Filtering options

Filter cells based on total UMI count.
Filter cells based on number of genes detected.
Filter genes based on number of cells where it is expressed.

Mitochodrial RNA Options

Enter a regular expression to define the subset of mitochondrial. (e.g. ^mt- for mouse or ^MT- for human. Leave blank to skip this analysis.
Filter cells based on percentage of mitochondrial RNA.

Sampling options

Randomly sample a subset of cells passing filters. This allows for quicker exploration, before using the full dataset.
Plot showing total UMI per barcode. Barcodes selected for further analysis are displayed in green.
Violin plots showing distributions of total UMI per cell, and number of detected genes per cell.

Filtering summary:


Histogram of mean gene expression across all cells.

Variable Genes Options

Identify features that are outliers on a 'mean variability plot'.

PCA Options

Select the set of genes to use and the number of principal components to compute.

Clustering Options

t-SNE Options

Differential Expression Options

Select options for marker gene discovery.
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals.
Only test genes that are detected in a minimum fraction of cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed.
Heatmap showing gene expression for the top markers in each cluster.
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