Performing single-cell quality control is a vital step in the data analysis pipeline. Setting QC parameters can significantly affect downstream analysis: too permissive QC filtering makes the dataset too noisy to read, and too stringent QC thresholds may remove important information. In this article, let’s discuss the art of setting […]
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 […]
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 […]
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: […]
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 […]
One essential step in the preprocessing of single-cell RNA-Seq data (scRNA-seq) is batch effect correction. However, much confusion remained around this step. In this article, let’s address the most frequently asked questions about handling batch effect in single-cell RNA-Seq. . What is Batch Effect in Single-cell RNA-Seq? Batch effect happens […]
Assigning cell type identity to cells is a basic yet vital step required in single-cell RNA Sequencing data analysis (scRNA-Seq), often done after dimensionality reduction and scRNA-Seq clustering . If you have successfully captured informative clusters, it’s time to face an even harder challenge: identify what cell type or cell […]
How to Make Sense of Single-cell RNA Sequencing Data? Less is More Thanks to single-cell RNA sequencing (scRNA-seq), researchers are blessed with a trove of information. Yet, this blessing is also a curse in data visualization and further analysis! Since each cell is described by its gene expression profile, our […]
B cells are like key ‘defenders’ in a soccer match. They play a critical role in supporting T cells, the ‘strikers’, in their battle against diseases, by producing antibodies to block specific antigens. Yet there are different types of defenders. Some form the memory that remembers the same pathogen for […]
Single-cell data sets are exploding in size. From a few thousand cells, we now see more than 1 million data points being generated (Cao et. al., 2019). Large single-cell atlases are valuable resources for exploration and insights, but also pose a great challenge to anyone who wants to view and […]
A machine learning model for cell type classification? When analyzing single-cell transcriptomic data, scientists often perform cell type annotations by checking individual marker genes. However, marker genes are not even consistent among different literature sources. Six months ago, armed with the largest curated single-cell transcriptomic data, BioTuring single-cell team naively […]
Multiple studies have shown smoking’s effects on the human bronchi. However, few have characterized its precise impact at cellular resolution. Published recently on Science Advances, a study by Duclos and colleagues has employed single-cell RNA sequencing into exploring the cellular changes in the human bronchial epithelium between current and never […]
