In tidymass/massqc: Quality assessment of LC-MS based metabolomics data

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

Data preparation

library(massdataset)
library(tidyverse)
library(massqc)
data("sample_info", package = "massdataset")
data("expression_data", package = "massdataset")
data("variable_info", package = "massdataset")
object =
  create_mass_dataset(
    expression_data = expression_data,
    sample_info = sample_info,
    variable_info = variable_info
  )
object

PCA 2D plot

object %>%
  massqc_pca()

object %>%
  massqc_pca(color_by = "class")

###scale and then PCA
object %>%
  scale %>%
  massqc_pca(color_by = "class")

###log scale and then PCA
object %>%
  `+`(1) %>% 
  log %>% 
  scale %>%
  massqc_pca(color_by = "class")

object %>%
  scale %>%
  massqc_pca(color_by = "class", frame = FALSE) +
  ggsci::scale_fill_lancet()

object %>%
  scale %>%
  massqc_pca(color_by = "class", frame = FALSE) +
  ggsci::scale_fill_lancet() +
  ggrepel::geom_text_repel(aes(label = sample_id))

object %>%
  scale %>%
  massqc_pca(color_by = "class", frame = FALSE) +
  ggsci::scale_fill_lancet() +
  ggrepel::geom_text_repel(aes(label = ifelse(class == "QC", sample_id, NA)))

PCA 1D plot

####only show the PC1
object %>%
  massqc_pca_pc1()

object %>%
  massqc_pca_pc1(color_by = "class")

object %>%
  `+`(1) %>% 
  log() %>% 
  scale %>%
  massqc_pca_pc1(color_by = "class")

object %>%
  `+`(1) %>% 
  log() %>% 
  scale %>%
  massqc_pca_pc1(
    color_by = "class",
    order_by = "injection.order",
    point_alpha = 1,
    point_size = 5
  ) +
  ggsci::scale_color_lancet()

object %>%
  `+`(1) %>% 
  log() %>% 
  scale %>%
  massqc_pca_pc1(
    color_by = "class",
    order_by = "injection.order",
    point_alpha = 1,
    point_size = 5, 
    desc = TRUE
  ) +
  ggsci::scale_color_lancet()