A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions on various regulatory pathways. Currently, a statistical approach is universally used to identify the most relevant pathways in a given experiment. This approach only considers the set of genes present on each pathway and completely ignores other important biological factors. Here we show that in spite of its general adoption, and independently of the particular model used, this statistical analysis is unsatisfactory, and can often provide incorrect results. Using a systems biology approach, we developed an impact analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. Our preliminary work shows that the classical analysis produces both false positives and false negatives while the impact analysis provides biologically meaningful results. In this Phase I application, we are proposing to develop a prototype that would demonstrate the feasibility of a commercial software analysis package based on this novel approach. Our team has a very strong track record as demonstrated by: a large number of citations to our previous publications, a large user-base for our previously developed software (over 5,000 scientists from all 5 continents), and very strong letters of support. 1
The classical statistical approaches, which are universally used to identify the most relevant biological pathways in a given experiment, only consider the number of di(R)erentially expressed genes on each pathway and completely ignores other important biological factors. However, in spite of its general adoption, these statistical approaches are unsatisfactory, and can often provide incorrect results. We propose a novel signaling pathway analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc.
| Ansari, Sahar; Voichita, Calin; Donato, Michele et al. (2017) A novel pathway analysis approach based on the unexplained disregulation of genes. Proc IEEE Inst Electr Electron Eng 105:482-495 |
| Nguyen, Tin; Mitrea, Cristina; Tagett, Rebecca et al. (2017) DANUBE: Data-driven meta-ANalysis using UnBiased Empirical distributions-applied to biological pathway analysis. Proc IEEE Inst Electr Electron Eng 105:496-515 |
| Diaz, Diana; Donato, Michele; Nguyen, Tin et al. (2017) MICRORNA-AUGMENTED PATHWAYS (mirAP) AND THEIR APPLICATIONS TO PATHWAY ANALYSIS AND DISEASE SUBTYPING. Pac Symp Biocomput 22:390-401 |
| Bollig-Fischer, Aliccia; Marchetti, Luca; Mitrea, Cristina et al. (2014) Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion. Bioinformatics 30:3036-43 |
