Loupe Browser uses the same underlying algorithms as Cell Ranger for its core analyses, providing a consistent framework for data exploration.

For detailed information on specific methods, refer to the corresponding sections on the Cell Ranger Algorithms page.

Clustering (graph based and k means)

Loupe Browser employs graph-based and k-means clustering to group cells with similar gene expression profiles, enabling the identification of distinct cell populations within your data.

Relevant Cell Ranger Algorithms Link

Differential gene expression

Loupe Browser compares average gene expression between cell groups using the exact negative binomial test, switching to the faster edgeR asymptotic test for large counts.

Relevant Cell Ranger Algorithms Link

Multi-sample comparison

Loupe Browser (v7.0 and later) enables differential expression within cell types and between experimental conditions with multi-sample comparison (aka pseudo-bulk)

To test for differences in mean expression between groups of cells within a cell type, Loupe Browser first reads the entire gene expression count matrices from Cell Ranger. For every cluster (cell type), a new matrix is generated in which each column is the sum of all barcodes in that sample within the selected cluster (cell type). Loupe Browser then applies the exact negative binomial test proposed by the authors of the sSeq method (Yu, Huber, & Vitek, 2013) to the condensed sample matrices to test for significant differences.

Loupe Browser's implementation differs slightly from the original sSeq method in its normalization approach. Instead of using DESeq's geometric mean for library size, Loupe Browser calculates size factors based on the total UMI count per sample. Similar to sSeq, normalization is implicitly handled by incorporating these per-sample size factors into the probability calculations.