Generate + integrate a platemap and group results.
Mark Cheng
19/03/2024
How_to_integrate_a_platemap.Rmdload the library/package from github.
if(!require("remotes"))install.packages("remotes",repos="http://cran.us.r-project.org")## Loading required package: remotes
if(!require("NormaliseForIC50"))remotes::install_github("TKMarkCheng/NormaliseForIC50",dependencies = TRUE, force = TRUE)## Loading required package: NormaliseForIC50##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
options(scipen = 999) # do not use scientific notationReading in or generating the platemap
STEP1: Loading sample, defining output file.
search_folder = "Validation/" #CHANGE this to the folder containing all your input .xlsx data files
input_directory = paste0(here::here(search_folder),"/")
output_generated_plateMap_file <- here::here("Validation/validation_output/example_generated_platemap.xlsx") # CHANGE directory and name for yourselfSTEP2: Generate platemap based on the files in the specified directory.
This function fixes missing columns, and also finds the promega_plate_path by searching the Plate_Name you have given. You need to make sure your plate names are unique.
example_generated_plate_map <- NormaliseForIC50::generate_plate_map(input_directory,output_mode = "return")## A total of 17 promega data plates are found out of 30 excel files in the directory /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation//.## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `file_creation_date = get_read_date(promega_plate_path)`.
## ℹ In row 11.
## Caused by warning in `as.Date()`:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
head(example_generated_plate_map)## Group Negative_Control_Column Positive_Control_Column Individual_condition
## 1 NA 1 2 NA
## 2 NA 1 2 NA
## 3 NA 1 2 NA
## 4 NA 1 2 NA
## 5 NA 1 2 NA
## 6 NA 1 2 NA
## Virus Plate Plate_Name Well dilution_or_concentration
## 1 NA NA example1 A3 dilution
## 2 NA NA example1kc A3 dilution
## 3 NA NA example1 A4 dilution
## 4 NA NA example1kc A4 dilution
## 5 NA NA example1 A5 dilution
## 6 NA NA example1kc A5 dilution
## Starting_Dilution_or_concentration dilution_series
## 1 20 1 in 3
## 2 20 1 in 3
## 3 20 1 in 3
## 4 20 1 in 3
## 5 20 1 in 3
## 6 20 1 in 3
## promega_plate_path
## 1 /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation///2022-09-04_reads/example1.xlsx
## 2 /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation///anomaly_detection/example1kc.xlsx
## 3 /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation///2022-09-04_reads/example1.xlsx
## 4 /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation///anomaly_detection/example1kc.xlsx
## 5 /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation///2022-09-04_reads/example1.xlsx
## 6 /home/runner/work/NormaliseForIC50/NormaliseForIC50/Validation///anomaly_detection/example1kc.xlsx
## file_creation_date column
## 1 2022-09-04 3
## 2 <NA> 3
## 3 2022-09-04 4
## 4 <NA> 4
## 5 2022-09-04 5
## 6 <NA> 5you can choose to write to an excel file and manually add values. Or you can feed in a pre-made excel file.
STEP3: Manual correction of list
#NormaliseForIC50::generate_plate_map(input_directory,output_mode = "write",output_plateMap_file = output_generated_plateMap_file)
## manual correction/filling, making a new file as below
platemap <- readxl::read_excel(path="../Validation/validation_output/example_generated_platemap_manual_changes.xlsx")
platemap## # A tibble: 124 × 14
## Group Negative_Control_Column Positive_Control_Column Individual_condition
## <chr> <dbl> <dbl> <chr>
## 1 sera 1 2 patient1
## 2 sera 1 2 patient1
## 3 sera 1 2 patient2
## 4 sera 1 2 patient2
## 5 sera 1 2 patient3
## 6 sera 1 2 patient3
## 7 sera 1 2 patient4
## 8 sera 1 2 patient4
## 9 sera 1 2 patient5
## 10 sera 1 2 patient5
## # ℹ 114 more rows
## # ℹ 10 more variables: Virus <chr>, Plate <lgl>, Plate_Name <chr>, Well <chr>,
## # dilution_or_concentration <chr>, Starting_Dilution_or_concentration <dbl>,
## # dilution_series <chr>, promega_plate_path <chr>, file_creation_date <dttm>,
## # column <chr>using the platemap - query, normalise, and merge
get a collated sheet of normalised values of all the plates and columns described in the plate map.
df_list <- list()
for (each_plate_name in unique(platemap$Plate_Name)){
print(each_plate_name)
individual_platemap <- platemap %>% dplyr::filter(Plate_Name == each_plate_name)
plate_normalised <- NormaliseForIC50::normalise_plate_using_plateMap(individual_platemap)
#plate_normalised <- cbind(plate_name=each_plate_name,plate_normalised)
df_list[[each_plate_name]] <- plate_normalised
}## [1] "example1"## negative control = 83.75## positive control = 299325## negative control = 83.75## positive control = 299325## negative control = 83.75## positive control = 299325## negative control = 83.75## positive control = 299325## negative control = 83.75## positive control = 299325## [1] "example2"## negative control = 96.25## positive control = 361825## negative control = 96.25## positive control = 361825## negative control = 96.25## positive control = 361825## negative control = 96.25## positive control = 361825## negative control = 96.25## positive control = 361825## [1] "example3"## negative control = 96.25## positive control = 354575## negative control = 96.25## positive control = 354575## negative control = 96.25## positive control = 354575## negative control = 96.25## positive control = 354575## negative control = 96.25## positive control = 354575## [1] "example4"## negative control = 93.75## positive control = 363150## negative control = 93.75## positive control = 363150## negative control = 93.75## positive control = 363150## negative control = 93.75## positive control = 363150## negative control = 93.75## positive control = 363150## [1] "example5"## negative control = 91.25## positive control = 370775## negative control = 91.25## positive control = 370775## negative control = 91.25## positive control = 370775## negative control = 91.25## positive control = 370775## negative control = 91.25## positive control = 370775## [1] "example6"## negative control = 87.5## positive control = 351112.5## negative control = 87.5## positive control = 351112.5## negative control = 87.5## positive control = 351112.5## negative control = 87.5## positive control = 351112.5## negative control = 87.5## positive control = 351112.5## [1] "example7"## negative control = 92.5## positive control = 393425## negative control = 92.5## positive control = 393425## negative control = 92.5## positive control = 393425## negative control = 92.5## positive control = 393425## negative control = 92.5## positive control = 393425## [1] "example8"## negative control = 101.25## positive control = 381600## negative control = 101.25## positive control = 381600## negative control = 101.25## positive control = 381600## negative control = 101.25## positive control = 381600## negative control = 101.25## positive control = 381600## [1] "example9"## negative control = 91.25## positive control = 385575## negative control = 91.25## positive control = 385575## negative control = 91.25## positive control = 385575## negative control = 91.25## positive control = 385575## negative control = 91.25## positive control = 385575## [1] "example10"## negative control = 77.5## positive control = 361125## negative control = 77.5## positive control = 361125## negative control = 77.5## positive control = 361125## negative control = 77.5## positive control = 361125## negative control = 77.5## positive control = 361125## [1] "Inhib1_Inhib2_Vir1Vir2"## negative control = 30## positive control = 269387.5## negative control = 30## positive control = 269387.5## negative control = 42.5## positive control = 473787.5## negative control = 42.5## positive control = 473787.5## [1] "Inhib1_Inhib2_Vir3Vir4"## negative control = 62.5## positive control = 371925## negative control = 62.5## positive control = 371925## negative control = 41.25## positive control = 290600## negative control = 41.25## positive control = 290600## [1] "Inhib1_Inhib2_Vir5Vir6"## negative control = 16.25## positive control = 266337.5## negative control = 16.25## positive control = 266337.5## negative control = 28.75## positive control = 200875## negative control = 28.75## positive control = 200875
merge_cols = c("Group","dilution_or_concentration","dilution_serie")
plates_merged <- Reduce(function (x,y) merge(x,y,by=merge_cols, all = TRUE),df_list) %>% dplyr::arrange(across(all_of(merge_cols)))
print(glue::glue("{dim(plates_merged)[2] -3} different unique experiment conditions.")) ## 124 different unique experiment conditions.filtering and grouping by virus or condition of interest
filtering
# if you want to pick by specific conditions or viruses
filter_by_condition = NULL
filter_by_virus = c("Virus1","VIrus2")
plates_merged_filter <- NormaliseForIC50::filter_merged(plates_merged,condition=filter_by_condition,virus = filter_by_virus)
openxlsx::write.xlsx(plates_merged_filter,
here::here(paste0(
"Validation/validation_output/PlatesMerged",
"_",(if (!is.null(filter_by_condition)) paste(filter_by_condition,collapse='-') else 'AllCondition'),
"_",(if (!is.null(filter_by_virus)) paste(filter_by_virus,collapse='-') else 'AllVirus'),
".xlsx"))
)Grouping
Grouping by virus
Alternatively, you can group by the virus or individual condition and generate sheets per virus or individual condition.
#groupby viruses
output_file_groupby_virus <- here::here("Validation/validation_output/PlatesMerged_Grouped_by_virus.xlsx")
library(openxlsx)
wb<-createWorkbook()
virues <- unique(platemap$Virus)
for (virus in virues){
plates_merged_filter <- NormaliseForIC50::filter_merged(plates_merged,virus = virus)
sheetName=virus
addWorksheet(wb,sheetName=sheetName)
writeData(wb, sheet = sheetName,
x=plates_merged_filter)
print(glue::glue("data for virus:{virus} calculated, grouped and written."))
}## data for virus:Virus1 calculated, grouped and written.
## data for virus:Virus2 calculated, grouped and written.
## data for virus:Virus3 calculated, grouped and written.
## data for virus:Virus4 calculated, grouped and written.
## data for virus:Virus5 calculated, grouped and written.
## data for virus:Virus6 calculated, grouped and written.
saveWorkbook(wb,file=output_file_groupby_virus,overwrite = TRUE)Grouping by condition
#groupby condition
output_file_groupby_condition <- here::here("Validation/validation_output/PlatesMerged_Grouped_by_condition.xlsx")
library(openxlsx)
wb<-createWorkbook()
conditions <- unique(platemap$Individual_condition)
for (condition in conditions){
plates_merged_filter <- NormaliseForIC50::filter_merged(plates_merged,condition = condition)
sheetName=condition
addWorksheet(wb,sheetName=sheetName)
writeData(wb, sheet = sheetName,
x=plates_merged_filter)
print(glue::glue("data for condition:{condition} calculated, grouped and written."))
}## data for condition:patient1 calculated, grouped and written.
## data for condition:patient2 calculated, grouped and written.
## data for condition:patient3 calculated, grouped and written.
## data for condition:patient4 calculated, grouped and written.
## data for condition:patient5 calculated, grouped and written.
## data for condition:patient6 calculated, grouped and written.
## data for condition:patient7 calculated, grouped and written.
## data for condition:patient8 calculated, grouped and written.
## data for condition:patient9 calculated, grouped and written.
## data for condition:patient10 calculated, grouped and written.
## data for condition:patient11 calculated, grouped and written.
## data for condition:patient12 calculated, grouped and written.
## data for condition:patient13 calculated, grouped and written.
## data for condition:patient14 calculated, grouped and written.
## data for condition:patient15 calculated, grouped and written.
## data for condition:patient16 calculated, grouped and written.
## data for condition:patient17 calculated, grouped and written.
## data for condition:patient18 calculated, grouped and written.
## data for condition:patient19 calculated, grouped and written.
## data for condition:patient20 calculated, grouped and written.
## data for condition:patient21 calculated, grouped and written.
## data for condition:patient22 calculated, grouped and written.
## data for condition:patient23 calculated, grouped and written.
## data for condition:patient24 calculated, grouped and written.
## data for condition:patient25 calculated, grouped and written.
## data for condition:E64d calculated, grouped and written.
## data for condition:Camostat calculated, grouped and written.
saveWorkbook(wb,file=output_file_groupby_condition,overwrite = TRUE)