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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) |
[email protected]
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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
|
| 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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