Data reduction for spectral clustering to analyze high throughput flow cytometry data

Background  

Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.     

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background  

The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets.     

Missing value imputation for microarray gene expression data using histone acetylation information

Background  

It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.     

FLAME,a novel fuzzy clustering method for the analysis of DNA microarray data

Background  

Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process.     

Missing value imputation for epistatic MAPs

Background  

Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.     

Classification and biomarker identification using gene network modules and support vector machines

Background  

Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.     

Integrative missing value estimation for microarray data

Background  

Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples.     

OligoSpawn: a software tool for the design of overgo probes from large unigene datasets

Background  

Expressed sequence tag (EST) datasets represent perhaps the largest collection of genetic information. ESTs can be exploited in a variety of biological experiments and analysis. Here we are interested in the design of overlapping oligonucleotide (overgo) probes from large unigene (EST-contigs) datasets.     

PubMatrix: a tool for multiplex literature mining

Background  

Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence.     

Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data

Background  

Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE) rather than recursive feature elimination (RFE). We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE.     

EDISA: extracting biclusters from multiple time-series of gene expression profiles

Background  

Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets.     

New components of the <Emphasis Type="Italic">Dictyostelium</Emphasis> PKA pathway revealed by Bayesian analysis of expression data

Background  

Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.     

Probabilistic prediction and ranking of human protein-protein interactions

Background  

Although the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%.     

Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

Background  

Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.     

Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants

Background  

Single nucleotide polymorphisms (SNPs) and genes that exhibit presence/absence variation have provided informative marker sets for bacterial and viral genotyping. Identification of marker sets optimised for these purposes has been based on maximal generalized discriminatory power as measured by Simpson's Index of Diversity, or on the ability to identify specific variants. Here we describe the Not-N algorithm, which is designed to identify small sets of genetic markers diagnostic for user-specified subsets of known genetic variants. The algorithm does not treat the user-specified subset and the remaining genetic variants equally. Rather Not-N analysis is designed to underpin assays that provide 0% false negatives, which is very important for e.g. diagnostic procedures for clinically significant subgroups within microbial species.     

MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Background  

The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes.     

JISTIC: Identification of Significant Targets in Cancer

Background  

Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect.     

Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

Background  

Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets.     

Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures

Background  

Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.