Read counts for each of the nine samples were extracted and used for differential expression analyzes using the EdgeR package in Bioconductor 3.0 (Robinson et al. 2009).
Let us illustrate the connection between (ordinary) half-linear differential equations analyzed in this paper and partial differential equations with p-Laplacian.
Transcript reads were normalized and differential expression analyzed with DESeq2 (Love et al. 2014).
Only 9 of the differential spots analyzed did not match all the confidence identity requirements, and are indicated in additional file 15.
Out of the 136 differential spots analyzed from silver- and Coomassie-stained gels, 49 were identified by searching in the M. truncatula EST database and the SwissProt databases.
We used the GeneChip® Human Transcriptome Array 2.0 (HTA 2.0) for analyzing differential gene expression studies to analyze differential splicing patterns in NT and iAs-T cells.
The performance differentials are analyzed using varying network load and mobility.
Absolute mortality differentials were analyzed using Decomposition Analysis.
To avoid erroneous values due to sample deterioration, blood cell counts and differentials were analyzed in the laboratories of each hospital that participated in this study.
CRP levels and white blood cell (WBC) counts with differentials were analyzed at the routine department of clinical chemistry at Uppsala University Hospital.
