install.packages("tidyverse")Appendix A — FAQs
A.1 How do I install R packages other than SeroTrackR?
The key package that you will need to be able to run the SeroTrackR R package and perform any other subsequent analyses is the tidyverse R packages.
Then once you have installed the tidyverse meta-package, then you load the library in as below:
library(tidyverse)There are many dependencies that the SeroTrackR R package relies on. These packages help us to wrangle our data, process our MFI data into RAU, apply the machine learning classification algorithm, and create PDF reports.
If you are using R via RStudio, then when you load the SeroTrackR package it may prompt you to install all of these other files. Select yes if that is the case.
If it does not prompt you automatically, or if you are using another platform to use R, then use the code below:
setup <- function(){
needed <- c(
# Imports: Required
"dplyr", "drc", "forcats", "ggplot2", "here", "janitor",
"kableExtra", "knitr", "magrittr", "openxlsx", "parsnip",
"purrr", "ranger", "readr", "readxl", "rmarkdown", "stats",
"stringr", "tidyr", "tidyselect", "utils", "workflows",
# Imports: Suggested
"glue", "htmltools", "httr", "jsonlite", "shiny.fluent",
"tidyverse", "zoo"
)
for(package in needed){
if(!sum(installed.packages() %in% package)){
install.packages(package)
}
require(package, character.only = TRUE)
}
}
setup()A.2 How do I organise my files?
It is best practice to create an R project (.Rproj) file and store all of your files in there. A comprehensive tutorial on how to create an R project can be found here.
You can then create a Quarto Markdown Document data_processing.qmd inside your R project where you can process all of your serological data.
my_R_project
└── data/
├── raw_data_plate1.csv
├── raw_data_plate2.csv
├── raw_data_plate3.csv
├── platelayout.xlsx
└── results/
└── data_processing.qmdA.3 How can I read in my data?
Once you have an R project (.Rproj) setup, you can save all of your files into a new folder called “data”. Then you can read in your data as follows:
my_raw_data <- "data/my_plate1.csv"
my_plate_layout <- "data/my_plate_layout.xlsx"A.4 Is there capability to run this without internet?
You will need internet to initially download the R package and to process certain files which call upon the internet.
Important
The developers are currently working on how to leverage a safe no-internet option that allows for the incorporation of the classification algorithm. The reason we use the internet at the moment is because the files containing the algorithm are quite large.
A.5 I have multiple plate layout files. How can I input them?
Use the getPlateLayout() function to create a master plate layout file to then input into the other functions in the package!
getPlateLayout("your/folder/with/plate/layouts/")Here replace “your/folder/with/plate/layouts/” with the main file that contains your folders. For example, if your folder looks like this:
my_R_project/
└── data/
├── plate_1/
│ ├── raw_magpix_data_plate1.csv
│ └── plate_layout_1.xlsx
├── plate_2/
│ ├── raw_magpix_data_plate2.csv
│ └── plate_layout_2.xlsx
└── plate_3/
├── raw_magpix_data_plate3.csv
└── plate_layout_3.xlsxyou would write:
getPlateLayout("data/")you could ALSO write:
getPlateLayout()OR:
getPlateLayout(folder_path = c("plate_layout_1.xlsx", "plate_layout_2.xlsx", "plate_layout_3.xlsx"))A.6 I have multiple Luminex data types I’d like to analyse. How can I do this?
If you have, for example, both Bio-Plex and MAGPIX files and would like to analyse them both, then you can do some clever data manipulation as below:
*Note that in this example, there is one plate layout that contains all files in it. But the same idea can apply to readPlateLayout().
Input your Bio-Plex file/s:
Using a reproducible example:
library(SeroTrackR)
library(tidyverse)
bioplex_raw_plates <- c(
system.file("extdata", "example_BioPlex_plate1.xlsx", package = "SeroTrackR"),
system.file("extdata", "example_BioPlex_plate2.xlsx", package = "SeroTrackR")
)
all_plate_layout <- system.file("extdata", "example_platelayout_1.xlsx", package = "SeroTrackR")For your data:
bioplex_raw_plates <- c(
"data/example_BioPlex_plate1.xlsx",
"data/example_BioPlex_plate2.xlsx"
)
all_plate_layout <- "data/example_platelayout_1.xlsx" Input your MAGPIX file/s:
Using a reproducible example:
magpix_raw_plate <- system.file("extdata", "example_MAGPIX_plate3.csv", package = "SeroTrackR")For your data:
magpix_raw_plate <- "data/example_MAGPIX_plate3.csv"Read the serological data files in:
# Serological data
bioplex_sero_data <- readSeroData(
raw_data = bioplex_raw_plates,
platform = "bioplex"
)PASS: File example_bioplex_plate1.xlsx successfully validated.PASS: File example_bioplex_plate2.xlsx successfully validated.magpix_sero_data <- readSeroData(
raw_data = magpix_raw_plate,
platform = "magpix",
version = "4.2"
)PASS: File example_magpix_plate3.csv successfully validated.Merge the files together:
sero_data_merged <- NULL
# data_raw
sero_data_merged$data_raw <- bioplex_sero_data$data_raw %>%
bind_rows(magpix_sero_data$data_raw)
# results
sero_data_merged$results <- bioplex_sero_data$results %>%
bind_rows(magpix_sero_data$results)
# counts
sero_data_merged$counts <- bioplex_sero_data$counts %>%
bind_rows(magpix_sero_data$counts)
# blanks
sero_data_merged$blanks <- bioplex_sero_data$blanks %>%
bind_rows(magpix_sero_data$blanks)
# stds
sero_data_merged$stds <- bioplex_sero_data$stds %>%
bind_rows(magpix_sero_data$stds)
# run
sero_data_merged$run <- bioplex_sero_data$run %>%
bind_rows(magpix_sero_data$run)Continue the rest of the pipeline:
plate_list_all <- readPlateLayout(
plate_layout = all_plate_layout,
sero_data = sero_data_merged
)
qc_results <- runQC(
sero_data = sero_data_merged,
plate_list = plate_list_all
)
mfi_to_rau_output <- MFItoRAU(
sero_data = sero_data_merged,
plate_list = plate_list_all,
qc_results = qc_results,
std_point = 10
)
# etc.. A.7 How can I count the number of seropositive and seronegative samples?
To do this, use the template code below, assuming that you have followed the steps outlined here.
classifyResults_output %>%
count(pred_class_max)# A tibble: 2 × 2
pred_class_max n
<fct> <int>
1 seronegative 43
2 seropositive 209To calculate the serostatus PER PLATE:
classifyResults_output %>%
count(Plate, pred_class_max)# A tibble: 6 × 3
Plate pred_class_max n
<chr> <fct> <int>
1 plate1 seronegative 9
2 plate1 seropositive 75
3 plate2 seronegative 17
4 plate2 seropositive 67
5 plate3 seronegative 17
6 plate3 seropositive 67To calculate the percentage of the serostatus per plate:
classifyResults_output %>%
count(Plate, pred_class_max) %>%
group_by(Plate) %>%
mutate(Percent = round(100 * n / sum(n), 1))# A tibble: 6 × 4
# Groups: Plate [3]
Plate pred_class_max n Percent
<chr> <fct> <int> <dbl>
1 plate1 seronegative 9 10.7
2 plate1 seropositive 75 89.3
3 plate2 seronegative 17 20.2
4 plate2 seropositive 67 79.8
5 plate3 seronegative 17 20.2
6 plate3 seropositive 67 79.8You can do some clever data wrangling to make the table look like this:
classifyResults_output %>%
# Count our serostatus by plate
count(Plate, pred_class_max) %>%
group_by(Plate) %>%
mutate(
# Calculate serostatus prevalence for each plate as a percentage
Percent = round(100 * n / sum(n), 1),
# Create new variable "Count" to make it easier to interpret
Count = paste0(n, " (", Percent, "%)")
) %>%
# Remove the "Percent" and "n" columns as they are unnecessary now
dplyr::select(-c(Percent, n)) %>%
# Create cleaner table to view each serostatus type as a column
pivot_wider(
id_cols = Plate,
names_from = pred_class_max,
values_from = Count
)# A tibble: 3 × 3
# Groups: Plate [3]
Plate seronegative seropositive
<chr> <chr> <chr>
1 plate1 9 (10.7%) 75 (89.3%)
2 plate2 17 (20.2%) 67 (79.8%)
3 plate3 17 (20.2%) 67 (79.8%)