Dawnn training set simulation

This project is a collection of files to allow users to reproduce the model development and benchmarking in "Dawnn: single-cell differential abundance with neural networks" (Hall and Castellano, under review). Dawnn is a tool for detecting differential abundance in single-cell RNAseq datasets. It is available as an R package here. Please contact us if you are unable to reproduce any of the analysis in our paper. The files in this collection correspond to the code and resulting dataset from the training set generation procedure. <br> FILES: <strong>autogen4_code.R</strong> R code to generate training set (250,000 independent random walks are simulated, with each random walk constituting an instance within the training set). Saves output to<em><strong> </strong></em><em>labels_df.csv</em>. <strong>labels_df.csv</strong> Training dataset generated by <em>autogen4_code.R</em> (Each row corresponds to a training instance, with the first column containing the simulated probability of the cell at the centre of a simulated trajectory having been drawn from <em>Condition_1</em>, and the remaining columns containing the labels corresponding to its 1000 neighbouring cells, with labels drawn according to the random walks simualted in <em>autogen4_code.R</em>).