Dawnn training set simulation

<p>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 <a href="https://github.com/george-hall-ucl/dawnn" target="_blank">here</a>. Please contact us if you are unable to reproduce any of the analysis in our paper.</p> <p>The files in this collection correspond to the code and resulting dataset from the training set generation procedure.</p> <p><br></p> <p>FILES:</p> <ul> <li><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>.</li> <li><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>).</li> </ul>