sc_morph_tracking

The script for article "Single-cell morphological tracking of cell states to identify small-molecule modulators of liver differentiation".

This was broken down into small chunks given the size of the single cell data at the start and was run on a supercomputer

01_subset.R

• format and merge timecourse and drug screening data

02_umap_subset.R

• Run PCA and UMAP on the combined dataframes to inspect single cell data for outliers

04_doublet_classifier.R

• Inspection of UMAP shows cluster of single cells that upon identifying in the images using X,Y coordinates appear to be "doublets" (where two nuclei have been segmented as one)
• Aggregation removes this issue but to use the single cell data these doublets ideally need to be removed from the dataset
• No single feature appears to delineate them well enough so a classifier could be trained to label them based on all features
• Train classifier to label doublets and write out a list of cells to be removed from the dataset

05_umap_predictions.R

• inspection of cell labels in UMAP space

06_remove_doublets_umap.R

• go back to the unnormalised data and remove the cells predicted to be doublets
• repeat standardisation and UMAP visualisation

07_outlier_rm.R

• one cluster of a handful of extreme outlier cells removed

08_umap.R

• finally repeat UMAP on cleaned dataset

09_explore.R

• create all the plots for publication

10_slingshot.R

• generate clusters for slingshot trajectories

11_slingshot_analysis.R

• run slingshot on timecourse data
• then add in drug data to get pseudotime values along the timecourse trajectories