Description Usage Arguments Value Details Examples

View source: R/differential_expression.R

Non-parametric differential expression test for sparse non-negative data

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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.

1 2 3 |

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