Single Cell RNA-seq Analysis

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.

Reports and visuals are crucial to single cell RNA-seq analysis; however it can be a pain to set up. That’s why Basepair includes a host of rich visual and interactive components as part of the interactive report that is generated every time you run the pipeline.

• 1. Quality Control Metrics

Basepair’s single cell RNA-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.

• 2. Clustering & Visualization

Included in the interactive single cell RNA-seq analysis 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.

• 3. Differential Expression Analysis

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.

• 4. Upregulation Across Genes

The scRNA-seq analysis 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.