Repository files navigation

As of version 2.0.0 the MIC package has being refocused to provideMIC-specific analysis and validation utilities. Genomics-relatedfunctions (PATRIC download helpers, genome ->$k$-mer conversion,$k$-mer utilities, and similar) have been ported to a new package,faLearn.

To continue using these functions, simply install and load thefaLearnpackage alongsideMIC:

# install.packages("remotes")remotes::install_github("agerada/faLearn")library(faLearn)

MIC is an R package for the analysis of minimum inhibitoryconcentration (MIC) data. The package was designed to be compatible withtheAMR, in particular most of the functionsinMIC are designed to accept and returnAMR objects, such asmicandsir. The primary functions inMIC are designed towardsvalidation studies of minimum inhibitory concentrations, however it alsocan (optionally) be used to support the construction of machine learningmodels that predict MIC values from genomic data.

• Validation metrics (such as essential agreement) for MIC experimentsor predictions allow comparison against a gold standard, in line withISO 20776-2:2021.
• Plots and tables can be generated from validation experiments.
• Quality control analysis of MIC experiments.
• Functions to deal with censoring in MIC data.
• Helper functions to download whole genome sequencing data andsusceptibility metadata from thePATRICdatabase at BV-BRC.
• Conversion of whole genome sequence data (assembled .fna files) tok-mer based features for machine learning models.
• Fast k-mer counting using C++ andRcpp.
• K-mer features stored inXGBoost-compatiblelibsvm format.

install.packages("MIC")

# install.packages("remotes")remotes::install_github("agerada/MIC")

Load theMIC package – it is highly recommended thatAMR is alsoloaded. Where possible,MIC functions maintain compatibility withAMR objects, in particular themic andsir classes.

library(MIC)#>#> Attaching package: 'MIC'#> The following object is masked from 'package:base':#>#>     tablelibrary(AMR)

To compare twomic vectors (e.g., one from a gold standard and onefrom a prediction or investigational assay), thecompare_mic functioncan be used. An example dataset of MIC values is provided with thepackage, which will be used here.

data("example_mics")head(example_mics)#>      gs  test           mo  ab#> 1 0.002 0.002 B_ESCHR_COLI GEN#> 2 0.004 0.002 B_ESCHR_COLI GEN#> 3     8    16 B_ESCHR_COLI GEN#> 4 0.008 0.016 B_ESCHR_COLI GEN#> 5    64    64 B_ESCHR_COLI GEN#> 6  0.06  0.06 B_ESCHR_COLI GEN

The dataset contains MIC values (inmic format) for a “test” assay,and a “gold standard” (gs) assay. We will usecompare_mic to comparethe MICs and validate the “test” assay:

val<- compare_mic(gold_standard=example_mics$gs,test=example_mics$test)val#> MIC validation object with 300 observations#> Agreement type: essential

Callingsummary provides the essential agreement (EA) rates and assaybias:

summary(val)#> MIC validation summary#> Essential agreement: 267 (89%)#> Bias: -7

If organisms and antimicrobials are provided,compare_mic will alsocalculate and return the categorical agreement (CA) rates, in the formof minor, major, and very major errors:

val<- compare_mic(gold_standard=example_mics$gs,test=example_mics$test,mo=example_mics$mo,ab=example_mics$ab)val#> MIC validation object with 300 observations#> Agreement type: essential and categorical#> Antibiotics: GEN, MEM, AMX#> Organisms: B_ESCHR_COLI

This time, callingsummary will provide a breakdown of the categoricalagreement rates in addition to the EA rates:

summary(val)#> MIC validation summary#> Antibiotic: AMX, GEN, MEM#> Organism: B_ESCHR_COLI#> Essential agreement: 267 (89%)#> Resistant: 113 (37.67%)#> Minor errors: 0 (0%)#> Major errors: 6 (2%)#> Very major errors: 8 (2.67%)#> Mean bias: -7#> N: 300#> *Use as.data.frame() to see full summary*

Usingas.data.frame allows us to continue working with the summarisedresults:

head(as.data.frame(val))#>   gold_standard  test essential_agreement  ab           mo gold_standard_sir#> 1         0.002 0.002                TRUE GEN B_ESCHR_COLI                 S#> 2         0.004 0.002                TRUE GEN B_ESCHR_COLI                 S#> 3             8    16                TRUE GEN B_ESCHR_COLI                 R#> 4         0.008 0.016                TRUE GEN B_ESCHR_COLI                 S#> 5            64    64                TRUE GEN B_ESCHR_COLI                 R#> 6          0.06  0.06                TRUE GEN B_ESCHR_COLI                 S#>   test_sir error#> 1        S  <NA>#> 2        S  <NA>#> 3        R  <NA>#> 4        S  <NA>#> 5        R  <NA>#> 6        S  <NA>

The results of anmic_validation can be plotted in a confusion matrix(failed essential agreements are in red):

plot(val)

The plot can also be faceted by antimicrobial:

plot(val,facet_wrap_ncol=1)

Thetable function can be used to generate a table of the results:

# generate table for MEMmem_dat<- subset(example_mics,ab=="MEM")mem_val<- compare_mic(gold_standard=mem_dat$gs,test=mem_dat$test)table(mem_val)