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Given a features x cells matrix, perform one-vs-all differential tests to find markers.


marker_features(mat, groups, method = "wilcoxon")



IterableMatrix object of dimensions features x cells


Character/factor vector of cell groups/clusters. Length #cells


Test method to use. Current options are:

  • wilcoxon: Wilconxon rank-sum test a.k.a Mann-Whitney U test


tibble with the following columns:

  • foreground: Group ID used for the foreground

  • background: Group ID used for the background (or NA if comparing to rest of cells)

  • feature: ID of the feature

  • p_val_raw: Unadjusted p-value for differential test

  • foreground_mean: Average value in the foreground group

  • background_mean: Average value in the background group


Tips for using the values from this function:

  • Use dplyr::mutate() to add columns for e.g. adjusted p-value and log fold change.

  • Use dplyr::filter() to get only differential genes above some given threshold

  • To get adjusted p-values, use R p.adjust(), recommended method is "BH"

  • To get log2 fold change: if your input matrix was already log-transformed, calculate (foreground_mean - background_mean)/log(2). If your input matrix was not log-transformed, calculate log2(forground_mean/background_mean)