dc.contributor.author |
Mondal, Pronoy Kanti |
|
dc.date.accessioned |
2022-01-28T08:49:42Z |
|
dc.date.available |
2022-01-28T08:49:42Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
162p. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10263/7255 |
|
dc.description |
Thesis is under the supervision of Prof. Indranil Mukhopadhyay |
en_US |
dc.description.abstract |
Single-cell transcriptome data provide us with an enormous scope of studying biological systems at the cellular level. We aim to address different problems involving the statistical analysis of single-cell RNA-seq data. First, we develop a realistic statistical model for fitting single-cell transcriptome data based on a two-part model for gene-wise unimodal or bimodal distribution in addition to using a generalized linear model with a probit link for zero occurrences. In continuation to this work, we discuss testing methods to compare transcriptome profiles between two groups. We suggest two different likelihood ratio-based tests under unimodal and bimodal assumptions. We also propose a cell pseudotime reconstruction method avoiding dimensionality reduction, which may lead to loss of information in the data. We view the pseudotime reconstruction problem as finding the best permutation based on a cost function and invoke a genetic algorithm to find the optimum permutation. We also discuss a novel method to remove batch effects to facilitate merging two or more single-cell RNA-seq datasets. All our approaches are supported by simulation study and real data analysis. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Indian Statistical Institute, Kolkata |
en_US |
dc.relation.ispartofseries |
ISI Ph. D Thesis;TH526 |
|
dc.subject |
Single-cell RNA-seq |
en_US |
dc.subject |
Gene expression modeling |
en_US |
dc.subject |
Differential expression |
en_US |
dc.subject |
Pseudotime estimation |
en_US |
dc.title |
On Some statistical problems in single-cell transcriptome data analysis |
en_US |
dc.type |
Thesis |
en_US |