• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.10-20
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• 2024

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1512-1518
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• 2021

Since single cell RNA sequencing provides the expression profiles of individual cells, it provides higher cellular differential resolution than traditional bulk RNA sequencing. Using these single cell RNA sequencing data, clustering analysis is generally conducted to find cell types and understand high level biological processes. In order to effectively process the high-dimensional single cell RNA sequencing data fir the clustering analysis, this paper uses a variational autoencoder to transform a high dimensional data space into a lower dimensional latent space, expecting to produce a latent space that can give more accurate clustering results. By clustering the features in the transformed latent space, we compare the performance of various classical clustering methods for single cell RNA sequencing data. Experimental results demonstrate that the proposed framework outperforms many state-of-the-art methods under various clustering performance metrics.

• Journal of Life Science
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• v.34 no.6
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• pp.434-441
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• 2024

Mammary gland tumors are the most common tumors detected in non-spayed female dogs and pose a significant clinical challenge. Due to the strong similarity between canine mammary tumors (CMT) and human breast cancer (HBC), biomarkers identified in HBC can also be detected in CMT. These biomarkers have been shown to offer valuable insights into early diagnosis, prognosis, and treatment strategies. The purpose of this article is to provide a concise overview of CMT biomarkers based on the current literature. Traditional treatments for CMT in dogs typically begin with surgery, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these treatments alone are not always fully effective. A diagnostic biomarker can detect the presence of a disease or the characteristics of a disease and classify an individual's status. Prognostic biomarkers focus on predicting the expected progression, recurrence, or survival of the disease in patients. By utilizing advances in understanding the mechanism of canine-specific mammary gland tumors, the estimation of biomarkers offers hope for improved outcomes in cancer patients. Novel technologies, such as single-cell RNA sequencing analysis, could provide a valuable resource for deciphering intra- and inter-tumoral heterogeneity. This review paper explores current research on CMT biomarkers and suggests directions for their development.