KonAnBio offers a comprehensive range of customised, high-quality services in single cell RNA sequencing data analysis, for science research in single-cell transcriptomics and related biomedical industry worldwide. Our company can help you with scientific and meticulous design for material selection, cell isolation, library construction, sequencing and data analysis to ensure high-quality research results.
A single‐cell RNA sequencing can be used to analyse the transcriptome at single‐cell level for over millions of cells in a single study. By using a single-cell RNA seq we can classify, characterise and distinguish each cell at the transcriptome level. Single-cell RNA sequencing use in biomedical research has advanced the understanding of the pathogenesis of disease and at the same time has provided valuable insights into new diagnostic and therapeutic strategies.
Data analysis steps of scRNA-seq can be generally divided into three stages: raw data processing and QC, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis tailored to specific research scenarios.Single cell RNA seq can help researchers investigate cellular functions to better understand cell-to-cell differentiation, cell lineage relationships, and disease evolution. The analysis can also help in discovery of potential biomarkers and drug targets as well as analysis of rare cell types. The technology interrogates individual cells. Quality control and preprocessing: Quality control is a critical first step in single-cell RNA sequencing data analysis. Single-cell RNA-Seq provides three types of quality control metrics: Read counts, coverage and correlation. In addition to those quality metrics, further preprocessing is often carried out to remove unwanted elements from certain downstream analyses.
Exploratory analysis: This step consists of making use of the preprocessed single-cell RNA-seq data to identify groups of similar cells. In exploratory analysis we aso visualise using non-linear dimensionality reduction algorithms that will help us find answers for biological and technical questions that arise.
Cell type identification: Single-cell RNA seq cell type identification involves the partitioning of data into clusters of individual cells, and each cluster is annotated to different cell types based on canonical markers found in the differentially expressed genes of the cluster.
Trajectory inference: Trajectory inference allows a better study of the underlying dynamics of a biological process of interest, such as cellular development, differentiation and immune response. Trajectory inference has enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression.
Integrative single-cell analyses: Integrative single-cell analyses combine different datasets, data types and species together. As a result, a more accurate and detailed cell labelling and mechanistic insight into gene regulation in the studied system is obtained. This can further be divided into: integrative multiple single cells with RNA-seq, integrating single-cell RNA-seq and epigenomics, integrating single-cell RNA-seq and proteomics, cross-species integration.