"The true artist in painting or any other aesthetic expression sets out to express himself in terms of life he really knows".
In order to determine if the patterns of expressed genes during fracture healing were reflective of stem cells, we compared the set of genes that were differentially expressed during fracture healing to the expression sets that are associated embryonic stem cell and mesenchymal stem cells.
To derive a list of differentially expressed genes from each of the datasets, we loaded the gene expression sets into R/Bioconductor and performed standard log normalization.
These logical expression sets need to be implemented with a suitable structure search engine that understands SMARTS expressions to automatically classify compounds in databases, such as the ChemAxon [23] or Daylight [24] software tools.
Here, we propose a novel computational framework, namely local network component analysis (LNCA), to exploit data heterogeneity and automatically quantify accurate transcription factor activity (TFA) in practical terms, through integrating the partitioned expression sets (i.e., local information) and prior TF-gene regulatory knowledge.
Notably, for five of these interactions, the targets were identified only in the protein expression sets.
The retained gene expression sets included genes like PKNOX2 (AFiPSCs); HMBOX1, MGA (FiPSCs) or RAXL1 (AFiPSCs & FiPSCs).
A straightforward way to include other types of expression sets is to extend the sum of squares in (4) over more data sets.
We integrate two collections of expression sets, which reduce the number of possible genes to use as explanatory variables further; down to 4140 genes (since the numbers differ across the experiments, and we only utilize those genes for which we have measurements in all experiments).
Utilization of large gene expression sets, suffer from other problems.
These expression sets can be directly passed on to Cytoscape and used with CellFateScout.
