Statistical analysis of global gene expression data: some practical considerations.
Applying appropriate error models and conservative estimates to microarray data helps to reduce the number of false predictions and allows one to focus on biologically relevant observations. Several key conclusions have been drawn from the statistical analysis of global gene expression data: it is worth keeping core information for each experiment, including raw and processed data; biological and technical replicates are needed; careful experimental design makes the analysis simpler and more powerful; the choice of the similarity measure is nontrivial and depends on the goal of an experiment; array information must be complemented with other data; and gene expression studies are 'hypothesis generators'.
Holzman
Ted
T
BIATECH, 19310 North Creek Parkway, Suite 115, Bothell, WA 98011, USA.
Kolker
Eugene
E
D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D004867:Equipment Design; D020869:Gene Expression Profiling / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D013673:Technology Assessment, Biomedical