scRNA-seq data analysis Archives - BioTuring's Blog
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Tag Archives: scRNA-seq data analysis

Single-cell RNA-Seq Trajectory Analysis Review
trajectory analysis umap 1

Cellular state often exists in a continuum rather than distinct phases. A prime example is cell development and differentiation. With single-cell RNA-Seq, researchers can observe this continuum of transcriptomic changes via analysis of individual cells. The need to computationally model these dynamics leads to the birth of single-cell RNA-seq trajectory […]

The Basics of DESeq2 – A Powerful Tool in Differential Expression Analysis for Single-cell RNA-Seq
deseq2 single cell example

Differential expression analysis is a common step in a Single-cell RNA-Seq data analysis workflow. In our previous post, we have given an overview of differential expression analysis tools in single-cell RNA-Seq. This time, we’d like to discuss a frequently used tool – DESeq2 (Love, Huber, & Anders, 2014).  According to […]

BioTuring Data Science Platform: A Jupyter notebook library of latest methods for single-cell analysis in R and Python
r single cell analysis bioturingdatascienceplatform

Did you know that by the end of 2021, the number of tools for single-cell RNA-sequencing (scRNA-seq) data analysis has passed 1,000? (Zappia and Theis, 2021) This massive resource accelerates the exploration of single-cell data. At the same time, the plethora of options poses several challenges for researchers, such as:   […]

The Maze of Differential Gene Expression Analysis in Single-cell RNA-Seq
scrna seq differential expression - venice

Single-cell RNA sequencing (scRNA-seq) unfolds biological processes at individual cell resolution. One key step that makes up the power of scRNA-seq is the spotting of differentially expressed (DE) genes. However, characteristics like high heterogeneity and data sparsity (high zero counts) are the main obstacles in finding DE genes in scRNA-seq […]

The Essence of scRNA-Seq Clustering: Why and How to Do it Right

Clustering is The Microscope For scRNA-Seq data In previous posts, we have walked you through important steps in analyzing your single-cell RNA sequencing (scRNA-Seq) data, including data visualization and normalization. This time, let’s explore the next logical step in the data analysis pipeline: clustering scRNA-Seq data.  What is scRNA-Seq clustering?  […]

A Guide to scRNA-Seq Normalization
scrna seq normalization normalized value

In the previous post, we talked about how to visualize single-cell RNA Sequencing (scRNA-seq) data to gain meaningful insights. But there are many steps from raw sequencing data to such beautiful visualization (and further analysis) that decide whether or not researchers can make sense of their data. They include preprocessing […]

Zooming in the intratumoral heterogeneity of liver cancer with BBrowser single cell database
intratumoral heterogeneity - liver cancer cell composition

Introduction  Why are tumors so resilient against available cancer therapies? One answer lies in intratumoral heterogeneity.  Intratumoral heterogeneity describes the diversity in tumor cell populations. Cancer cells exhibit startling distinctions in their types, shape, metabolic activities, transcriptomic profiles, and more. The causes of intratumoral heterogeneity are both heritable (clonal expansion […]

A new tool to interactively visualize single-cell objects (Seurat, Scanpy, SingleCellExperiments, …)

Seurat (Butler et. al 2018) and Scanpy (Wolf et. al 2018) are two great analytics tools for single-cell RNA-seq data due to their straightforward and simple workflow. However, for those who want to interact with their data, and flexibly select a cell population outside a cluster for analysis, it is […]