Admera Health Single-Cell RNA-seq Analysis

Single-Cell Sequencing
Written By Grace Box

In the ever-evolving field of genomics, single-cell RNA sequencing (scRNA-seq) has emerged as a groundbreaking technology that allows researchers to delve into the intricate world of gene expression at the single-cell level. Single-cell RNA sequencing data can be difficult to analyze, and it can be challenging to extract meaningful insights from the data. As a leader in genomics, Admera Health has developed a cutting-edge data analysis pipeline specifically tailored for 10x Genomics single-cell RNA-seq data. This pipeline empowers researchers to unlock the hidden secrets of cellular diversity and function, leading to groundbreaking discoveries. In this post, we will explore how Admera Health's scRNA-seq data analysis pipeline can elevate your research to new heights.

A Comprehensive Approach to Data Analysis:

Admera Health's scRNA-seq data analysis report provides a comprehensive and user-friendly solution to process and interpret single-cell RNA-seq data. From quality control to advanced downstream analyses, the pipeline encompasses a range of essential steps that are crucial for deriving meaningful insights from scRNA-seq experiments. Detailed information on steps can be found in the Single-Cell RNA-seq Sample Report.

  • Cell Ranger and Sample Aggregation: The pipeline begin with performing alignment using the reference genome obtained from 10X Genomics, filtering, barcode counting and unique molecular identifier (UMI) counting. Cell Ranger utilizes the cellular barcodes obtained through the Chromium platform to generate feature-barcode matrices. This enables the identification of distinct cell populations and the exploration of gene expression patterns. Admera Health aggregates multiple samples using Cell Ranger’s aggr pipeline to create an experiment-wide feature-barcode matrix.

  • Raw Data Quality Control (QC) and Preprocessing:

    • Sequencing Stats and Barcode Rank Plot: Admera Health first examine the sequencing statistics which include important metrics such as UMI counts, Q30 rates (indicating the percentage of high-quality base calls), and mapping rates to various genomic regions. These measures provide insights into the quality and reliability of the sequencing data. We review the barcode rank plots for individual samples to evaluate scRNA-seq data characteristics based on UMI distribution. By examining the UMI counts in the plot, we can gain valuable information about the gene expression patterns within the dataset.

    • The Percentage of Mitochondrial Genes: Admera Health calculate the percentage of mitochondrial genes (%mito) using the Seurat tool. Mitochondrial genes code for proteins involved in cellular respiration, and an elevated percentage of mitochondrial gene expression can indicate various issues, including cell stress, cell damage, or technical artifacts during library preparation.

  • Dimensionality Reduction and Cell-type Assignment: We perform two common types of dimensionality reduction using Seurat R-package, namely, t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP).
    Afterward, we employ our algorithm developed with the assistance of the CellMarker2.0 database to identify gene markers associated with your gene of interest. Using the Seurat tool, we identify the corresponding clusters.

  • Differential Expression Analysis: Differential Expression (DE) analysis was performed using Seurat for each cell type. To ensure the reliability of the results, only genes meeting specific criteria were included for further analysis.

  • Heatmap and Enrichment Analysis: After performing a differential expression analysis, we identified a set of genes that displayed significant differential expression for different cell types. From this set, we selected the top N genes for further analysis. Subsequently, we utilized the same gene set for enrichment analysis.

  • Pseudotime and Trajectory Analysis: Single-cell Pseudotime and trajectory analysis are computational methods used to infer the developmental progression and lineage relationships of individual cells within a population. These approaches leverage single-cell transcriptomic data to unravel the temporal ordering and spatial relationships of cells, enabling the reconstruction of developmental trajectories and the identification of key regulatory events during cellular development.

Elevating Research through Expert Support:

Admera Health not only provides an advanced scRNA-seq data analysis pipeline but also offers expert support from a team of skilled bioinformaticians. This ensures that researchers receive personalized guidance, assistance with data analysis, and customized analysis strategies tailored to their research goals.

Conclusion:

Admera Health's 10x single-cell RNA-seq data analysis pipeline is designed to empower researchers to unlock the full potential of their scRNA-seq data. By providing a comprehensive approach that includes Cell Ranger and Sample Aggregation, Raw Data QC and Preprocessing, Sequencing Stats and Barcode Rank Plots, The Percentage of Mitochondrial Genes, Dimensionality Reduction and Cell-type Assignment, Differential Expression Analysis, Heatmap and Enrichment Analysis, Pseudotime and Trajectory Analysis the pipeline enables researchers to extract meaningful insights from their single-cell RNA-seq experiments. With the expert support of Admera Health's bioinformaticians, researchers can elevate their research and uncover new discoveries that have the potential to revolutionize our understanding of cellular biology and advancement in research.

Whether you're exploring cellular diversity, investigating disease mechanisms, or identifying novel therapeutic targets, Admera Health's scRNA-seq data analysis pipeline is here to support your research journey and help you make significant contributions to the field of genomics.

References (find more in our Single-Cell RNA-seq Sample Report)

Grace Box
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