inst/scripts/data.md

In Keniajin/protGear: Protein Micro Array Data Management and Interactive Visualization

' Example data for protGear package.

' dataC.csv

' ------------------------------------

' library(protGear)

define the structure

genepix_vars <- array_vars(channel="635" ,

chip_path = system.file("extdata/array_data/", package="protGear"),

totsamples = 21,

blockspersample = 2,

sampleID_path = system.file("extdata/array_sampleID/", package="protGear"),

mig_prefix = "_first",

machine =1,

## optional

date_process = "0520")

read in all the datasets

list all the file names under data folder

filenames <- list.files(file.path(genepix_vars$paths[[1]]),

pattern=".txt$|.gpr$", full.names=FALSE)

read all the data files and save them in a list

data_path <- paste0(genepix_vars$paths[[1]],"/")

data_files <- purrr::map(.x = filenames,

.f = read_array_files,

data_path=data_path ,

genepix_vars=genepix_vars)

data_files <- set_names(data_files, purrr::map(filenames, name_of_files))

## this does the background correction after reading the

## genepix data file.

sample_ID_merged_dfs <- purrr::map(.x=dfs, .f=merge_sampleID ,data_files=data_files ,

genepix_vars, method="subtract_local")

sample_ID_merged_dfs <- set_names(sample_ID_merged_dfs, purrr::map(filenames, name_of_files))

dataC <- sample_ID_merged_dfs[[1]]

@-------------------------------------bg_example----------------------------

' bg_example.csv

' ----------------------------------------

' This is a sample of the allData_bg dataset

' ---------------------------------

' dfs <- names(data_files)

allData_bg <- purrr::map(.x=dfs, .f=extract_bg,data_files=data_files,genepix_vars)

allData_bg <- set_names(allData_bg, purrr::map(filenames, name_of_files))

allData_bg <- plyr::ldply(allData_bg)

'

' Data1_sample.csv --

' -----------------------------------

' A sample of the dataC.csv file

'

'

' Data1_bg_sample.csv

'-----------------------------------

' The data is generated from

' Data1_bg_sample <- data_files[[1]]

'

' matrix_antigen.csv

' -------------------------------

' After creating the sample_ID_merged_dfs list

' As indicated in the vignette

' 1) Perform Coefficient of Variation (CV)

' 2) Select the Best replicates

' 3) If the proteins are tagged perform Tag subtraction

' 4) Create the to_normalise data frame and then

to_normalise <- df_to_normalise %>%

ungroup() %>% dplyr::select(-slide,-sampleID,-sample_array_ID) %>%

dplyr::select(antigen, sample_index, everything()) %>%

gather(variable, value, -(antigen:sample_index)) %>%

unite(temp, antigen ) %>% dplyr::select(-variable) %>%

spread(temp, value) %>%

as.data.frame(.)

### get the row names of the machine data

row.names(to_normalise) <- to_normalise$sample_index

#batch_all <- as.factor(paste0(to_normalise$machine,"/",to_normalise$day))

#machines <- as.factor(to_normalise$machine)

#day_batches <- as.factor(to_normalise$day)

## create the matrix to normalise

matrix_antigen <- to_normalise %>%

dplyr::select(-sample_index) %>%

as.matrix(.)

'

' array_matrix.csv

' --------------------------------

'## create the matrix to hold the important parameters

in place of AMA1 you use one of your features or antigen

array_matrix <- df_to_normalise %>%

filter(antigen=="AMA1") %>%

ungroup() %>%

dplyr::select(sample_array_ID,sample_index,slide)

'

'

' buffers_sample2.csv

' ---------------------------------

' This is a sample of buffers data frame below

buffer_transp <- purrr::map(.x=sample_ID_merged_dfs,

.f=buffer_spots , buffer_spot="buffer")

buffer_transp <- set_names(buffer_transp, purrr::map(filenames, name_of_files))

buffers <- plyr::ldply(buffer_transp)

'

'

' TAG_antigens.csv

' ----------------------

' This is a file with all the proteins processed and their respective TAG's.

' Tags are explained in detail under https://www.frontiersin.org/articles/10.3389/fimmu.2018.02866/full

' Microarray Protein Map

'

' array_data/machine1

' -----------------------------------

' KK2-06.txt and BRB001.txt

' This is data extracted using GenePix® Pro software (Molecular Devices).

' This is data sets from from KILchip v1.0 (https://doi.org/10.3389/fimmu.2018.02866),

' a protein microarray chip designed to enable the simultaneous detection of antibodies

' against > 100 Plasmodium falciparum proteins. The slides were printed in

' triplicate on a slide divided into mini-arrays, defined as the region allocated to

' a discrete sample and can be further divided into blocks.

'

'

' @format An object of class 'ElistRaw' from limma package that

' contains 5 entries: matrix E, data.frames targets, genes, source and printer.

' E is a matrix of protein intensities of size 23232 by 28 (number of samples);

' targets is a data.frame containing original sample file names;

' genes is a data.frame with information about each protein on the Protoarray

' source stores a name of method that has produced this dataset

' and printer contains information about printer

'

"rawdata"

Keniajin/protGear documentation built on Feb. 6, 2023, 6:28 p.m.