In tidymass/massdataset: massdataset

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = TRUE,
  out.width = "100%"
)

Use `extract_xxx` functions

We first created a mass_dataset class object.

library(massdataset)
library(tidyverse)

data("expression_data")
data("sample_info")
data("sample_info_note")
data("variable_info")
data("variable_info_note")

object =
  create_mass_dataset(
    expression_data = expression_data,
    sample_info = sample_info,
    variable_info = variable_info,
    sample_info_note = sample_info_note,
    variable_info_note = variable_info_note
  )

object

In massdataset package, there are a series of functions named as extract_xxx(), users can use them to extract data from mass_dataset calss object.

##sample_info
extract_sample_info(object)
##variable_info
extract_variable_info(object) %>% head()
##expression_data
extract_expression_data(object) %>% head()
##sample_info_note
extract_sample_info_note(object) 
##variable_info_note
extract_variable_info_note(object) 
##ms2_data
extract_ms2_data(object)
##process_info
extract_annotation_table(object)
##process_info
extract_process_info(object)

Use slot()

slot(object = object, name = "sample_info")
slot(object = object, name = "variable_info") %>% head()
slot(object = object, name = "expression_data") %>% head()
slot(object = object, name = "sample_info_note") 
slot(object = object, name = "variable_info_note") 
slot(object = object, name = "ms2_data") 
slot(object = object, name = "process_info") 
slot(object = object, name = "annotation_table")

Use `@`

mass_data class is a S4 object. So we can also use @.

object@expression_data %>% head()
object@sample_info
object@variable_info %>% head()
object@sample_info_note
object@variable_info_note
object@process_info
object@ms2_data
object@annotation_table