Filtered blog posts by category.
23 November / / cell scoring / clustering / t-SNE / markers
Single-cell RNA sequencing is all about exploring cell subpopulations and their distinguishing properties. In this blog, you will learn how to automatically select subset of cells based on different criteria, including generating own scores. Different subsets will then be explored using previously known, as well as newly discovered marker genes. We will work the brain cells from Drosophila melanogaster - a fruit fly, published in the study if Li et. al (2017).
04 October / / clustering / data mining / genomics
How can we find cell populations in the absence of known markers? Automatic clustering methods and some background knowledge might help! Louvain Clustering is a neat clustering method that detects communities in a network of nearest neighbours. We will use this on an example of Bone marrow mononuclear cells with AML data (Zheng et al., 2017), that we have retrieved with the Single Cell Datasets widget. First, let us observe the data in a Data Table.
Welcome to our very first blog on scOrange, a visual programming environment to explore single-cell sequencing data sets without writing a single line of code! Many questions related to organism development, evolution, disease progression or cell population heterogeneity cannot be answered with traditional, bulk sequencing protocols. The emerging single-cell RNA sequencing (scRNA-seq) assays greatly improve the resolution, as one can investigate transcriptome profiles on an individual cell level. This brings new challenges for computational analysis methods, as the resulting large gene expression matrices provide exciting opportunities for data visualization and modeling.