The differential expression analysis seeks to find, for each cluster, features that are more highly expressed in that cluster relative to the rest of the sample. Here a differential expression test was performed between each cluster and the rest of the sample for each feature. The Log2 fold-change (L2FC) is an estimate of the log2 ratio of expression in a cluster to that in all other cells. A value of 1.0 indicates 2-fold greater expression in the cluster of interest. The p-value is a measure of the statistical significance of the expression difference and is based on a negative binomial test. The p-value reported here has been adjusted for multiple testing via the Benjamini-Hochberg procedure. In this table you can click on a column to sort by that value. Also, in this table features were filtered by (Mean UMI counts > 1.0) and the top N features by L2FC for each cluster were retained. Features with L2FC < 0 or adjusted p-value >= 0.10 were grayed out. The number of top features shown per cluster, N, is set to limit the number of table entries shown to 10,000; N=10,000/K^2 where K is the number of clusters. N can range from 1 to 50. For the full table, please refer to the 'differential_expression.csv' files produced by the pipeline.