Sharif Digital Repository

Bulk tissue RNA-seq data has been widely used for investigating the transcriptome and analyzing it for different purposes. A single bulk sample of a heterogeneous population includes different cell-types each in different proportions. Bulk tissue RNA-seq measures the average expression level of genes across these cell types and does not account for cross-subject variation in cell-type compositions. Furthermore, biological signals might be masked by taking the average of gene expressions. Because of these reasons, bulk-RNA-seq is not suffcient for studying complex tissues. Knowing these cell-type compositions are important, because studying cell-specific changes in the transcriptome might be... 

Single Cell RNA sequencing (scRNA-seq) data provides more information about gene expression at cellular level. However, because of noise and sparsity that exist in scRNA-seq data, analysis of this data has faced to obstacles. Global normalization approach can not resolve correctly missing data that come from technical variability. So this approach cause emerging incorrect bias and dishonest conclusion about cell type. In this study we review some models for scRNA-seq data imputation,explain a new method for filtering genes and clustering data and use matrix completion algorithm for imputation data  

T lymphocytes or T cells are a type of white blood cell that play an important role in the immune system. Memory T cells are a subset of them that are able to reactivate when being re-exposed to the pathogen. Because of the properties of these cells, they are an attractive choice for immunotherapy. Initial memory subgroups were shown to be more persistently effective in immunotherapies. However little is known about development and differentiation of these subgroups. With the discovery of a new subset of T cells, called T memory stem cells (TSCM), They are considered to develop through four stages, naive T cells (TN), T memory stem cells (TSCM), central memory T cells (TCM) and effector... 

In this study a droplet-based microfluidic system is desighned and fabricated to effectiverly lyse MCF7 cells and extract and purify their DNAs. The main purpose of this study is to transfer all the steps from macro scale to a microfluidic system containing a fluidic chip. This system is a semi automatic system and every part of the lysis and purification process is performed in one step. The first step is to encapsulate single cells and multi cells inside the droplets. By controlling the concentration of the cell solution, the number of encapsulated cells inside the droplet is efficiently and easily controlled and the cells were encapsulated as single cells and as multi cells inside the... 

The advent and advance of single-cell technologies have enabled us to measure the cell function and identity by using different assays and viewing it by different technologies. Nowadays, we are able to measure multiple feature vectors from same- single cells from multiple abstract molecular levels (genome, transcriptome, proteome, ...) simultaneously. Hence, the analysts can view the cell from different yet correlated angles and study their behaviours. Such progress in joint single-cell assessments plus the development and spread of more common single-cell assays - that measure one feature vector per cell - caused the growing need for computational tools to integrate these datasets in order... 

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... 

Single-cell RNA-sequencing has opened opportunities to unlock the cell transcriptome at cellular-level resolution and investigate cell population structures and cell-type-specific gene expression patterns. Previously, we were only able to perform bulk RNA sequencing regardless of the cell type composition of cell populations. Now, with the advancements in sequencing technologies, it is possible to extract cell-level and cell-type-specific sequences, related to each type of cell separately and in a scalable and high-throughput manner. For such large studies, logistical constraints inevitably dictate that data be generated separately i.e., at different times and with different operators.... 

The purpose of this study is to develop the reduced order nonlinear modeling of single cell closed section thin walled composite beams. In this way, global behavior of one dimensional beam under axial load, bending and torsional moment is produced. This model is based on the classical lamination theory, and the nonlinear model is developedby usingthe von-karman strains. In this process the effects of material anisotropy and axial warping are considered. Numerical results are obtained for thin-walledcomposites box beams, addressing the effects of fiber angle and laminate stacking sequence. The nonlinear model is compared with theoretical results of homogeneous beams and the natural... 

Single-cell RNA sequencing (scRNA-seq) technologies have empowered us to study gene expressions at the single-cell resolution. These technologies are developed based on barcoding of single cells and sequencing of transcriptome using next-generation sequencing technologies. Achieving this single-cell resolution is specially important when the target population is complex or heterogeneous, which is the case for most biological samples, including tissue samples and tumor biopsies.Single-cell technologies suffer from high amounts of noise and missing values, generally known as dropouts. This complexity can affect a number of key downstream analyses such as differential expression analysis,... 

DNA methylation in one of the most important epigenetic variations, which causes significant variations in gene expressions of mammalians. Our current knowledge about DNA methylation is based on measurments from samples of bulk data which cause ambiguity in intracellular differences and analysis of rare cell samples. For this reason, the ability to measure DNA methylation in single-cells has the potential to play an important role in understanding many biological processes including embryonic developement, disease progression including cancer, aging, chromosome instability, X chromosome inactivation, cell differentiation and genes regulation. Recent technological advances have enabled... 

As sequencing technologies have advanced in the field of single cells, it has become possible to investigate complex and rare cell populations and discover regulatory relationships between genes. The detection of rare cells has been greatly facilitated by this technology. However, due to the large volume of data and the complex and uncertain distribution of data, as well as the high rate of technical zeros, the analysis of single cell data clusters remains a computational and statistical challenge. Dimensionality reduction is a significant component of big data analysis. Machine learning methods provide the possibility of better analysis by reducing the non-linear dimensions of data. A graph...