Tusch Guenter, Tolea Olvi, Kutsumi Yuka, Sam Vincent K, Mamidi Lakshmi
Medical and Bioinformatics Graduate Program, Grand Valley State University, Allendale, MI, USA.
Stud Health Technol Inform. 2013;192:1173.
Translational research of time-series of gene-expression microarray datasets makes use on gene expression profiles that have been obtained at different points in time. Our web-based multi-user program helps a researcher find temporal patterns like peaks in large pre-selected microarray data sets that include data from different but related studies in publicly available databases. If all studies use the same platform, data can be combined for a meta-analysis type approach. For combination of data from different platforms we allow only Affymetrix GeneChips, for which a method for pooling of information exists. To search for time patterns, the data are transformed into an abstract layer that is independent from the particular selection of time point in the individual studies.
基因表达微阵列数据集时间序列的转化研究利用了在不同时间点获得的基因表达谱。我们基于网络的多用户程序可帮助研究人员在大型预选微阵列数据集中找到诸如峰值等时间模式，这些数据集包含来自公开可用数据库中不同但相关研究的数据。如果所有研究都使用相同的平台，则可以将数据合并用于荟萃分析类型的方法。对于来自不同平台的数据合并，我们仅允许使用存在信息合并方法的Affymetrix基因芯片。为了搜索时间模式，数据被转换到一个抽象层，该层独立于各个研究中时间点的特定选择。
