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stemangiolastemangiola
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dropped pbmc_small from the package
1 parent cab11b7 commit 814516c

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-100
lines changed

‎R/data.R

-5
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@@ -1,8 +1,3 @@
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#' Example data set
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#'
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#'
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"pbmc_small"
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#' Example data set 2
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#'
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#'

‎R/dplyr_methods.R

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@@ -197,6 +197,7 @@ bind_cols.Seurat <- bind_cols_
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% distinct(groups)
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#'
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#' @rdname dplyr-methods
@@ -258,6 +259,7 @@ distinct.Seurat <- function (.data, ..., .keep_all = FALSE)
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% filter(groups == "g1")
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#'
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#' # Learn more in ?dplyr_eval
@@ -318,6 +320,7 @@ filter.Seurat <- function (.data, ..., .preserve = FALSE)
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#' @export
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% group_by(groups)
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#'
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NULL
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% summarise(mean(nCount_RNA))
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#'
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#' @export
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#'
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% mutate(nFeature_RNA = 1)
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#'
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NULL
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#' @export
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% rename(s_score = nFeature_RNA)
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#'
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NULL
@@ -592,9 +598,8 @@ rowwise.Seurat <- function(data, ...)
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#'
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#'
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#' tt = pbmc_small
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#' tt %>% left_join(tt %>% distinct(groups) %>% mutate(new_column = 1:2))
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#' data("pbmc_small")
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#' pbmc_small %>% left_join(pbmc_small %>% distinct(groups) %>% mutate(new_column = 1:2))
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#'
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NULL
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@@ -641,8 +646,8 @@ left_join.Seurat <- function (x, y, by = NULL, copy = FALSE, suffix = c(".x", ".
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#'
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#' tt = pbmc_small
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#' tt %>% inner_join(tt %>% distinct(groups) %>% mutate(new_column = 1:2) %>% slice(1))
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#' data("pbmc_small")
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#' pbmc_small %>% inner_join(pbmc_small %>% distinct(groups) %>% mutate(new_column = 1:2) %>% slice(1))
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#'
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#' @rdname dplyr-methods
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#' @name inner_join
@@ -692,8 +697,8 @@ inner_join.Seurat <- function (x, y, by = NULL, copy = FALSE, suffix = c(".x", "
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#'
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#' tt = pbmc_small
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#' tt %>% right_join(tt %>% distinct(groups) %>% mutate(new_column = 1:2) %>% slice(1))
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#' data("pbmc_small")
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#' pbmc_small %>% right_join(pbmc_small %>% distinct(groups) %>% mutate(new_column = 1:2) %>% slice(1))
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#'
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#' @rdname dplyr-methods
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#' @name right_join
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#'
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#' @examples
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#' `%>%` = magrittr::`%>%`
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#'
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#' tt = pbmc_small
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#' tt %>% full_join(tibble::tibble(groups = "g1", other=1:4))
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#' data("pbmc_small")
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#' pbmc_small %>% full_join(tibble::tibble(groups = "g1", other=1:4))
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#'
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#' @rdname dplyr-methods
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#' @name full_join
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% slice(1)
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NULL
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@@ -871,6 +876,7 @@ slice.Seurat <- function (.data, ..., .preserve = FALSE)
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% select(cell, orig.ident )
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#'
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#' @family single table verbs
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% sample_n(50)
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#' pbmc_small %>% sample_frac(0.1)
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#'
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% count(groups)
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#'
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#'
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#' @examples
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#'
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#' `%>%` = magrittr::`%>%`
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#' data("pbmc_small")
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#' pbmc_small %>% pull(groups)
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#'
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NULL

‎R/methods.R

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@@ -77,8 +77,8 @@ setMethod(
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#'
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#' @examples
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#'
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#' data("pbmc_small")
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#' pbmc_small %>%
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#'
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#' join_features(features = c("HLA-DRA", "LYZ"))
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#'
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#'

‎R/print_method.R

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#'
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#' @examples
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#' library(dplyr)
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#' data("pbmc_small")
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#' pbmc_small %>% print()
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#' @name print
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NULL

‎R/tibble_methods.R

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@@ -57,6 +57,7 @@
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#'
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#' @export
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#' @examples
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#' data("pbmc_small")
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#' pbmc_small %>% as_tibble()
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NULL
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@@ -133,6 +134,7 @@ as_tibble.Seurat = function(x, ...,
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#'
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#' @export
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#' @examples
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#' data("pbmc_small")
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#' pbmc_small %>% tidy %>% glimpse()
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#'
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#'

‎R/tidyr_methods.R

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#' @examples
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#'
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#' library(dplyr)
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#' data("pbmc_small")
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#' pbmc_small %>% nest(data = -groups) %>% unnest(data)
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#'
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#'
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#' @examples
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#'
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#' library(dplyr)
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#' data("pbmc_small")
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#' pbmc_small %>% nest(data = -groups) %>% unnest(data)
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#'
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#' @rdname tidyr-methods
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#' @export
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#' @examples
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#'
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#' data("pbmc_small")
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#' pbmc_small %>% extract(groups, into = "g", regex = "g([0-9])", convert = TRUE)
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#' @return A Seurat object or a tibble depending on input
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#' # See vignette("pivot") for examples and explanation
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#'
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#' library(dplyr)
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#' data("pbmc_small")
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#' pbmc_small %>% pivot_longer(c(orig.ident, groups), names_to = "name", values_to = "value")
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#'
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#' @export
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#' @examples
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#'
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#' data("pbmc_small")
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#' pbmc_small %>% unite("new_col", c(orig.ident, groups))
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#'
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#'
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#' @export
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#' @examples
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#'
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#' data("pbmc_small")
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#' un = pbmc_small %>% unite("new_col", c(orig.ident, groups))
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#' un %>% separate(col = new_col, into= c("orig.ident", "groups"))
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#'

‎data/pbmc_small.rda

-52.2 KB
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‎man/count.Rd

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‎man/dplyr-methods.Rd

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‎man/fragments/intro.Rmd

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@@ -71,7 +71,7 @@ library(tidyseurat)
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This is a seurat object but it is evaluated as tibble. So it is fully compatible both with Seurat and tidyverse APIs.
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```{r}
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pbmc_small <- tidyseurat::pbmc_small
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data("pbmc_small")
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```
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**It looks like a tibble**
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pbmc_small@assays
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```
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# Annotation polishing
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We may have a column that contains the directory each run was taken from, such as the "file" column in `pbmc_small`.
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```{r}
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pbmc_small$file[1:5]
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```
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We may want to extract the run/sample name out of it into a separate column. Tidyverse `extract` can be used to convert a character column into multiple columns using regular expression groups.
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```{r}
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# Create sample column
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pbmc_small_polished <-
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pbmc_small %>%
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tidyseurat::extract(file, "sample", "../data/([a-z0-9]+)/outs.+", remove = FALSE)
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# Reorder to have sample column up front
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pbmc_small_polished %>%
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select(sample, everything())
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```
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# Preliminary plots
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Set colours and theme for plots.
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)
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```
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We can treat `pbmc_small_polished` effectively as a normal tibble for plotting.
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We can treat `pbmc_small` effectively as a normal tibble for plotting.
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Here we plot number of features per cell.
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```{r plot1}
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pbmc_small_polished %>%
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pbmc_small %>%
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tidyseurat::ggplot(aes(nFeature_RNA, fill = groups)) +
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geom_histogram() +
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my_theme
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Here we plot total features per cell.
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```{r plot2}
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pbmc_small_polished %>%
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pbmc_small %>%
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tidyseurat::ggplot(aes(groups, nCount_RNA, fill = groups)) +
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geom_boxplot(outlier.shape = NA) +
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geom_jitter(width = 0.1) +
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Here we plot abundance of two features for each group.
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```{r}
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pbmc_small_polished %>%
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pbmc_small %>%
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join_features(features = c("HLA-DRA", "LYZ")) %>%
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ggplot(aes(groups, abundance_RNA + 1, fill = groups)) +
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geom_boxplot(outlier.shape = NA) +
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```{r preprocess}
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pbmc_small_pca <-
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pbmc_small_polished %>%
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pbmc_small %>%
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SCTransform(verbose = FALSE) %>%
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FindVariableFeatures(verbose = FALSE) %>%
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RunPCA(verbose = FALSE)

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