This notebook is an example of how to use the Jupyter interactive widget, Clustergrammer-widget, to visualize single cell RNAseq data from the publication
"Single-cell analysis of mixed-lineage states leading to a binary cell fate choice" Olsson A, Venkatasubramanian M, et.al. Nature August 9th 2016 link-to-paper
The figure below is a slightly adapted version of Figure 2A made fromt the available source data Excel file.
# import the widget from clustergrammer_widget import * from copy import deepcopy # load data into new network instance and cluster net = deepcopy(Network()) net.load_file('single_cell_RNAseq_Aronow.txt') net.make_clust() # view the results as a widget clustergrammer_widget(network = net.export_net_json())
The above heatmap shows single-cell-clusters (see publication for details) as columns and differentially expressed genes as rows. Clustergrammer uses hierarchical clustering to identify clusters of cells and genes. We can see that single-cell-clusters largely cluster according to their categories (e.g. Gfi1-high).