Help

Uploading datasets

upload screen
Upload screen

To upload a new dataset, select a data file containing a gene expression matrix. This file can be a sparse matrix file in Matrix Market format (.mtx, as produced by scipy.io.mmwrite) or a dense matrix as a space or tab-delimited file (as produced by np.savetxt). The file should not contain any headers. Make sure to indicate whether the file is genes-by-cells or cells-by-genes.

Running uncurl

Data preview
Data preview

After updating the data, you will eventually be redirected to a view that looks like the one above. On the top, there are two plots. The plot on the left shows the distribution of total read counts per cell. The plot on the right shows the relationship between mean and variance for all genes.

Uncurl options

Uncurl parameters
Uncurl parameters

User interface

Scatterplot + barplot
Visualization: scatterplot of means + barplot

After Uncurl has finished running, you will be redirected to a page that looks like the one above. The graph on the left is a scatterplot that shows a dimensionally reduced view of the cells or cluster means. The graph on the right shows the top genes for the selected cluster, or relationships between clusters.

To change the scatterplot view, click on the radio buttons above the plot, circled in red. The default view shows the cluster means. The "Processed cells" option shows a scatterplot with all cells.

To change the cluster being shown on the barplot, click on any cell or cluster mean.

Scatterplot + barplot
Visualization: scatterplot of all cells + barplot

To change the color scheme, use the "Label scheme" dropdown. This also allows you to upload a new color scheme for visualization.

Interacting with the plot

Double click on a cluster name to see only that cluster. Single click on a cluster name to toggle its visibility.

Mousing over the top right of the plot will show a control panel. This allows you to zoom in/out, select cells, or save the plot.

Gene set databases

UNCURL-App currently contains interfaces to three gene set databases: Enrichr, CellMarker, and CellMeSH. These databases can be used to aid in identifying cell types corresponding to clusters.

Enrichr

This is an interface to the Enrichr tool (http://amp.pharm.mssm.edu/Enrichr/). This does not include all gene set libraries present in Enrichr, just the ones that might be helpful in identifying cell types.

CellMarker

CellMeSH

New cell labels

New labels for each cell can be uploaded by selecting "New color track" from the "Label scheme" dropdown.

Merging, splitting, re-analysis

Using the scatterplot, the user can merge or split existing clusters, or create a new cluster from selected cells.