Single Cell Analysis Archives - Page 2 of 2 - BioTuring's Blog
Data analysis made easy. For biologists, especially.

Category Archives: Single Cell Analysis

A sub-clustering tutorial: explore T cell subsets with BioTuring Single-cell Browser
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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, …)
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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 […]

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

BioTuring Single-cell Browser: a modern interactive single-cell database with 3D visualizations and downstream analyses
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Single-cell sequencing technologies have brought up an unprecedented level of resolution to omics studies with extremely detailed views into individual cells’ expression patterns. With the emergence of drop-based sequencing methods, the resolution now even comes with scale and efficiency. In a single experiment, scientists can now profile hundreds of thousands […]

Single-cell News and Views: March 2019
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1. An atlas of acute myeloid leukemia at single-cell resolution Adapted from Galan et. al., 2019 Acute myeloid leukemia (AML) is a cancer characterized by the accumulation of white blood cells in the bone marrow and blood. The disease has been notoriously challenging to study because of its extreme intratumoral […]

A review of Haining Lab’s work: Loss of ADAR1 in tumors overcomes resistance to immune checkpoint blockade
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Ever questioned why the immune system does not attack our own double-stranded RNA? Speaking of this, we should not ignore the role of ADAR, the gene that encodes Double-stranded RNA-specific adenosine deaminase enzyme, responsible for converting adenosines to inosines (A -> I editing) in double-stranded RNA (dsRNA) substrates. ADAR was […]

BioTuring Browser: the software to resolve major challenges in single-cell RNA-seq data analysis
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Single-cell RNA-seq technologies have opened up a completely new era for transcriptomic studies. For the first time ever, scientists can look at individual transcriptomic profiles of millions of cells, and better understand how each cell functions in a tissue. Yet science is confronting bigger challenges analyzing these massive amounts of […]

Cell Ranger Problems and Hera-T
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Today we finished the first version of Hera-T, a new single-cell RNA-seq quantification algorithm. We developed Hera-T by improving challenging alignment errors that Cell Ranger has. As a result, Hera-T is more accurate than Cell Ranger. Hera-T is more than 10 times faster than Cell Ranger, while consuming just a small amount of […]