R/rename-content.R
In theMILOlab/cypro: A Toolbox for Analysis of Single Cell Microscopy Data
Defines functions
rename_analysis_slots_with
rename_analysis_slots
renameTracksDfWith
renameTracksDf
renameStatsDfWith
renameStatsDf
renameClusterDfWith
renameClusterDf
renameMetaDfWith
renameMetaDf
renameGroups
Documented in
renameClusterDf
renameClusterDfWith
renameGroups
renameMetaDf
renameMetaDfWith
renameStatsDf
renameStatsDfWith
renameTracksDf
renameTracksDfWith
#' @title Rename groups
#'
#' @description Allows to rename particular groups within a grouping variable
#' of the cypro object's cell data.
#'
#' @inherit argument_dummy params
#' @param grouping_variable Character value. The name of the grouping variable
#' whose groups you want to rename.
#' @param ... The groups to be renamed specified according to the following
#' syntax: \emph{'new_cluster_name'} \code{=} \emph{'old_cluster_name'}.
#'
#' @details Renaming groups of variables \emph{well_plate_name, well_plate_index, well}
#' and \emph{well_image} is not allowed and will result in an error.
#'
#' Use \code{getGroupNames()} to check if renaming resulted in the desired output.
#'
#' @inherit updated_object return
#' @export
#'
renameGroups <- function(object, grouping_variable = NULL, ..., phase = NULL){
check_object(object)
assign_default(object)
phase <- check_phase(object, phase, max_phases = 1)
all_grouping_vars <-
getGroupingVariableNames(object, named = TRUE, phase = phase, verbose = FALSE)
valid_grouping_vars <-
all_grouping_vars[!all_grouping_vars %in% well_plate_vars]
confuns::check_one_of(
input = grouping_variable,
against = valid_grouping_vars
)
grouping_var <- valid_grouping_vars[valid_grouping_vars == grouping_variable]
slot <- base::names(grouping_var)
rename_input <- confuns::keep_named(c(...))
if(base::length(rename_input) == 0){
msg <- ct_warnings$how_to_name_input
confuns::give_feedback(
msg = msg,
fdb.fn = "stop"
)
}
df <- getCellDf(object, slot = slot, phase = phase)
valid_rename_input <-
confuns::check_vector(
input = base::unname(rename_input),
against = base::levels(df[[grouping_variable]]),
fdb.fn = "warning",
ref.input = "groups to rename",
ref.against = glue::glue("all groups of variable '{grouping_variable}'. ({ct_warnings$how_to_name_input})")
)
rename_input <- rename_input[rename_input %in% valid_rename_input]
# rename cluster
renamed_df <-
dplyr::mutate(
.data = df,
{{grouping_variable}} := forcats::fct_recode(.f = !!rlang::sym(grouping_variable), !!!rename_input)
)
object <- setCellDf(object, slot = slot, df = renamed_df, phase = phase)
base::return(object)
}
#' @title Rename meta variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell meta
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit argument_dummy params
#' @param ... Input for function \code{dplyr::rename_with()}. This includes the arguments
#' \code{.fn} and \code{.cols}.
#'
#' @details Use \code{getGroupingVariableNames()} to check if renaming resulted in
#' the desired output.
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @seealso \code{dplyr::rename()}, \code{dplyr::rename_with()}
#'
#' @inherit updated_object return
#' @export
#'
renameMetaDf <- function(object, ..., phase = NULL){
check_object(object)
assign_default(object)
core_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(cell_id, cell_line, condition)
renamed_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(-cell_line, -condition, -cell_id) %>%
dplyr::rename(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "meta", df = final_df, phase = phase)
base::return(object)
}
#' @rdname renameMetaDf
#' @export
renameMetaDfWith <- function(object, ..., phase = NULL){
check_object(object)
assign_default(object)
core_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(cell_id, cell_line, condition)
renamed_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(-cell_line, -condition, -cell_id) %>%
dplyr::rename_with(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "meta", df = final_df, phase = phase)
base::return(object)
}
#' @title Rename cluster variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell cluster
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit renameMetaDf params details return
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @seealso \code{dplyr::rename()}, \code{dplyr::rename_with()}
#'
#' @export
#'
renameClusterDf <- function(object, phase = NULL, ...){
check_object(object)
assign_default(object)
core_df <-
getClusterDf(object, phase = phase) %>%
dplyr::select(cell_id)
renamed_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(-cell_line, -condition, -cell_id) %>%
dplyr::rename(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "cluster", df = final_df, phase = phase)
base::return(object)
}
#' @rdname renameClusterDf
#' @export
renameClusterDfWith <- function(object, phase = NULL, ...){
check_object(object)
assign_default(object)
core_df <-
getClusterDf(object, phase = phase) %>%
dplyr::select(cell_id)
renamed_df <-
getClusterDf(object, phase = phase) %>%
dplyr::select(-cell_id) %>%
dplyr::rename_with(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "cluster", df = final_df, phase = phase)
base::return(object)
}
#' @title Rename statistic variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell statistic
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit renameMetaDf params return
#'
#' @details Renaming the statistic data.frame affects all slots of the cypro object that refer to
#' statistic variables (correlation analysis, variable sets etc.). All are renamed according to
#' the input.
#'
#' Use \code{getStatVariableNames()} to check if renaming resulted in
#' the desired output.
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @export
renameStatsDf <- function(object, ...){
check_object(object, exp_type_req = "time_lapse")
# rename stat_df
if(multiplePhases(object)){
object@cdata$stats <-
purrr::map(.x = object@cdata$stats, .f = function(stat_df){
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(renamed_df)
})
} else {
stat_df <- getStatsDf(object, with_grouping = FALSE)
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$stats <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets, .f = function(vset){
renamed_vset <- base::tryCatch({
confuns::vredefine(input = vset, ...)
}, error = function(error){
NA
})
if(!base::is.character(renamed_vset)){
base::return(vset)
} else {
base::return(renamed_vset)
}
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary,
.f = function(summary_df){
summary_df$variable <-
confuns::vredefine(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots(object, ...)
base::return(object)
}
#' @rdname renameStatsDf
#' @export
renameStatsDfWith <- function(object, ...){
check_object(object, exp_type_req = "time_lapse")
# rename stat_df
if(multiplePhases(object)){
object@cdata$stats <-
purrr::map(.x = object@cdata$stats,
.f = function(stat_df){
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(cell_id_var)
})
} else {
stat_df <-
getStatsDf(object, phase = phase, with_cluster = F, with_meta = F, with_well_plate = F)
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$stats <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets,
.f = function(vset){
confuns::vredefine_with(input = vset, ...)
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary,
.f = function(summary_df){
summary_df$variable <-
confuns::vredefine_with(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine_with(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots_with(object = object, ...)
base::return(object)
}
#' @title Rename track variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell track
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit renameMetaDf params return
#'
#' @details Renaming the tracks data.frame affects all slots of the cypro object that refer to
#' track variables (correlation analysis, variable sets etc.). All are renamed according to
#' the input.
#'
#' Use \code{getTrackVariableNames()} to check if renaming resulted in
#' the desired output.
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @export
renameTracksDf <- function(object, ...){
check_object(object)
# rename stat_df
if(multiplePhases(object)){
object@cdata$tracks <-
purrr::map(.x = object@cdata$tracks, .f = function(stat_df){
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(renamed_df)
})
} else {
track_df <-
getTracksDf(
object = object,
with_grouping = FALSE
)
cell_id_var <- track_df$cell_id
renamed_df <-
dplyr::select(track_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$tracks <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets, .f = function(vset){
renamed_vset <- base::tryCatch({
confuns::vredefine(input = vset, ...)
}, error = function(error){
NA
})
if(!base::is.character(renamed_vset)){
base::return(vset)
} else {
base::return(renamed_vset)
}
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary, .f = function(summary_df){
summary_df$variable <-
confuns::vredefine(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots(object)
return(object)
}
#' @rdname renameTracksDf
#' @export
renameTracksDfWith <- function(object, ...){
check_object(object)
# rename stat_df
if(multiplePhases(object)){
object@cdata$tracks <-
purrr::map(.x = object@cdata$tracks, .f = function(track_df){
cell_id_var <- track_df$cell_id
renamed_df <-
dplyr::select(track_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(cell_id_var)
})
} else {
track_df <-
getTracksDf(
object = object,
with_grouping = FALSE
)
cell_id_var <- track_df$cell_id
renamed_df <-
dplyr::select(track_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$tracks <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets, .f = function(vset){
confuns::vredefine_with(input = vset, ...)
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary, .f = function(summary_df){
summary_df$variable <-
confuns::vredefine_with(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine_with(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots_with(object = object, ...)
base::return(object)
}
# NOT EXPORTED ------------------------------------------------------------
rename_analysis_slots <- function(object, ...){
# rename correlation
if(multiplePhases(object)){
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = confuns::rename_numeric_vars,
rename.data = FALSE,
...)
base::return(vset_by_phases_renamed)
})
} else {
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = confuns::rename_numeric_vars,
rename.data = FALSE,
...)
}
# rename clustering
if(multiplePhases(object)){
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine(input = conv_object@variables, ...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine(input = conv_object@variables, ...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
# rename dim red
if(multiplePhases(object)){
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine(input = conv_object@variables_num, ...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine(input = conv_object@variables_num, ...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
return(object)
}
rename_analysis_slots_with <- function(object, ...){
# rename correlation
if(multiplePhases(object)){
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = confuns::rename_numeric_vars_with,
rename.data = FALSE,
...)
base::return(vset_by_phases_renamed)
})
} else {
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = confuns::rename_numeric_vars_with,
rename.data = FALSE,
...)
}
# rename clustering
if(multiplePhases(object)){
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine_with(
input = conv_object@variables,
...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine_with(
input = conv_object@variables,
...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
# rename dim red
if(multiplePhases(object)){
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine_with(
input = conv_object@variables_num,
...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine_with(
input = conv_object@variables_num,
...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
return(object)
}
theMILOlab/cypro documentation built on April 5, 2022, 2:03 a.m.
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Defines functions rename_analysis_slots_with rename_analysis_slots renameTracksDfWith renameTracksDf renameStatsDfWith renameStatsDf renameClusterDfWith renameClusterDf renameMetaDfWith renameMetaDf renameGroups
Documented in renameClusterDf renameClusterDfWith renameGroups renameMetaDf renameMetaDfWith renameStatsDf renameStatsDfWith renameTracksDf renameTracksDfWith
#' @title Rename groups
#'
#' @description Allows to rename particular groups within a grouping variable
#' of the cypro object's cell data.
#'
#' @inherit argument_dummy params
#' @param grouping_variable Character value. The name of the grouping variable
#' whose groups you want to rename.
#' @param ... The groups to be renamed specified according to the following
#' syntax: \emph{'new_cluster_name'} \code{=} \emph{'old_cluster_name'}.
#'
#' @details Renaming groups of variables \emph{well_plate_name, well_plate_index, well}
#' and \emph{well_image} is not allowed and will result in an error.
#'
#' Use \code{getGroupNames()} to check if renaming resulted in the desired output.
#'
#' @inherit updated_object return
#' @export
#'
renameGroups <- function(object, grouping_variable = NULL, ..., phase = NULL){
check_object(object)
assign_default(object)
phase <- check_phase(object, phase, max_phases = 1)
all_grouping_vars <-
getGroupingVariableNames(object, named = TRUE, phase = phase, verbose = FALSE)
valid_grouping_vars <-
all_grouping_vars[!all_grouping_vars %in% well_plate_vars]
confuns::check_one_of(
input = grouping_variable,
against = valid_grouping_vars
)
grouping_var <- valid_grouping_vars[valid_grouping_vars == grouping_variable]
slot <- base::names(grouping_var)
rename_input <- confuns::keep_named(c(...))
if(base::length(rename_input) == 0){
msg <- ct_warnings$how_to_name_input
confuns::give_feedback(
msg = msg,
fdb.fn = "stop"
)
}
df <- getCellDf(object, slot = slot, phase = phase)
valid_rename_input <-
confuns::check_vector(
input = base::unname(rename_input),
against = base::levels(df[[grouping_variable]]),
fdb.fn = "warning",
ref.input = "groups to rename",
ref.against = glue::glue("all groups of variable '{grouping_variable}'. ({ct_warnings$how_to_name_input})")
)
rename_input <- rename_input[rename_input %in% valid_rename_input]
# rename cluster
renamed_df <-
dplyr::mutate(
.data = df,
{{grouping_variable}} := forcats::fct_recode(.f = !!rlang::sym(grouping_variable), !!!rename_input)
)
object <- setCellDf(object, slot = slot, df = renamed_df, phase = phase)
base::return(object)
}
#' @title Rename meta variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell meta
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit argument_dummy params
#' @param ... Input for function \code{dplyr::rename_with()}. This includes the arguments
#' \code{.fn} and \code{.cols}.
#'
#' @details Use \code{getGroupingVariableNames()} to check if renaming resulted in
#' the desired output.
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @seealso \code{dplyr::rename()}, \code{dplyr::rename_with()}
#'
#' @inherit updated_object return
#' @export
#'
renameMetaDf <- function(object, ..., phase = NULL){
check_object(object)
assign_default(object)
core_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(cell_id, cell_line, condition)
renamed_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(-cell_line, -condition, -cell_id) %>%
dplyr::rename(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "meta", df = final_df, phase = phase)
base::return(object)
}
#' @rdname renameMetaDf
#' @export
renameMetaDfWith <- function(object, ..., phase = NULL){
check_object(object)
assign_default(object)
core_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(cell_id, cell_line, condition)
renamed_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(-cell_line, -condition, -cell_id) %>%
dplyr::rename_with(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "meta", df = final_df, phase = phase)
base::return(object)
}
#' @title Rename cluster variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell cluster
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit renameMetaDf params details return
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @seealso \code{dplyr::rename()}, \code{dplyr::rename_with()}
#'
#' @export
#'
renameClusterDf <- function(object, phase = NULL, ...){
check_object(object)
assign_default(object)
core_df <-
getClusterDf(object, phase = phase) %>%
dplyr::select(cell_id)
renamed_df <-
getMetaDf(object, phase = phase) %>%
dplyr::select(-cell_line, -condition, -cell_id) %>%
dplyr::rename(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "cluster", df = final_df, phase = phase)
base::return(object)
}
#' @rdname renameClusterDf
#' @export
renameClusterDfWith <- function(object, phase = NULL, ...){
check_object(object)
assign_default(object)
core_df <-
getClusterDf(object, phase = phase) %>%
dplyr::select(cell_id)
renamed_df <-
getClusterDf(object, phase = phase) %>%
dplyr::select(-cell_id) %>%
dplyr::rename_with(...)
check_renamed_variables(base::colnames(renamed_df))
final_df <- base::cbind(core_df, renamed_df) %>% tibble::as_tibble()
object <- setCellDf(object, slot = "cluster", df = final_df, phase = phase)
base::return(object)
}
#' @title Rename statistic variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell statistic
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit renameMetaDf params return
#'
#' @details Renaming the statistic data.frame affects all slots of the cypro object that refer to
#' statistic variables (correlation analysis, variable sets etc.). All are renamed according to
#' the input.
#'
#' Use \code{getStatVariableNames()} to check if renaming resulted in
#' the desired output.
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @export
renameStatsDf <- function(object, ...){
check_object(object, exp_type_req = "time_lapse")
# rename stat_df
if(multiplePhases(object)){
object@cdata$stats <-
purrr::map(.x = object@cdata$stats, .f = function(stat_df){
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(renamed_df)
})
} else {
stat_df <- getStatsDf(object, with_grouping = FALSE)
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$stats <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets, .f = function(vset){
renamed_vset <- base::tryCatch({
confuns::vredefine(input = vset, ...)
}, error = function(error){
NA
})
if(!base::is.character(renamed_vset)){
base::return(vset)
} else {
base::return(renamed_vset)
}
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary,
.f = function(summary_df){
summary_df$variable <-
confuns::vredefine(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots(object, ...)
base::return(object)
}
#' @rdname renameStatsDf
#' @export
renameStatsDfWith <- function(object, ...){
check_object(object, exp_type_req = "time_lapse")
# rename stat_df
if(multiplePhases(object)){
object@cdata$stats <-
purrr::map(.x = object@cdata$stats,
.f = function(stat_df){
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(cell_id_var)
})
} else {
stat_df <-
getStatsDf(object, phase = phase, with_cluster = F, with_meta = F, with_well_plate = F)
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$stats <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets,
.f = function(vset){
confuns::vredefine_with(input = vset, ...)
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary,
.f = function(summary_df){
summary_df$variable <-
confuns::vredefine_with(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine_with(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots_with(object = object, ...)
base::return(object)
}
#' @title Rename track variables
#'
#' @description Implementations of the functions \code{dplyr::rename()} and
#' \code{dplyr::rename_with()} that can be used to rename the variables of the cell track
#' data.frame.
#'
#' \code{rename*Df()} changes the names of individual variables using \emph{new_name} = \emph{old_name}
#' syntax. \code{rename*DfWith()} renames variables with a function specified in argument \code{.fn}.
#'
#' @inherit renameMetaDf params return
#'
#' @details Renaming the tracks data.frame affects all slots of the cypro object that refer to
#' track variables (correlation analysis, variable sets etc.). All are renamed according to
#' the input.
#'
#' Use \code{getTrackVariableNames()} to check if renaming resulted in
#' the desired output.
#'
#' @note Make sure not to rename protected variables. Use the function \code{protectedVariables()}
#' to obtain variable names that must not be changed and must not be added. Doing
#' so will result in erroneous analysis results as many functions rely on these
#' variables to carry specific meaning.
#'
#' @export
renameTracksDf <- function(object, ...){
check_object(object)
# rename stat_df
if(multiplePhases(object)){
object@cdata$tracks <-
purrr::map(.x = object@cdata$tracks, .f = function(stat_df){
cell_id_var <- stat_df$cell_id
renamed_df <-
dplyr::select(stat_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(renamed_df)
})
} else {
track_df <-
getTracksDf(
object = object,
with_grouping = FALSE
)
cell_id_var <- track_df$cell_id
renamed_df <-
dplyr::select(track_df, -cell_id) %>%
dplyr::rename(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$tracks <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets, .f = function(vset){
renamed_vset <- base::tryCatch({
confuns::vredefine(input = vset, ...)
}, error = function(error){
NA
})
if(!base::is.character(renamed_vset)){
base::return(vset)
} else {
base::return(renamed_vset)
}
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary, .f = function(summary_df){
summary_df$variable <-
confuns::vredefine(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots(object)
return(object)
}
#' @rdname renameTracksDf
#' @export
renameTracksDfWith <- function(object, ...){
check_object(object)
# rename stat_df
if(multiplePhases(object)){
object@cdata$tracks <-
purrr::map(.x = object@cdata$tracks, .f = function(track_df){
cell_id_var <- track_df$cell_id
renamed_df <-
dplyr::select(track_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
base::return(cell_id_var)
})
} else {
track_df <-
getTracksDf(
object = object,
with_grouping = FALSE
)
cell_id_var <- track_df$cell_id
renamed_df <-
dplyr::select(track_df, -cell_id) %>%
dplyr::rename_with(...) %>%
dplyr::mutate(cell_id = {{cell_id_var}}) %>%
dplyr::select(cell_id, dplyr::everything())
object@cdata$tracks <- renamed_df
}
# rename variable sets
object@variable_sets <-
purrr::map(.x = object@variable_sets, .f = function(vset){
confuns::vredefine_with(input = vset, ...)
})
# rename variables
if(multiplePhases(object)){
object@vdata$summary <-
purrr::map(.x = object@vdata$summary, .f = function(summary_df){
summary_df$variable <-
confuns::vredefine_with(input = summary_df$variable, ...)
base::return(summary_df)
})
} else {
object@vdata$summary$variable <-
confuns::vredefine_with(input = object@vdata$summary$variable, ...)
}
object <- rename_analysis_slots_with(object = object, ...)
base::return(object)
}
# NOT EXPORTED ------------------------------------------------------------
rename_analysis_slots <- function(object, ...){
# rename correlation
if(multiplePhases(object)){
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = confuns::rename_numeric_vars,
rename.data = FALSE,
...)
base::return(vset_by_phases_renamed)
})
} else {
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = confuns::rename_numeric_vars,
rename.data = FALSE,
...)
}
# rename clustering
if(multiplePhases(object)){
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine(input = conv_object@variables, ...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine(input = conv_object@variables, ...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
# rename dim red
if(multiplePhases(object)){
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine(input = conv_object@variables_num, ...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine(input = conv_object@variables_num, ...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
return(object)
}
rename_analysis_slots_with <- function(object, ...){
# rename correlation
if(multiplePhases(object)){
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = confuns::rename_numeric_vars_with,
rename.data = FALSE,
...)
base::return(vset_by_phases_renamed)
})
} else {
object@analysis$correlation <-
purrr::map(.x = object@analysis$correlation,
.f = confuns::rename_numeric_vars_with,
rename.data = FALSE,
...)
}
# rename clustering
if(multiplePhases(object)){
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine_with(
input = conv_object@variables,
...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$clustering <-
purrr::map(.x = object@analysis$clustering, # iterate over hclust, kmeans, pam
.f = function(method_list){ # names(method_list): vsets for which clustering with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables <-
confuns::vredefine_with(
input = conv_object@variables,
...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
# rename dim red
if(multiplePhases(object)){
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(vset_by_phases){
vset_by_phases_renamed <-
purrr::map(.x = vset_by_phases,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine_with(
input = conv_object@variables_num,
...)
base::return(conv_object)
})
base::return(vset_by_phases_renamed)
})
base::return(method_list_out)
})
} else {
object@analysis$dim_red <-
purrr::map(.x = object@analysis$dim_red, # iterate over pca, tsne, umap
.f = function(method_list){ # names(method_list): vsets for which dim red with that method has been computed
method_list_out <-
purrr::map(.x = method_list,
.f = function(conv_object){
conv_object@variables_num <-
confuns::vredefine_with(
input = conv_object@variables_num,
...)
base::return(conv_object)
})
base::return(method_list_out)
})
}
return(object)
}
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