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Series GSE31289 Query DataSets for GSE31289
Status Public on Aug 12, 2011
Title Renormalized GSE5509 by using the parametric method
Sample organism Rattus norvegicus
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 GSE5509 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:
Reanalysis of GSM127049
Reanalysis of GSM127050
Reanalysis of GSM127051
Reanalysis of GSM127052
Reanalysis of GSM127053
Reanalysis of GSM127054
Reanalysis of GSM127055
Reanalysis of GSM127056
Reanalysis of GSM127057
Reanalysis of GSM127058
Reanalysis of GSM127059
Reanalysis of GSM127060
Reanalysis of GSM127061
Reanalysis of GSM127062
Reanalysis of GSM127063
Reanalysis of GSM127064
Reanalysis of GSM127065
Reanalysis of GSM127066
Reanalysis of GSM127067
Reanalysis of GSM127068
Reanalysis of GSM127069
Reanalysis of GSM127070
Reanalysis of GSM127071
Reanalysis of GSM127072
Reanalysis of GSM127073
Reanalysis of GSM127074
Reanalysis of GSM127075
Reanalysis of GSM127076
Reanalysis of GSM127077
Reanalysis of GSM127078
Reanalysis of GSM127079
Reanalysis of GSM127080
Reanalysis of GSM127081
Reanalysis of GSM127082
Reanalysis of GSM127083
Reanalysis of GSM127084
Reanalysis of GSM127085
Reanalysis of GSM127086
Reanalysis of GSM127087
BioProject PRJNA154127

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
GSE31289_Summarized_Matrix.txt.gz 6.2 Mb (ftp)(http) TXT
Processed data are available on Series record

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