Datasets

Interact with Parse computational pipeline outputs from three separate single cell sequencing datasets below. All interactive output files are viewable on any standard browser, without any software downloads. These reports illustrate the unparalleled quality and sensitivity of data obtainable with the Parse Whole Transcriptome Kits, in both transcript and gene detection for samples as large as 100,000 cells.
For each of the following datasets, you have the option to download an interactive html experimental report, a digital gene matrix, a spreadsheet of cell metadata, and a comprehensive list of genes.

High-Throughput Sequencing of 100,000 Mouse and Human Cells
1:1 Mix of HEK293/3T3 Cells - 100,000 Cells
- Explore the high sensitivity of the Whole Transcriptome Kit’s gene detection in a mix of human and mouse cell lines.
- See how combinatorial barcoding enables high throughput sequencing of cells, with much lower doublet rates than droplet solutions.

High Resolution Profiling of Immune Cells
PBMCs from a Healthy Donor - 67,000 cells
- Explore how the Parse Whole Transcriptome Kit enables sensitive single cell profiling across all immune cell types and sub-types.
- View how the Whole Transcriptome Kit enables robust detection of genes such as CD4 and CD8 that are often missed in scRNA-sequencing.

Profiling Single Nuclei from an Embryonic Mouse Brain
E18 Mouse Whole Brain - 62,000 Nuclei
- Explore how the Parse Whole Transcriptome Kit maintains high transcript and gene detection in nuclei (15,000 and 3,700/nuclei respectively).
- See the specificity of cluster-specific genes expression (up to 4000x enrichment), with much lower levels of background contamination than other single cell sequencing solutions.

The Parse Biosciences Evercode™ Technology
Technical brochure for use with or without datasets
- Learn how the Evercode split-pool combinatorial barcoding leads to improved experimental design and eliminates the need for special equipment
- Understand how Evercode improves data quality and achieves robust gene detection across different sample types