FastconcR is a program to get an fast and easy discriminatory concentrations for fungi based on serial dilution.
The FastconcR project was developed by Edgar Nieto and Sydney Everhart. A paper with more details about the program is available from
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##License GPL-3 # Loading packages
using("tidyverse", "ezec", "broom")## Loading required package: tidyverse
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.5 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.0.2 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Loading required package: ezec
## Loading required package: broom
#Reading serial dilution data which has 4 repeatss
data.serial.dilution <-
reading_data_serial_dilution("example_serial_dilution_entry.csv",length_repeats = 4,plug = 0.6)
data.serial.dilution hola <- getting_EC50(data.serial.dilution)holadata.RG <- getting_RG(data.serial.dilution)## Joining, by = "ID"
data.RG#Getting the potential Discriminatory concentration
DC_2(data.RG)#Testing the potential gotten Discriminatory concentration
test.DC <- lineal_model_1(data.RG, "logEC50","RG",desiredR2 = 0.75)
#if it release results it means it has good R2 the model
test.DC
### Now testing with default r2
test.DC.2 <- lineal_model_1(data.RG, "logEC50","RG")
test.DC.2myplot_model_1(data.RG)## `geom_smooth()` using formula 'y ~ x'
# Graph of the linear model between LogEC50 and Relative growth included the model and R2 and p value: it depends of the values it might look weird, so it must be modified the function if so
myplot_model_1_label(data.RG)## `geom_smooth()` using formula 'y ~ x'
It tells you if the Estimate.50DC is good or not based on the R2 of the linear regression between Estimate.50DC and the EC50
test.2.DC <- lineal_model_2( test.DC, "Estimate.50DC","EC50", desiredR2 = 0.70)
test.2.DC
### Now testing with default r2
test.2.DC.2 <- lineal_model_2( test.DC, "Estimate.50DC","EC50")
test.2.DC.2myplot_model_2(test.DC)## `geom_smooth()` using formula 'y ~ x'
# plotting model 2 included the model and R2 and p value: it depends of the values it might look weird, so it must be modified the function if so
myplot_model_2_label (test.DC)## `geom_smooth()` using formula 'y ~ x'
#n the following example the data has 3 repeats per ID #Reading the data
using the discriminatory concentrations of the survey
data.dis.concentration <- reading_data_discriminatory_concentration("example_discriminatory_concentration_entry.csv", length_repeats = 3, plug = 0.6)
data.dis.concentration
#Getting the Estimate EC50DC of the survey
dis.concentration<- getting_EC50D_survey(data.RG, data.dis.concentration, "logEC50","RG")
dis.concentration
#If you have some defined by groups if you may have
Farmer.fields.isolates <- c(136:442, 455, 456, 466, 468, 470, 471, 478:554, 602:610, 612:635, 671, 672, 682:690, 695:797, 810:823, 834, 835, 848:1024, 1255: 1326, 1491: 1500, 1661:1670, 1831: 2246, 2303: 2342,2362: 2381, 2393: 2573)
baseline.isolates <- c(1: 136, 444: 454, 457: 465, 467, 469, 472:477, 555:601, 611, 636:670, 673: 680, 691:694 , 798: 809, 824: 833, 836: 847)
#Overwriting and adding the groups
dis.concentration.by.groups <- add_groups(filename = dis.concentration, group1 = baseline.isolates, group2 = Farmer.fields.isolates)myplot_comparison_s(dis.concentration.by.groups)
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