3 research outputs found
Clinically driven semi-supervised class discovery in gene expression data
Abstract
Motivation: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries.
Results: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors.
Availability: MonoClaD (Monotone Class Discovery). See http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/
Supplementary information: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software.html
Contact: [email protected]
Mutual Enrichment in Ranked Lists and the Statistical Assessment of Position Weight Matrix Motifs
Statistics in ranked lists is important in analyzing molecular biology
measurement data, such as ChIP-seq, which yields ranked lists of genomic
sequences. State of the art methods study fixed motifs in ranked lists. More
flexible models such as position weight matrix (PWM) motifs are not addressed
in this context. To assess the enrichment of a PWM motif in a ranked list we
use a PWM induced second ranking on the same set of elements. Possible orders
of one ranked list relative to the other are modeled by permutations. Due to
sample space complexity, it is difficult to characterize tail distributions in
the group of permutations. In this paper we develop tight upper bounds on tail
distributions of the size of the intersection of the top of two uniformly and
independently drawn permutations and demonstrate advantages of this approach
using our software implementation, mmHG-Finder, to study PWMs in several
datasets.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
Clinically driven semi-supervised class discovery in gene expression data
MOTIVATION: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries.
RESULTS: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors