removed tibble dependency (7a6dd7d5) · Commits · github_fork / Presto 20250701

tests/testthat/test_correctness.R

+3 −2

Original line number	Original line	Diff line number	Diff line
	@@ -19,8 +19,9 @@ test_that('presto::wilcoxauc gives same results as stats::wilcox.test', {
	return(res)		return(res)
	})		})
	res_base_r <- Reduce(rbind, lapply(names(res_base_r), function(group) {		res_base_r <- Reduce(rbind, lapply(names(res_base_r), function(group) {
	res_base_r[[group]] %>% data.frame() %>%		.res <- res_base_r[[group]] %>% data.frame()
	tibble::rownames_to_column('feature') %>%		.res$feature <- row.names(.res)
			.res <- .res %>%
	dplyr::mutate(group = group)		dplyr::mutate(group = group)
	})) %>%		})) %>%
	dplyr::select(feature, group, statistic, p.value)		dplyr::select(feature, group, statistic, p.value)

+6 −5

Original line number	Original line	Diff line number	Diff line
	@@ -86,7 +86,8 @@ head(wilcoxauc(object_seurat, 'cell_type'))

	```		```

	Seurat supports multiple assay types. This can be specified with `seurat_assay`.		Seurat supports multiple assay types. This can be specified with
			`seurat_assay`.

	```{r}		```{r}
	head(wilcoxauc(object_seurat, 'cell_type', seurat_assay = 'RNA'))		head(wilcoxauc(object_seurat, 'cell_type', seurat_assay = 'RNA'))
	@@ -155,8 +156,8 @@ head(wilcoxauc(exprs, y))

	## Top markers		## Top markers

	We often find it helpful to summarize what the most distinguishing features are		We often find it helpful to summarize what the most distinguishing features
	in group.		are in each group.

	```{r}		```{r}
	res <- wilcoxauc(exprs, y)		res <- wilcoxauc(exprs, y)
	@@ -196,8 +197,8 @@ head(wilcoxauc(exprs_sparse, y))
	## groups_use		## groups_use

	Sometimes, you don't want to test all groups in the dataset against all other		Sometimes, you don't want to test all groups in the dataset against all other
	groups. For instance, I want to compare only observations in group 'A' to those		groups. For instance, I want to compare only observations in group 'A' to
	in group 'B'. This is achieved with the groups_use argument.		those in group 'B'. This is achieved with the groups_use argument.

	```{r}		```{r}
	res_AB <- wilcoxauc(exprs, y, groups_use = c('A', 'B'))		res_AB <- wilcoxauc(exprs, y, groups_use = c('A', 'B'))