Category Archives: Single Cell Analysis

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… Read more »

Venice: a non-parametric test for finding marker genes in single-cell RNA-seq data

In this blog, we introduce Venice a non-parametric approach for finding marker genes in single-cell RNA-seq data. Using a widely adopted benchmarking approach (Wang et al. 2019), Venice obtains the best accuracy among 14 other tools. Venice is also the fastest tool among all the benchmarked methods. Venice is open-source,… Read more »

BioTuring Browser: making published single-cell data really accessible

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… Read more »

Exploring the single-cell atlas of mouse retinal development (Clark et al., 2019): interesting stories revealed

Tiny as they are, retinal cells are extremely abundant and complex. The total population of about 100 million rod photoreceptors is among the most numerous neuron populations in the human body, second to cerebellar granule cells (Masland et al., 2012). Forming a sheet of tissue that is only ~200 μm… Read more »

Single-cell News and Views: March 2019

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… Read more »

A review of Haining Lab’s work: Loss of ADAR1 in tumors overcomes resistance to immune checkpoint blockade

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… Read more »

BBrowser: the software to resolve major challenges in single-cell RNA-seq data analysis

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… Read more »

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… Read more »

Principal component analysis explained simply

As we are entering the era of Big Data, everyone and their moms seem to be talking about PCA. All the papers you read mention PCA (with lots of jargon, of course). Half of the seminars you’ve been to this month touch on PCA. Your boss/collaborators suggest trying PCA on your data. “What… Read more »