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For each dataset we used each method to identify a module (or a set of genes for t-test) that is up-regulated, down-regulated or differentially expressed in the cases compared to the controls.
For three datasets (SLE, LESNICK-PD and ASTHMA) we found that all the methods failed to identify a module that overlapped with the KEGG pathway (as we show later, in at least one of those cases (LESNICK-PD) we identify multiple potential biological insights from the DEGAS module).
After we identify a module, we deflate the matrix by subtracting the signal of current set of loadings and latent variables from the data matrices.
Using Weighted Gene Analysisssion NetWGCNAAnalysis (weCNA) we identify a module (group) of highly related transcripts, correlated with IA lesion size.
The basic operation of this algorithm is to identify a module on the weighted graph by expanding a kernel node set, which originates from a given 'seed' node used as starting-point.
By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity.
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We identified a module which may be associated with direct nutrient transport, signal transduction and regulation in nutrient deficiency conditions in rice.
Finally, our method identifies a module including RB1 and a metagene DKK1/PRKG1/CSTF2T significant at p1 <0.001 and p2 = 0.02 levels.
In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression.
Our network analysis identified a module that was highly related to atherosclerosis in the IA and this module was highly enriched for genes in activated macrophages and spleen.
We also identified a module of tumor cells which seems to correspond to a new subtype that has not been identified before.
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