set to 0.
eps (optional) - function tolerance . default : 1e-4.
IterMax (optional) - Maximum iterations. default : 10.

Outputs:
M - Estimated 'archetypal' transcriptomic states (dimension is 'genes X cell types')
W - The convex mixting values for each cell (dimension is 'cell types X cells')
CostPerIter - Cost function value after each iteration (1 X no. of iterations)

Syntax:

[M,W,CostPerIter] = StateEstimation(X,k,Dist,M0,Reps,eps,IterMax);

Note: In case qualitative information is available, the output of the qualNorm function can be used as the M0 here.

Note: If you are using the means estimated during the state estimation step as your initial guess for clustering, we strongly recommend doing the following before providing the means:

[M0] = KmeansPP(M0,X,k);

This step will simply replace the estimated means with the closest points in the dataset. We have seen that this significantly improves the clustering accuracy. If you want to run the clustering quickly, you can simply do it without providing any initial means. In such a case, UNCURL will use Kmeans++ to general starting points for the clustering algorithm.

Dimensionality reduction

Suppose you now have the M and the W matrices from the state estimation step. You can convert the data to l dimensions using UNCURL. The function to do so is specified as follows:

Inputs:
M - Estimated 'archetypal' transcriptomic states (dimension is 'genes X cell types')
W - The convex mixting values for each cell (dimension is 'cell types X cells')
l (optional) - Desired no. of dimensions. Default: 2.

Outputs:
X_ld - Reduced dimension matrix of dimensions 'l X cells'

Syntax:

X_ld = PoissRedDim(M,W,l);

Clustering

UNCURL also employs several distribution based clustering algorithms to cluster the data into different cell types. The function for clustering is specified as follows:

Inputs:
X - Dataset of dimension 'genes X cells'
k - No. of cell types expected in the dataset
Dist - Sampling distribution of the dataset. The current options are 'Poiss' (Poisson),'NB' (Negative Binomial with compiled mex file), 'NB_slow' (negative binomial without mex file) and 'ZIP' (Zero Inflated Poisson). The default is 'NB_slow'.
M0 - Initial guess for means (dimension 'genes X cell types'), if available. Enter [] if you don't know any.
Reps - No. of unsupervised repeats to perform. The parameters with the lowest log-likelihood value are chosen. In case of semi-supervision this can be set to 0.
IterMax (optional) - Maximum iterations. default : 5.

Outputs: Pred - Predicted class label for each cell. Dimension '1 X cells'
LogLike - The Log Likelihood matrix. Dimension is 'cell types X cells'

Syntax:

[Pred,LogLike] = UNCURL_Clustering(X, k, Distribution, M0, Reps, IterMax);

Note: The Negative binomial clustering will not work if the data is not in counts format. In such a case, the user should round/convert the data to counts.

Note: In case qualitative information is available, the output of the qualNorm function can be used as the M0 here.

Lineage estimation

Suppose you now have the M and the W matrices from the state estimation step, UNCURL can be used to infer a smooth lineage for your data. The function to do so is specified as follows:

Inputs: M - Estimated 'archetypal' transcriptomic states (dimension is 'genes X cell types')
W - The convex mixting values for each cell (dimension is 'cell types X cells')

Outputs: RedDim - The reduced dimension matrix of dimensions '2 X cells'
LinDat - Smoothed trajectory having dimension '2 X cells'
AdjW - Weighted adjacency matrix of dimension 'cells X cells'
Adj - Unweighted adjacency matrix of dimension 'cells X cells'
EndNodes - EndNodes of the graph

Syntax:

[RedDim, LinDat, AdjW, Adj, EndNodes] = RunUNCURL_lineage(M,W);

Note: This part also generates a plot showing the end nodes on a scatter plot. This lets the user choose which is the starting node.

Pseudotime calculation and smoothing genes

Suppose you now have the weighted adjacency matrix and the starting node (from the lineage estimation step), you can use this to calculate the pseudotime of each cell and estimate a smoothed (and normalized) value for each gene's expression. The function to do so is specified as follows:

Inputs:
AdjW - Weighted adjacency matrix of dimension 'cells X cells'
StartIn - Starting cell
X - Dataset of dimension 'genes X cells'

Outputs: Pseudotime - Pseudotime values for all cells
FitMat - Smoothed & scaled values for all genes
ScMat - Scaled values for all genes (not smoothed)

Syntax:

[Pseudotime, FitMat, ScMat] = SmoothDataUsingLineage(AdjW, StartIn, X);

Note: The data matrix provided here can either be the observed data matrix or the estimated data matrix (= M X W). Our observation has been that using the estimated data matrix leads to smoother gene expression values even before fitting the genes. However, we leave it up to the user to decide what they want to do here.

Plotting

UNCURL presently has two plotting functions, namely 'ScatterPlotGivenLabels()' and 'PlotGraphGivenAdj()'. Their syntaxes are explained below:

Suppose you have a 2-dimensional representation (X_ld), cell type labels (Lab, this can be any numerical labels between 1 and k) and k (no. of cell types). You can plot the data on reduced dimensions with each label getting a separate color using the function 'ScatterPlotGivenLabels()' as follows:

ScatterPlotGivenLabels(X_ld,Lab,k);

Note:- Matlab has an in-built function 'gplotmatrix', which is more dynamic and should be used unless your version does not have that function (https://www.mathworks.com/help/stats/gplotmatrix.html).

Smoothed 2-dimensional representation (LinDat), cell type labels (Lab, this can be any numerical labels between 1 and k) and an Adjacency matrix (Adj). In case the true labels are not known, you can enter any type of label here. You can plot a graph with nodes colored by the labels as follows:

PlotGraphGivenAdj(Adj,LinDat,Lab)

Datasets

UNCURL comes with a few toy datasets, namely :

Zeisel_data - Contains a sub-sampled version of the dataset from Zeisel et. al. with the differentially expressed genes (it also contains bulk data for the cell types) Islam_data - Contains the dataset from Islam et. al. (with bulk data from the cell types) Synthetic_Linear - Contains synthetic data from a linear trajectory (with true M, w matrices and cell type labels) Synthetic_Branches - Contains synthetic data from a branched trajectory (with true M, w matrices and branch labels)

Note:- These datasets have been pre-processed to include only the differentially expressed genes (using bulk data for differential gene expression analysis).

Frequently Asked Questions

FAQs for UNCURL can be found here: https://sites.google.com/uw.edu/uncurl-release/faq .

Reporting issues

Please report issues to either Sumit Mukherjee (mukhes3@uw.edu) or Yue Zhang (yjzhang@cs.washington.edu).

Python version:

UNCURL also has a Python version (a lot more current with ability to scale up to very large datasets) which can be found here: https://github.com/yjzhang/uncurl_python