Open AccessDiagnostic plots for detecting outlying slides in a cDNA microarray experiment
Author(s) -
Taesung Park,
Sung-Gon Yi,
Seungyeoun Lee,
Duncan Lee
Publication year - 2005
Publication title -
biotechniques/biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/05383rr02
Subject(s) - microarray , microarray analysis techniques , complementary dna , computational biology , microarray databases , gene chip analysis , cluster analysis , pattern recognition (psychology) , biology , identification (biology) , measure (data warehouse) , dna microarray , data mining , computer science , gene expression , bioinformatics , gene , artificial intelligence , genetics , botany
Different sources of systematic and random error variations are often observed in cDNA microarray experiments. A simple scatter plot is commonly used to examine outlying slides that have unusual expression patterns or larger variability than other slides. These outlying slides tend to have large impacts on the subsequent analyses, such as identification of differentially expressed genes and clustering analysis. However, it is difficult to select outlying slides rigorously and consistently based on subjective human pattern recognition on their scatter plots. A graphical method and a rigorous diagnostic measure are proposed to detect outlying slides. The proposed graphical method is easy to implement and shown to be quite effective in detecting outlying slides in real microarray data sets. This diagnostic measure is also informative to compare variability among slides. Two cDNA microarray data sets are carefully examined to illustrate the proposed approach. A 3840-gene microarray experiment for neuronal differentiation of cortical stem cells and a 2076-gene microarray experiment for anticancer compound time-course expression of the NCI-60 cancer cell lines.