Loading...

Modelling Cell`s State in Different Cell Types

Saberi, Amir Hossein | 2018

1755 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52210 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Hossein Khalaj, Babak; Motahari, Abolfazl
  7. Abstract:
  8. Existence of heterogeneity in vital tissues of complex multicellular organisms like mammals, and fatal tissues like cancer on one hand, and limited access to biological properties of their components on the other hand, turn the study of these tissue traits to one of the most interesting fields in bioinformatics. One of the hottest subjects in this field is the recognition of functional components of these tissues by using bulk data extracted from the whole tissue.Almost every method that aims to achieve such a purpose, particularly using gene expression data, assumes that all of the cell types which constitute the studied tissue have a deterministic expression profile.In this thesis we present a novel algorithm, that has a probabilistic lookout to the expression profile of each cell type, and sets that profile as a random vector. With this assumption, the mentioned algorithm estimates some descriptive statistics of that random vector. As a result, it is feasible to estimate gene regulatory network for each cell type in the subject tissue.While former methods just estimate mean vector of aforementioned random vector,the presented algorithm in this thesis provides more informative results with better precision in estimation of mean vectors.Another advantage of this algorithm is the capability to adapt with other biological data like mutation frequency and also datasets from other fields like hyper spectral images. This capability emerges from the fact that we do not use extra biological information to solve the problem. In this thesis, in addition to presenting theoretical basis of aforementioned algorithm, the result of the algorithm on simulated and real data will be evaluated
  9. Keywords:
  10. Gene Expression Data ; Gene Regulatory Networks ; Gaussian Mixture Modeling ; Single Cell Sequencing ; Simplex Method ; Convex Combination Algorithm

 Digital Object List

 Bookmark

...see more