Softwares:

1. Spanning-tree Progression Analysis of Density-normalized Events (SPADE)
2. Sample Progression Discovery (SPD)
3. Spectral Analysis for Class Discovery and Classification (SPACC)
4. Fast Calculation of Pairwise Mutual Information Based on Kernel Estimation
5. TreeVis: A MATLAB-based Tool for Tree Visualization
　

1. Spanning-tree Progression Analysis of Density-normalized Events (SPADE)

Flow cytometry captures the heterogeneity of biological systems by providing multiparametric measurements of individual cells. Traditional analysis of flow cytometry datasets is often a subjective process that requires prior knowledge of the biological system. We present a novel analytical approach, Spanning-tree Progression Analysis of Density-normalized Events (SPADE), that uncovers an underlying cellular hierarchy from flow cytometry data, without requiring prior knowledge.   

The software can be downloaded here

2. Sample Progression Discovery (SPD)

In contrast to the majority of existing methods which focus on identifying differences between sample groups (i.e. normal vs. cancer, treated vs. control), SPD aims to identify an underlying progression among individual samples, both within and across sample groups. We view SPD as a hypothesis generation tool, when applied to datasets where the progression is unclear. For example, when applied to a microarray dataset of cancer samples, SPD would assume that the cancer samples collected from individual patients represent different stages during an intrinsic progression underlying cancer development. The inferred relationship among the samples may therefore indicate a pathway or hierarchy of cancer progression, which serves as a hypothesis to be tested.   

The software can be downloaded here

3. Spectral Analysis for Class Discovery and Classification (SPACC)

Classification methods are mainly divided into two categories. Unsupervised methods do not use class labels. They have the ability to discover new classes, but lack ways to identify new classes systematically. In supervised methods, the class label information plays such an important role that supervised methods do not have the ability to discover new classes. I developed a novel classification method, SPACC, where the key idea is to make the class label information play a less important role. SPACC is the first that performs class discovery and classification simultaneously.  

The software can be downloaded here

4. Fast Calculation of Pairwise Mutual Information Based on Kernel Estimation

We present a new software implementation for more efficiently computing the mutual information for all pairs of genes from gene expression microarrays. Computation of the mutual information is a necessary first step in various information theoretic approaches for reconstructing gene regulatory networks from microarray data. When the mutual information is estimated by kernel methods, computing the pairwise mutual information is quite time-consuming. Our implementation significantly reduces the computation time. For an example data set of 9563 genes and 336 samples, the current available software for ARACNE requires 142 hours to compute the mutual information between all possible pairs of genes, whereas our implementation requires 1.6 hours.

The Matlab code is available at here

5. TreeVis: A MATLAB-based Tool for Tree Visualization

Many analyses of biological data produce results in the form of tree structures. Generating easily interpretable layouts to visualize these tree structures is a non-trivial task. We present a new visualization algorithm, TreeVis, to generate clear two-dimensional layouts of complex tree structures.

The Matlab code is available at here