Number of samples
Number of variables
Sample IDs
Variable IDs
Get missing value number/percentage in expression data.
###old version #' @title apply #' @method apply mass_dataset #' @param X X #' @param MARGIN MARGIN #' @param FUN FUN #' @param ... ... #' @param simplify simplify #' @export #' @rdname summary-mass_dataset #' @return result
#' @title intersect #' @method intersect mass_dataset #' @param x x #' @param y y #' @export #' @rdname summary-mass_dataset #' @return result
get_sample_number(object)
get_variable_number(object)
get_sample_id(object)
get_variable_id(object)
get_mv_number(
object,
by = c("total", "sample", "variable"),
show_by = c("number", "percentage")
)
# S3 method for mass_dataset
dim(x)
# S3 method for mass_dataset
nrow(x)
# S3 method for mass_dataset
ncol(x)
# S3 method for mass_dataset
colnames(x)
# S3 method for mass_dataset
rownames(x)
# S4 method for mass_dataset
apply(X, MARGIN, FUN, ..., simplify = TRUE)
# S4 method for mass_dataset,mass_dataset
intersect(x, y)
# S3 method for mass_dataset
summary(object, ...)
# S3 method for mass_dataset
length(x)
# S3 method for mass_dataset
names(x)
# S3 method for mass_dataset
dimnames(x)
# S3 method for mass_dataset
is.na(x)
# S4 method for mass_dataset
add_column(
.data,
...,
.before = NULL,
.after = NULL,
.name_repair = c("check_unique", "unique", "universal", "minimal")
)
object
total: Missing value number in total. sample: Missing value number in each sample. variable: Missing value number in each variable.
number: missing value number. percentage: missing value percentage.
x
X
MARGIN
FUN
dynamic-dots Name-value pairs, passed on to tibble(). All values must have the same size of .data or size 1.
simplify
y
mass_data class
One-based column index or column name where to add the new columns, default: after last column.
One-based column index or column name where to add the new columns, default: after last column.
Treatment of problematic column names: "minimal": No name repair or checks, beyond basic existence, "unique": Make sure names are unique and not empty, "check_unique": (default value), no name repair, but check they are unique, "universal": Make the names unique and syntactic a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R). A purrr-style anonymous function, see rlang::as_function() This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.
A numeric.
A numeric.
A character vector.
A character vector.
A numeric (vector).
message
message
message
message
message
result
result
vector object
vector object
vector object
message
mass_dataset class
apply.mass_dataset = function(X, MARGIN, FUN, ..., simplify = TRUE) apply(as.matrix(X@expression_data), MARGIN, FUN, ..., simplify = simplify)
intersect.mass_dataset = function(x, y) intersect(x@sample_info$sample_id, y@sample_info$sample_id)
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
get_sample_number(object = object)
#> [1] 8
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
get_variable_number(object = object)
#> [1] 1000
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
get_sample_id(object = object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
head(get_variable_id(object = object))
#> [1] "M136T55_2_POS" "M79T35_POS" "M307T548_POS" "M183T224_POS"
#> [5] "M349T47_POS" "M182T828_POS"
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:33:21
head(get_variable_id(object = object))
#> [1] "M136T55_2_POS" "M79T35_POS" "M307T548_POS" "M183T224_POS"
#> [5] "M349T47_POS" "M182T828_POS"
get_mv_number(object)
#> [1] 3829
get_mv_number(object, by = "sample")
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> 682 702 397 381 424 427 405 411
head(get_mv_number(object, by = "variable", "percentage"))
#> M136T55_2_POS M79T35_POS M307T548_POS M183T224_POS M349T47_POS
#> 0.250 0.250 0.375 0.750 0.250
#> M182T828_POS
#> 0.125