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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).
05 October / / preprocess / batch effects / t-SNE / markers
Sequencing datasets often suffer from undesired technical variability, causing some cells to pick up more signal than others. The detection rate can vary considerably among genes. Ultimately, the measurements can also vary significantly when comparing data from different runs of the same experiment, taken on a different day, by a different technician, and so forth. This calls for preprocessing and normalization methods, making the values comparable across cells, genes and experimental conditions.
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.