Real world FIE-MS dataset.

data(abr1)

Source

The FIEmspro package https://github.com/aberHRML/FIEmspro

Value

A list with the following elements:

fact

A data frame containing experimental meta-data.

pos

A data frame for positive data with 120 observations and 2000 variables.

neg

A data frame for negative data with 120 observations and 2000 variables.

Details

FIE-MS data matrices developed from analysis of samples representing a time course of pathogen attack in a model plant species (Brachypodium distachyon). The data was developed in a single batch with all samples randomised using a Thermo LTQ linear ion trap processed using fiems_ltq_main. Both positive and negative ion mode are given (abr1$pos and abr1$neg). To avoid confusions, variable names are given with a letter corresponding to the ionisation mode followed by the actual nominal mass value (e.g. P130 corresponds to the nominal mass 130 in the positive mode).

Experimental factors are given in the abr1$fact data frame:

  • injorder: sample injection order

  • name: sample name

  • rep: biological replicate for a given class

  • day: number of days following infection after which the sample has been harvested - Level H corresponds to an healthy plant.

  • class: identical to day except that class=6 when day=H

  • pathcdf,filecdf,name.org,remark: are generated from profile processing and are kept for traceability purposes.

Factor of interest for classification are contained in abr1$fact$day. There are 20 biological replicates in each class has

Author

Manfred Beckmann, David Enot and Wanchang Lin meb,dle,

Examples

# Load data set data(abr1) # Select data set dat <- abr1$neg # number of observations and variables in the negative mode matrix dim(dat)
#> [1] 120 2000
# names of the variables dimnames(dat)[[2]] %>% head()
#> [1] "N1" "N2" "N3" "N4" "N5" "N6"
# print out the experimental factors abr1$fact %>% head()
#> injorder pathcdf filecdf name.org remark name #> 1 1 C:/Xcalibur/ANDI-LTQ/050509-Abr1/cdf 01.cdf 12_2 ok 12_2 #> 2 2 C:/Xcalibur/ANDI-LTQ/050509-Abr1/cdf 02.cdf 13_3 ok 13_4 #> 3 3 C:/Xcalibur/ANDI-LTQ/050509-Abr1/cdf 03.cdf 15_4 ok 15_5 #> 4 4 C:/Xcalibur/ANDI-LTQ/050509-Abr1/cdf 04.cdf 12_1 ok 12_2 #> 5 5 C:/Xcalibur/ANDI-LTQ/050509-Abr1/cdf 05.cdf 12_2 ok 12_2 #> 6 6 C:/Xcalibur/ANDI-LTQ/050509-Abr1/cdf 06.cdf 11_1 ok 11_2 #> rep day class #> 1 2 2 2 #> 2 3 3 3 #> 3 5 4 4 #> 4 2 1 1 #> 5 2 2 2 #> 6 1 1 1
# check out the repartition of class table(abr1$fact$class)
#> #> 1 2 3 4 5 6 #> 20 20 20 20 20 20