Tutorials#

To get started, we recommend following our tutorials.

Note

For questions about using Pycallingcards or broader questions about modeling data, please use our raise commit.

Single-cell calling cards data without background#

Tutorial: Analyzing single cell calling cards collected from the mouse cortex

This is a brd4 single-cell(sc) calling cards dataset in mouse cortex from Moudgil et al., Cell. (2020), and it can be downloaded from GEO.

In this tutorial, we will call peaks, make annotation, do differential peak analysis, and pair peaks with genes.

Single-cell calling cards data with background#

Tutorial: K562 HCT116 SP1 single-cell calling cards data.

In this data, we test transcription factor SP1 bindings in cell lines K562 and HCT116 by single-cell(sc) calling cards techenology. The data is from Moudgil et al., Cell. (2020), and it can be downloaded from GEO.

In this tutorial, we will call peaks with backgound, make annotation, compare peaks with Chip-seq reference data and do differential peak analysis.

Bulk calling cards data without background#

Tutorial: Brd4 bindings for Male/Female mouse bulk calling cards data.

This is a brd4 bulk callingcards dataset in mouse cortex from Kfoury et al., PNAS. (2021), and it can be downloaded from GEO.

In this tutorial, we will call peaks, make annotation, do differential peak analysis, and pair bindings with gene expression.

Bulk calling cards data with background#

Tutorial: SP1 bindings in Cre-driver mouse lines.

This is a transcription factor SP1 binding bulk calling cards data in cre-driver mouseline and bulk brd4 data is also sequenced as backgound. This data contain two time points: day 10(P10) and day 28(P28). The data are from Cammack et al., PNAS. (2020), and it can be downloaded from GEO.

In this tutorial, we will call peaks, make annotation, and do differential peak analysis.

Yeast calling cards data#

Tutorial: Yeast TYE7 bulk calling cards data

This is a transcription factor TYE7 bindings in yeast by single-cell(sc) calling cards techenology. The data is from Shively CA, PNAS. (2020), and it can be download from GEO.

In this tutorial, we will call peaks, make annotation, and do differential peak analysis.