Statistical framework for gene expression data analysis.
DNA (mRNA) microarray, a highly promising technique with a variety of applications, can yield a wealth of data about each sample, well beyond the reach of every individual's comprehension. A need exists for statistical approaches that reliably eliminate insufficient and uninformative genes (probe sets) from further analysis while keeping all essentially important genes. This procedure does call for in-depth knowledge of the biological system to analyze. We conduct a comparative study of several statistical approaches on our own breast cancer Affymetrix microarray datasets. The strategy is designed primarily as a filter to select subsets of genes relevant for classification. We outline a general framework based on different statistical algorithms for determining a high-performing multigene predictor of response to the preoperative treatment of patients. We hope that our approach will provide straightforward and useful practical guidance for identification of genes, which can discriminate between biologically relevant classes in microarray datasets.
Modlich
Olga
O
Institute of Chemical Oncology, University of Düsseldorf, Düsseldorf, Germany.
Munnes
Marc
M
D000465:Algorithms; D000903:Antibiotics, Antineoplastic / Q000627:therapeutic use; D018906:Antineoplastic Agents, Alkylating / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000145:classification / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism*; D017024:Chemotherapy, Adjuvant; D015331:Cohort Studies; D003520:Cyclophosphamide / Q000627:therapeutic use; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D015251:Epirubicin / Q000627:therapeutic use; D005260:Female; D015870:Gene Expression; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012641:Selection, Genetic
D000903:Antibiotics, Antineoplastic; D018906:Antineoplastic Agents, Alkylating; D015251:Epirubicin; D003520:Cyclophosphamide