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Status |
Public on Aug 29, 2018 |
Title |
Mouse Boolean Implication Network |
Sample organism |
Mus musculus |
Experiment type |
Expression profiling by array Third-party reanalysis
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Summary |
Numerous gene expression datasets from diverse mouse tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available mouse datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
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Overall design |
11,758 published mouse microarray samples assayed on the GPL1261 were re-analyzed. RMA was used to normalize the RAW CEL files all together.
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Contributor(s) |
Sahoo D |
Citation(s) |
31091168, 32322218 |
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Submission date |
Aug 27, 2018 |
Last update date |
Apr 27, 2020 |
Contact name |
Debashis Sahoo |
E-mail(s) |
dsahoo@ucsd.edu
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Phone |
6508624736
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Organization name |
UCSD
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Department |
Pediatrics
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Lab |
Boolean
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Street address |
9500 Gillman Drive
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City |
La Jolla |
State/province |
California |
ZIP/Postal code |
92093 |
Country |
USA |
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This SubSeries is part of SuperSeries: |
GSE119128 |
An unbiased Boolean analysis of public gene expression data for core cell cycle gene classification |
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