| 000 | 02514nam a22003017a 4500 | ||
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| 001 | th526 | ||
| 003 | ISI Library, Kolkata | ||
| 005 | 20240919123047.0 | ||
| 008 | 220131b ||||| |||| 00| 0 eng d | ||
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_aISI Library _bEnglish |
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| 082 | 0 | 4 |
_223 _a572.8845 _bM741 |
| 100 | 1 |
_aMondal, Pronoy Kanti _eauthor |
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| 245 | 1 | 0 |
_aOn some statistical problems in single-cell transcriptome data analysis/ _cPronoy Kanti Mondal |
| 260 |
_aKolkata: _bIndian Statistical Institute, _c2021 |
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| 300 | _avii, 155 pages, | ||
| 502 | _aThesis (Ph.D.) - Indian Statistical Institute, 2021 | ||
| 504 | _aIncludes bibliographical references | ||
| 505 | 0 | _aChapter 1: Introduction -- Chapter 2: Modeling scRNA-seq expression data -- Chapter 3: Testing dierential scRNA-seq expression data -- Chapter 4: PseudoGA: Cell pseudotime reconstruction based on genetic algorithm -- Chapter 5: SCDI: A fast clustering-based method for Single-cell Data Integration -- | |
| 508 | _aGuided by Prof. Indranil Mukhopadhyay | ||
| 520 | _aSingle-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. | ||
| 650 | 4 | _aSingle-Cell RNA-Seqence | |
| 650 | 4 | _aGene Expression Modeling | |
| 650 | 4 | _aDifferential Expression | |
| 650 | 4 | _aPseudotime Estimation | |
| 650 | 4 | _aSingle-Cell Data Integration | |
| 856 |
_yFull Text _uhttp://dspace.isical.ac.in:8080/jspui/handle/10263/7255 |
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_2ddc _cTH |
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_c428458 _d428458 |
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