U.S. Department of Health and Human Services
National Institutes of Health
National Center for Advancing Translational Sciences

Select dataset(s) from existing experiment






Splash page

When you first access the application, a pop-up box will include some background information as shown below.

Selection of data

Existing datasets

  1. Select either single-cell RNA-Seq or Bulk RNA-seq data.

  2. Under the Existing datasets header, choose one or more existing data sets. Click on a row to select a data set. Click again to de-select.

  1. Once the data set(s) are selected, you can subset the data to target specific factors (e.g. specific samples, condition, Seurat clusters, etc.)

    • To select cells/samples from specific experimental groups, click Subset data and a pop-up modal will appear as shown below. Here, select specific groups based on the factor specified in Select factor.
    • You can select samples by selecting the check box. You can also Select all or Deselect all as shown below.
  2. If you want to load the entire dataset rather than subsetting the data, just click on the Load full dataset(s) button. If you have subset the data and want to load it, click on the Load selected sample(s).

  3. There are several options on the scRNA-Seq side including: Type of clustering (explained in detail below), setting the minimum detection rate for all genes (Detection rate threshold), excluding rRNA, mitochondrial and pseudo genes (Exclude RNA/MT/pseudo genes) and down sampling the total number of cells for an analysis (Downsample cells).

    • Under Type of clustering, you can choose Use pre-assigned clusters in metadata, which will utilize the pre-computed clusters in the metadata. If you choose Run multiple resolutions using Seurat, this will run multiple resolutions in Seurat from 0.4 to 2.8 with a 0.4 step for you to view in later areas in the application. This is useful for comparing results from different resolutions.
    • You can also select a gene detection rate threshold and choose to filter out rRNA-, mitochondrial- and pseudo genes (we recommend this step).