Biomedical subjects

Xinan Yang

Publications and source records attributed to Xinan Yang.

3 recordsLinked to original sources

PubMed2006

OrderedList--a bioconductor package for detecting similarity in ordered gene lists.

UNLABELLED: OrderedList is a Bioconductor compliant package for meta-analysis based on ordered gene lists like those resulting from differential gene expression analysis. Our package quantifies the similarity between gene lists. The significance of the similarity score is estimated from random scores computed on perturbed data. OrderedList illustrates list similarity in intuitive plots and determines the score-driving genes for further analysis. AVAILABILITY: http://www.bioconductor.org CONTACT: claudio.lottaz@molgen.mpg.de SUPPLEMENTARY INFORMATION: Please visit our webpage on http://compdiag.molgen.mpg.de/software.

Algorithms↗

PubMed2006

Similarities of ordered gene lists.

MOTIVATION: Many applications of microarray technology in clinical cancer studies aim at detecting molecular features for refined diagnosis. In this paper, we follow an opposite rationale: we try to identify common molecular features shared by phenotypically distinct types of cancer using a meta-analysis of several microarray studies. We present a novel algorithm to uncover that two lists of differentially expressed genes are similar, even if these similarities are not apparent to the eye. The method is based on the ordering in the lists. RESULTS: In a meta-analysis of five clinical microarray studies we were able to detect significant similarities in five of the ten possible comparisons of ordered gene lists. We included studies, where not a single gene can be significantly associated to outcome. The detection of significant similarities of gene lists from different microarray studies is a novel and promising approach. It has the potential to improve upon specialized cancer studies by exploring the power of several studies in one single analysis. Our method is complementary to previous methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent ones across multiple studies.

Algorithms↗

PubMed2005

Detecting common gene expression patterns in multiple cancer outcome entities.

Most oncological microarray studies focus on molecular distinctions in different cancer entities. Recently, researchers started using microarrays for investigating molecular commonalities of multiple cancer types. This poses novel bioinformatics challenges. In this paper we describe a method that detects common molecular mechanisms in different cancer entities. The method extends previously described concepts by introducing Meta-Analysis Pattern Matches. In an analysis of four prognostic cancer studies, involving breast cancer, leukemia, and mesothelioma, we are able to identify 42 genes that show consistent up- or down-regulation in patients with a poor disease outcome. These genes complement the set of previously published candidates for universal prognostic markers in cancer.

Algorithms↗