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Tag Archives: single-cell data

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 […]

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 […]

scRNA-Seq Cell Type Annotation: Common Approaches and Tools
scrna seq cell type annotation talk2data

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 […]

Comparing UMAP vs t-SNE in Single-cell RNA-Seq Data Visualization, Simply Explained
umap vs tsne global structure

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 […]

BioTuring Cell Search: a new tool to search for similar populations in public single-cell data sets

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 […]

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 […]

Exploring “Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma” (Li et al., 2018) | BioTuring Cellpedia

Welcome to our new BioTuring Cellpedia series! We are excited to introduce a new blog series that provides you with an overview of some interesting datasets indexed in BioTuring Browser public repository, a platform for instant access and reanalysis of published single-cell RNA-seq data. Details on their experimental designs and […]

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 […]

4 simple steps to perform differential expression analysis in single-cell data using BioTuring Browser

In single-cell data analysis, it is critical to understand how gene expression varies among different cell types, tissue compartments, conditions, or different patients. Although many tools and methods have been developed to support single-cell differential expression analysis, they all require some basic coding skills. In this blog, we show you […]