High throughput technologies play an important role which allows proteomics and transcriptomes data analysis in a rapid pace. This research studies transcriptome, epigenetics and their relationship which could be beneficiary for understanding inner mechanism of body and also helpful for drug design and even therapeutics.
The main contribution of the thesis can be described in four points: the review of the applications of bioinformatics and high throughput datasets, and the proposed solution of the each of the three major fields preprocessing, network construction of scRNA datasets and purity estimation of using methylation data. The contributions are as follows:
(1) Reviews technologies and techniques for scRNA-seq, methylation and presents valuable insights. The review can be served as a reference resource for researchers who are willing to work on transcriptome and epigenetics high throughput data.
(2) We develop CDSImpute (Correlation Distance Similarity Imputation) to identify drop-outs induced in ScRNA-seq data rather than biological zeros and recover true gene expression. The improvement of the performance of downstream analysis is consistent with simulation data and several publicly available scRNA-seq datasets.
(3) By using network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene-gene association based on molecular expressions in a single-cell resolution. Few slants are noticed in the potential advantages of single-cell network construction, which is conducted in our research.
(4) The Purity estimation of samples is crucial for reliable genomic aberration identification and uniform inter-sample and inter-patient comparisons. Here a simple but effective and flexible method is designed to estimate the level of methylation.
 R. Azim, S. Wang, “CDSImpute: An Ensemble Similarity Imputation method for single-cell RNA sequence dropouts,” Computers in Biology and Medicine, vol. 146, 105658, Aug. 2022, doi: 10.1016/j.compbiomed.2022.105658. (SCIE, Q1, IF: 6.698)
 R. Azim, S. Wang, S. Zhou, and X. Zhong, “Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data,” Cell Cycle, vol. 19, no. 16, pp. 2028–2039, Aug. 2020, doi: 10.1080/15384101.2020.1789315. (SCIE, Q2, IF: 5.173)
 S. Zhou, S. Wang, Q. Wu, R. Azim, and W. Li, “Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression,” Comput. Biol. Chem., vol. 85, p. 107200, Apr. 2020, doi: 10.1016/j.compbiolchem.2020.107200. (SCIE, Q2, IF: 3.737)
 R. Azim and S. Wang, “Cell-specific gene association network construction from single-cell RNA sequence,” Cell Cycle, pp. 1–16, Sep. 2021, doi: 10.1080/15384101.2021.1978265. (SCIE, Q2, IF: 5.173)
 Y. Qin, A. B. M. Munibur Rahman, and R. Azim, “Research on innovation and strategic risk management in manufacturing firms,” in Proceedings of the International Conference on Electronic Business (ICEB), 2018, vol. 2018-Decem, pp. 505–521.
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