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 […]
From Single-cell RNA-sequencing to Spatial RNA-sequencing Since its invention, single-cell RNA-sequencing has become one of the most popular genomic tools. Despite its power in dissecting the transcriptome at individual cell resolution, single-cell RNA-sequencing stil faces several limitations. One of them is the lack of critical spatial information. Adding spatial context […]
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 […]
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 […]
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 […]
Spatial Transcriptomics Brain Data Is In Demand The spatial gene expression contributes significantly to brain morphology, physiology, and connectivity. However, obtaining spatial transcriptomics brain data has long been a technical challenge. Recently, with continuous breakthroughs in spatial transcriptomics sequencing technique and data analysis tools, spatial human brain single cell RNA-Seq […]
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 […]
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 […]
Cells don’t function independently. They belong to a complex and interconnected network. This makes gene expression profiling of individual cells not enough for understanding their activities and crosstalk in the tissue context. Like a marriage between imaging and RNA sequencing, Spatial Transcriptomics is a revolutionized method to map gene activity […]
Take a non-small cell lung tumor. What do we see? To answer this challenging question, a lot of single-cell omics experiments have been conducted, yielding significant insights into the heterogeneity of non-small cell lung cancer (NSCLC) microenvironment. While each successfully characterizes a facet of this ecosystem, until now no work […]
