In tidymass/masscleaner: Data cleaning for LC-MS based metabolomics data

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

Introduction

We can use masscleaner to remove noisy features and outlier samples.

First, we need to prepare samples for masscleaner.

Download the demo data: peak tables and uncompress.

Download the demo data: sample information and uncompress.

Data preparation

Here we only use the positive mode as an example.

library(masscleaner)
library(tidyverse)

peak_table_pos = readr::read_csv("peak_tables/POS/Peak_table_for_cleaning.csv") %>%
  as.data.frame()
sample_info_pos = readr::read_csv("sample_info/sample_info_pos.csv") %>%
  as.data.frame()

expression_data_pos <- 
  peak_table_pos %>% 
  dplyr::select(-c(variable_id, mz, rt))

variable_info_pos <- 
  peak_table_pos %>% 
  dplyr::select(c(variable_id, mz, rt))

rownames(expression_data_pos) = variable_info_pos$variable_id
dim(expression_data_pos)
dim(sample_info_pos)
colnames(expression_data_pos) == sample_info_pos$sample_id

expression_data_pos =
  expression_data_pos[,sample_info_pos$sample_id]
sum(colnames(expression_data_pos) == sample_info_pos$sample_id)

object <-
  create_mass_dataset(expression_data = expression_data_pos, 
                      sample_info = sample_info_pos, 
                      variable_info = variable_info_pos)

object

Summary information.

get_mv_number(object)
massdataset::get_mv_number(object, by = "sample") %>% 
  head()
head(massdataset::get_mv_number(object, by = "variable"))

massdataset::get_mv_number(object, by = "sample", show_by = "percentage") %>% 
  head()
head(massdataset::get_mv_number(object, by = "variable"), show_by = "percentage")

Filter noisy features

Remove variables who have mv in more than 20% QC samples and in at lest 50% samples in control group or case group.

object %>% 
  activate_mass_dataset(what = "sample_info") %>% 
  dplyr::count(group)

show_variable_missing_values(object = object, 
                             percentage = TRUE) +
  scale_size_continuous(range = c(0.01, 2))

qc_id =
  object %>%
  activate_mass_dataset(what = "sample_info") %>%
  filter(class == "QC") %>%
  pull(sample_id)

control_id =
  object %>%
  activate_mass_dataset(what = "sample_info") %>%
  filter(group == "Control") %>%
  pull(sample_id)

case_id =
  object %>%
  activate_mass_dataset(what = "sample_info") %>%
  filter(group == "Case") %>%
  pull(sample_id)

object =
  object %>%
  mutate_variable_na_freq(according_to_samples = qc_id) %>%
  mutate_variable_na_freq(according_to_samples = control_id) %>%
  mutate_variable_na_freq(according_to_samples = case_id)

head(extract_variable_info(object))

object <- 
  object %>% 
  activate_mass_dataset(what = "variable_info") %>%
  filter(na_freq < 0.2 & (na_freq.1 < 0.5 | na_freq.2 < 0.5))
object

show_variable_missing_values(object = object[,qc_id], 
                             percentage = TRUE)

show_variable_missing_values(object = object[,control_id], 
                             percentage = TRUE)

show_variable_missing_values(object = object[,case_id], 
                             percentage = TRUE)

object %>% 
  activate_mass_dataset(what = "variable_info") %>% 
  dplyr::filter(na_freq.1 > 0.5) %>% 
  extract_variable_info() %>% 
  ggplot(aes(na_freq.1, na_freq.2)) +
  geom_point() +
  scale_x_continuous(limits = c(0,1)) +
  scale_y_continuous(limits = c(0,1))

object %>% 
  activate_mass_dataset(what = "variable_info") %>% 
  dplyr::filter(na_freq.2 > 0.5) %>% 
  extract_variable_info() %>% 
  ggplot(aes(na_freq.1, na_freq.2)) +
  geom_point() +
  scale_x_continuous(limits = c(0,1)) +
  scale_y_continuous(limits = c(0,1))

Filter outlier samples

We can use the detect_outlier() to find the outlier samples.

More information about how to detect outlier samples can be found here.

massdataset::show_sample_missing_values(object = object,
                                        color_by = "group",
                                        order_by = "injection.order",
                                        percentage = TRUE) +
  ggsci::scale_color_aaas()

Detect outlier samples.

outlier_samples =
  object %>%
  `+`(1) %>% 
  log(2) %>%
  scale() %>%
  detect_outlier()

outlier_samples

outlier_table <-
extract_outlier_table(outlier_samples)
outlier_table %>% 
  head()

outlier_table %>% 
  apply(1, function(x){
    sum(x)
  }) %>% 
  `>`(0) %>% 
  which()

No outlier samples.

Save data for next analysis.

save(object, file = "peak_tables/POS/object")

Session information

sessionInfo()

tidymass/masscleaner documentation built on Sept. 4, 2023, 3:21 a.m.