RaMS and friends

Standard export to CSV

As with all rectangular data, RaMS objects can be easily exported to CSV files with base R functions. This works best with a few chromatograms at a time, as the millions of data points found in most MS files can overwhelm common file readers.

library(RaMS)

# Locate an MS file
single_file <- system.file("extdata", "LB12HL_AB.mzML.gz", package = "RaMS")

# Grab the MS data
msdata <- grabMSdata(single_file, grab_what = "everything")

## 
## Reading file LB12HL_AB.mzML.gz... 0.21 secs 
## Reading MS1 data...0.25 secs 
## Reading MS2 data...0.02 secs 
## Reading BPC...0.08 secs 
## Reading TIC...0.08 secs 
## Reading file metadata...0.13 secs 
## Binding files together into single data.table
## Total time: 0.77 secs

# Write out MS1 data to .csv file
write.csv(x = msdata$MS1, file = "MS1_data.csv")

# Clean up afterward
file.remove("MS1_data.csv")

## [1] TRUE

Fancier export to Excel

Excel workbooks are a common format because of their intuitive GUI and widespread adoption. They can also encode more information than CSV files due to their multiple “sheets” within a single workbook - perfect for encoding both MS1 and MS2 information in one place. This vignette uses the openxlsx package, although there are several alternatives with identical functionality.

library(openxlsx)

# Locate an MS2 file
MS2_file <- system.file("extdata", "S30657.mzML.gz", package = "RaMS")

# Grab the MS1 and MS2 data
msdata <- grabMSdata(MS2_file, grab_what=c("MS1", "MS2"))

## 
## Reading file S30657.mzML.gz... 0.35 secs 
## Reading MS1 data...0.19 secs 
## Reading MS2 data...0.06 secs 
## Binding files together into single data.table
## Total time: 0.61 secs

# Write out MS data to Excel file
# openxlsx writes each object in a list to a unique sheet
# Produces one sheet for MS1 and one for MS2
write.xlsx(msdata, file = "MS2_data.xlsx")

# Clean up afterward
file.remove("MS2_data.xlsx")

## [1] TRUE

Exporting to SQL database

For more robust data processing and storage, or to work with larger-than-memory data sets, SQL databases are an excellent choice. This vignette will demo the RSQLite package’s engine, although several other database engines have similar functionality.

library(DBI)
# Get data from multiple files to show off
mzml_files <- system.file(c("extdata/LB12HL_AB.mzML.gz", 
                            "extdata/LB12HL_CD.mzML.gz"), 
                          package = "RaMS")
msdata <- grabMSdata(mzml_files)

##   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
## Total time: 1.34 secs

# Create the sqlite database and connect to it
MSdb <- dbConnect(RSQLite::SQLite(), "MSdata.sqlite")

# Export MS1 and MS2 data to sqlite tables
dbWriteTable(MSdb, "MS1", msdata$MS1)
dbWriteTable(MSdb, "MS2", msdata$MS2)
dbListTables(MSdb)

## [1] "MS1" "MS2"

# Perform a simple query to ensure data was exported correctly
dbGetQuery(MSdb, 'SELECT * FROM MS1 LIMIT 3')

##      rt       mz        int          filename
## 1 4.009 139.0503 1800550.12 LB12HL_AB.mzML.gz
## 2 4.009 148.0967  206310.81 LB12HL_AB.mzML.gz
## 3 4.009 136.0618   71907.15 LB12HL_AB.mzML.gz

# Perform EIC extraction in SQL rather than in R
EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
query_params <- list(lower_bound=118.086, upper_bound=118.087)
EIC <- dbGetQuery(MSdb, EIC_query, params = query_params)

# Append with additional files
extra_file <- system.file("extdata", "LB12HL_EF.mzML.gz", package = "RaMS")
extra_msdata <- grabMSdata(extra_file, grab_what = "everything")

## 
## Reading file LB12HL_EF.mzML.gz... 0.2 secs 
## Reading MS1 data...0.13 secs 
## Reading MS2 data...0.02 secs 
## Reading BPC...0.08 secs 
## Reading TIC...0.1 secs 
## Reading file metadata...0.14 secs 
## Binding files together into single data.table
## Total time: 0.67 secs

dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1') # Initial number of rows

##   COUNT(*)
## 1    42313

dbAppendTable(MSdb, "MS1", extra_msdata$MS1)

## [1] 22124

# Confirm three different files exist in DB
dbGetQuery(MSdb, 'SELECT DISTINCT filename FROM MS1')

##            filename
## 1 LB12HL_AB.mzML.gz
## 2 LB12HL_CD.mzML.gz
## 3 LB12HL_EF.mzML.gz

# Confirm new rows have been added
dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1')

##   COUNT(*)
## 1    64437

# Disconnect after export
dbDisconnect(MSdb)

# Clean up afterward
unlink("MSdata.sqlite")

Interfacing with Python via reticulate

R and Python are commonly used together, and the reticulate package makes this even easier by enabling a Python interpreter within R. RStudio, in which this vignette was written, supports both R and Python code chunks as shown below.

# Locate a couple MS files
data_dir <- system.file("extdata", package = "RaMS")
file_paths <- list.files(data_dir, pattern = "HL.*mzML", full.names = TRUE)

msdata <- grabMSdata(files = file_paths, grab_what = "BPC")$BPC

##   |                                                                              |                                                                      |   0%  |                                                                              |=======================                                               |  33%  |                                                                              |===============================================                       |  67%  |                                                                              |======================================================================| 100%
## Total time: 0.44 secs

# Not run to pass R CMD check on GitHub
# Make sure python, matplotlib, and seaborn are installed

import seaborn as sns
import matplotlib.pyplot as plt

sns.relplot(data=r.msdata, kind="line", x="rt", y="int", hue="filename")
plt.show()