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  • Exploration of gene expression variation across cell types is easily achieved for the typical clustered embedding plots. Visual inspection along other variables of interest (such as drug treatment, time point, cell cycle, etc.) in a clustered embedding plot is less straightforward because the cells from different states in the variable of interest are grouped by cell type causing datapoints from different states to overlap.

    Use plot filtering to drill down into the data by isolating subsets of cells based on available experimental metadata. 

    For example, how does TNC expression vary over the course of lactation (milk_stage)?

    A single plot of TNC gene expression can't answer the question because cells at different stages will plot to the same areas when the data is clustered to determine cell type. By clicking “Filter plotted cells”, subsets of cells can be selectively displayed. To visualize TNC expression for cells early in the course of...


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  • Image from AnnData documentation

    Sharing data is a crucial step in the lifecycle of a research project. But in spite of its value, this step can be time-consuming, particularly when you have to re-format your data before sharing it to an online repository like Single Cell Portal. 

    To reduce this burden, we’ve updated the file types that we accept for SCP studies. You can now create a Single Cell Portal study by uploading an .h5ad file containing an AnnData object. This should speed up the data contribution process by bridging the gap between the files that you work with when analyzing your data, and the files that you ultimately share with the research community. In addition, uploading AnnData to SCP will make it easier for other researchers to incorporate your data into their own analyses. While this change most directly impacts researchers who work in Scanpy, we hope that...

  • Single-cell genomics gives researchers the power to interrogate the cell state of individual cells within a sample. This allows us to ask questions like, “how are different cell types affected by HIV?” and “which genetic programs are activated in cancer cells?” In investigating these questions, researchers often seek out differences in genetic expression across different groups of cells. To make it easier to spot these differences, Single Cell Portal now supports exploratory differential expression (DE) analyses for some datasets. This tool will help our users get a quick impression of the genes that distinguish between cell groups of interest, which can help verify cell annotations and inspire more rigorous follow-up work.

     

    How does it work?

    When you visit a study where this tool is available, you’ll see a “Differential Expression” button on the right-hand panel of the study’s ‘Explore’ tab.