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Series GSE31291 Query DataSets for GSE31291
Status Public on Aug 12, 2011
Title Renormalized GSE8191 by using the parametric method
Sample organism Mus musculus
Experiment type Third-party reanalysis
Expression profiling by array
Summary Although principal component analysis is frequently used in multivariate/ analysis, it has disadvantages when applied to experimental or diagnostic data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the set. Second, the method is sensitive to experimental noise and bias between sample groups, since it cannot reflect the design of experiments; rather, it estimates the same weight and independence of all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. The resulting components were scaled to unify their size unit. Also, the principal axes were identified using training data sets and shared among experiments. This training data reflects the design of experiments, and its preparation allows noise to be reduced and group bias to be removed. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. Additionally, unknown samples were appropriately classified using pre-arranged axes, and principal axes well reflected the characteristics of groups in the experiments.
The summarized levels the genes are presented in the Matrix form.
Overall design PM data of samples in GSE8191 were parametrically normalized in chip-wise manner according to the three-parameter lognormal distribution method (Konishi et. al., 2009 PLoS ONE 3: e3555. Expression level of a gene was estimated by summarizing the corresponding PM data. A pseudo data was then derived in a form of antilog of the z-scores; the center of the pseudo data was 256. The pseudo data, ABS_CALL, and the normalized data (z-scores) are presented in the matrix form [see Supplementary file below]. PM data were not directly used for the study.
Contributor(s) Konishi T
Citation(s) 26678818
Submission date Aug 09, 2011
Last update date Jun 07, 2019
Contact name Tomokazu Konishi
Phone +81-18-872-1603
Organization name Akita Prefectural University
Department Bioresource Sciences
Lab Molecular Genetics
Street address Shimoshinjyo Nishi
City Akita
State/province Akita
ZIP/Postal code 010-0195
Country Japan
This SubSeries is part of SuperSeries:
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Reanalysis of GSM202668
Reanalysis of GSM202669
Reanalysis of GSM202670
Reanalysis of GSM202671
Reanalysis of GSM202672
Reanalysis of GSM202673
Reanalysis of GSM202674
Reanalysis of GSM202675
Reanalysis of GSM202676
Reanalysis of GSM202677
Reanalysis of GSM202678
Reanalysis of GSM202679
Reanalysis of GSM202680
Reanalysis of GSM202681
Reanalysis of GSM202682
Reanalysis of GSM202683
Reanalysis of GSM202684
Reanalysis of GSM202685
Reanalysis of GSM202686
Reanalysis of GSM202687
Reanalysis of GSM202688
Reanalysis of GSM202689
Reanalysis of GSM202690
Reanalysis of GSM202691
Reanalysis of GSM202692
Reanalysis of GSM202693
Reanalysis of GSM202694
Reanalysis of GSM202695
Reanalysis of GSM202696
Reanalysis of GSM202697
Reanalysis of GSM202698
Reanalysis of GSM202699
Reanalysis of GSM202700
Reanalysis of GSM202701
Reanalysis of GSM202702
Reanalysis of GSM202703
Reanalysis of GSM202704
Reanalysis of GSM202705
BioProject PRJNA154131

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE31291_Summarized_Matrix.txt.gz 2.6 Mb (ftp)(http) TXT
Processed data are available on Series record

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