Single-cell RNA-seq tutorials Archives - BioTuring's Blog
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Category Archives: Single-cell RNA-seq tutorials

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 […]

Batch Effect in Single-Cell RNA-Seq: Frequently Asked Questions and Answers
batch effect single cell rna seq 2

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 […]

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 […]

Dissect a spatial transcriptomics brain dataset with BBrowser: the human prefrontal cortex by Maynard et al. (2021)

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 […]

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 […]

Explore 10X Visium Spatial Transcriptomics data at ease with BioTuring Browser

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 […]

A tiny world inside non-small cell lung cancer revealed by single-cell omics: 35 cell types, and their marker genes

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 […]

Immunoglobulin genes up-regulated in lung adenocarcinoma infiltrating T cells: A report from BioTuring lung cancer single cell database

Highlights: In every biology textbook, expression of the B cell receptor (BCR) defines B cells, and the T cell receptor (TCR) defines T cells. However, in 2019, Ahmed et al. discovered a strange cell population in Type I Diabetes that coexpresses the BCR and TCR, and key lineage markers of […]

How to explore “Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution” (Duclos et. al 2019) | BioTuring Cellpedia

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 […]

A sub-clustering tutorial: explore T cell subsets with BioTuring Single-cell Browser

Single-cell RNA sequencing technologies have enabled many exciting discoveries of novel cell types and sub-types, such as the rosehip neurons (Boldog et al., 2018), disease-associated microglia (Keren-Shaul et al., 2017) and lipid-associated macrophages (Jaitin, Adlung, Thaiss, Weiner and Li et al., 2019). While sub-clustering cell populations is essential to find […]

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 […]