This GitHub repository accompanies the above named paper by Maughan et al., iScience 2022. It is made public to allow reproducibility of analysis. Gene expression analysis and visualisation is included; other experimental methods are described in the manuscript. It is assumed that a count matrix has been generated from raw sequencing files as described in the manuscript.
Analysis was run using R version 4.0.2. Package versions for libaries used in this analysis are included in this repository in the file package.versions.csv.
Included files are described below:
run.analysis.R - runs a full analysis to generate figures and results presented in the paper.
In the paper we present a method for identifying candidate marker genes for basal, secretory and ciliated cells. We do this as follows:
- parse_travaglini_cell_markers.R generates candidate lists of marker genes from single cell datasets, taken from supplementary table 4 of Travaglini et al 2020.
- filter_genes_lumgmap.R uses these genes applied to LUNGMAP data from different age groups to identify genes which co-correlate consistently in different age-groups (using the method generated by Danaher et al for immune deconvolution).
Other utility functions included are:
- geomean.R - calculates the geometric mean of a list of numbers.
- do.danaher.R - implements immune deconvolution as described in Danaher et al.
- rankOrderPlot.R - visualisation tool to highlight overrepresented genes