In 2019, there’s no longer a reason to wait weeks for your bioinformatics core to analyze your sequencing data. Do it yourself today in just a few clicks with Basepair’s automated scRNA-seq pipeline.

Visualize and Interact With Your Single Cell Data

Basepair’s scRNA-seq pipeline is fast and built on the best peer-reviewed tools available. All steps of the pipeline — from alignment, expression quantification, to clustering and visualization — are entirely automated. Plus, we understand how crucial reporting and visuals are to downstream analysis and what a pain it can be to set up. That’s why we include a host of rich visual and interactive components as part of the interactive report that is generated every time you run the pipeline. 


Basepair’s scRNA-seq analysis report includes boxplots that show the per-cell quality metrics.

  • Genes/features detected shows the number of unique genes detected per cell. Very low unique genes can indicate empty droplets, while very high values can indicate droplets with two or more cells.
  • UMI counts show the number of unique molecules detected per cell. Very low or high values indicate consequences similar to those of unique genes. 
  • Mitochondrial proportion is the proportion of reads mapping to the mitochondrial genome. Values higher than 0.1 can indicate low-quality or dying cells.

Generally, you want to remove outliers and see cells clustering into one group. Although we provide sensible default filtering thresholds, we show the data before and after filtering so you can assess the results yourself.


Included in your interactive report are clustering and visualization with t-SNE, UMAP and PCA plots. This groups your cells into biologically meaningful clusters, where each cluster usually corresponds to different tissues or cell types. You can then visualize the expression of particular genes across the clusters. This allows you to identify a cluster as corresponding to a particular cell type based on its known gene markers.


The differential expression tables show genes that are uniquely up- or down-regulated in each cluster of the t-SNE and UMAP plots. The analysis compares each cluster of cells to all other clusters, outputting log2 fold change, p-value, and adjusted p-values for each gene. Particularly useful metrics here are the log2 fold change and adjusted p-value. They indicate the magnitude of the gene’s expression change and reduce the chance of a false positive, respectively.


The single cell report includes a heatmap that visualizes the top 10 most up-regulated genes for each cluster in the t-SNE and UMAP plots. Its main purpose is to visualize the discriminatory power of the selected genes to separate the clusters. An additional useful feature of the heatmap is that it can highlight clusters that are not easily distinguishable from each other but may correspond to the same cell type.

Take Your scRNA-seq Data Analysis to the Next Level With Basepair

For every week spent waiting for someone else to process your data, or for every hour spent feeding genomics data into algorithms, converting file formats, waiting for everything to finish processing to move onto the next step, you’re taking away valuable time that could be used to delve into your research further. Why not put yourself in the driver’s seat and automate your NGS analysis process with Basepair?

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