scOrange Workflows

Multi-Sample loader

Load Data widget can load the from tab or comma delimited text files or annotated 10x Genomics data files. Drag-and-drop to create a list of files to load. Use cell or gene sampling, if required. The loader creates a single data set and marks the cells according to the data source.

Tags: Data

Regressing out batch effects

In this workflow, we show how to calculate cell-cycle phase scores based on canonical markers, and regressing these out of the data during pre-processing. The data used in this examples consists of murine hematopoietic progenitors.

Tags: Cell-cycle

Markers and subpopulations

Load data and a list of marker genes from our database. Select marker genes of choice. Score cells according to the expression of selected markers, and reveal marker-specific cells in the t-SNE cell landscape. Simultaneously open both Data Table (marker selection) and t-SNE visualization to turn scOrange into an interactive gene marker browser.

Data set preprocessing and alignment

We align single-cell data from two different experimental conditions and recover shared subpopulations, thus removing batch effects. We use the data from Kang et al. (2018), where authors stimulate the peripheral blood mononuclear cells (PMBCs) with interferon-alpha and compare them to a control sample. Dataset alignment reduces treatment-specific variation that may occlude the differences among cell subpopulations shared between the two data sets.

Tags: Dataset