Non-parametric differential expression test for sparse non-negative data
Non-parametric differential expression test for sparse non-negative data
diff_mean_test( y, labels, R = 99, log2FC_th = log2(1.2), mean_th = 0.05, cells_th = 5, only_pos = FALSE, only_top_n = NULL, mean_type = "geometric", verbosity = 1 )
y |
A matrix of counts; must be (or inherit from) class dgCMatrix; genes are row, cells are columns |
labels |
A factor giving the group labels; must have exactly two levels |
R |
The number of random permutations used to derive the p-values; default is 99 |
log2FC_th |
Threshold to remove genes from testing; absolute log2FC must be at least
this large for a gene to be tested; default is |
mean_th |
Threshold to remove genes from testing; gene mean must be at least this large for a gene to be tested; default is 0.05 |
cells_th |
Threshold to remove genes from testing; gene must be detected (non-zero count) in at least this many cells in the group with higher mean; default is 5 |
only_pos |
Test only genes with positive fold change (mean in group 1 > mean in group2); default is FALSE |
only_top_n |
Test only the this number of genes from both ends of the log2FC spectrum after all of the above filters have been applied; useful to get only the top markers; only used if set to a numeric value; default is NULL |
mean_type |
Which type of mean to use; if |
verbosity |
Integer controlling how many messages the function prints; 0 is silent, 1 (default) is not |
Data frame of results
This model-free test is applied to each gene (row) individually but is optimized to make use of the efficient sparse data representation of the input. A permutation null distribution us used to assess the significance of the observed difference in mean between two groups.
The observed difference in mean is compared against a distribution
obtained by random shuffling of the group labels. For each gene every
random permutation yields a difference in mean and from the population of
these background differences we estimate a mean and standard
deviation for the null distribution.
This mean and standard deviation are used to turn the observed
difference in mean into a z-score and then into a p-value. Finally,
all p-values (for the tested genes) are adjusted using the Benjamini & Hochberg
method (fdr). The log2FC values in the output are log2(mean1 / mean2).
Empirical p-values are also calculated: emp_pval = (b + 1) / (R + 1)
where b is the number of times the absolute difference in mean from a random
permutation is at least as large as the absolute value of the observed difference
in mean, R is the number of random permutations. This is an upper bound of
the real empirical p-value that would be obtained by enumerating all possible
group label permutations.
clustering <- 1:ncol(pbmc) %% 2 vst_out <- vst(pbmc, return_corrected_umi = TRUE) de_res <- diff_mean_test(y = vst_out$umi_corrected, labels = clustering)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.