Data analysis made easy. For biologists, especially.
BIRC5, known as Survivin, is one member of the Inhibitor of apoptosis protein family and plays an important role in preventing cell death (Jaiswal et al., 2015) and poor survival in cancers (Supplementary). For this reason, BIRC5 can be hypothesized to be associated with tumor resistance to cancer treatments. With […]
Acute myeloid leukemia (AML) happens when the myeloid precursor cells in the bone marrow grow abnormally (making up myeloblasts). A common treatment for AML is Venetoclax. This drug inhibits BCL2, an oncoprotein that prevents cell death and is often overexpressed in the majority of blasts from AML patients (Bensi et […]
Single-cell data sets are exploding in size. From a few thousand cells, we now see more than 1 million data points being generated (Cao et. al., 2019). Large single-cell atlases are valuable resources for exploration and insights, but also pose a great challenge to anyone who wants to view and […]
“How to extract the biology behind this huge matrix of thousands of rows and columns?” Large numbers of data points hold valuable biological stories yet are tricky to graph and analyze. Imagine you had the data for 1000 features and observations, your mind would be full of questions, wondering how […]
Cancer treatment, as challenging as it sounds, has been revolutionized by recent advances in immune checkpoint blockade (ICB) therapies, which use monoclonal antibodies (mAb) to block the suppressive immune checkpoints, helping the patient’s immune system to kill cancer cells. However, despite several clinical case studies of success, some patients still […]
Nominated as Method of the year by Nature in 2019, single-cell multimodal omics has enabled scientists to uncover many facets of the cells by simultaneously measuring multiple modalities in one single-cell experiment. One candidate for multimodal analysis is CITE-Seq, a technique that layers cell surface protein information on top of […]
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 […]
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 […]
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 […]
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 […]
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 […]
We are glad to announce that we will upsize the current single-cell database in BioTuring Single-cell Browser to 5,500,000 cells this September. With this release, we will double the current number of publications indexed in BioTuring Single-cell Browser, and cross the number of cells hosted on available public single-cell data […]
