Analysis of electroretinogram data using R
- Electroretinogram (ERG): Test to measure the electrical activity generated by retinal neuronal and non neuronal cells in response to a light stimuli
- Scotopic (DARK) ERG: test rod photoreceptors function
- Photopic (LIGHT) ERG: test cone photoreceptors function
- DARK and LIGHT data are obtained separately for each mice
Open excel workbook containing several sheets
library(readxl)
library(purrr)## Warning: package 'purrr' was built under R version 3.4.4read_excel_allsheets <- function(ERG_file) {
sheets <- readxl::excel_sheets(ERG_file)
Open_sheets <- map(sheets, function(X) readxl::read_excel(ERG_file, sheet = X,skip = 1))
names(Open_sheets) <- sheets
return(Open_sheets)
}Call “read_excel_allsheets” function
ERG_1M <- read_excel_allsheets("~/Desktop/Reanalysis of ERG/BC5/ERG 1M/ERG 1 mo SA43-159_164.xlsx")
str(ERG_1M, max.level = 1)## List of 12
## $ SA43 159 DARK :Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 159 LIGHT:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 160 DARK :Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 160 LIGHT:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 161 DARK :Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 161 LIGHT:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 162 DARK :Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 162 LIGHT:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 163 DARK :Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 163 LIGHT:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 164 DARK :Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 164 LIGHT:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:ERG_1M$`SA43 159 DARK`Select Scotopic (DARG) ERG data
DARK_ERG_1M <- ERG_1M[grep("DARK",names(ERG_1M))]
str(DARK_ERG_1M, max.level = 1)## List of 6
## $ SA43 159 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 160 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 161 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 162 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 163 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:
## $ SA43 164 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 16 variables:Format the elements of the list
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.4##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse)## ── Attaching packages ────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.0.0 ✔ readr 1.1.1
## ✔ tibble 1.4.2 ✔ stringr 1.3.1
## ✔ tidyr 0.8.1 ✔ forcats 0.3.0## Warning: package 'ggplot2' was built under R version 3.4.4## Warning: package 'tidyr' was built under R version 3.4.4## Warning: package 'stringr' was built under R version 3.4.4## ── Conflicts ───────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()Format_ERG_Table <- function(ERG_list){
smalltibble <- ERG_list %>%
slice(2:25) %>%
rename('wave'='Name__1') %>%
mutate(uV = if_else(wave == 'a', -uV, uV)) %>% # Only mutate uV if wave = a
select(2,6,9,10,11)
}Call the function “Format_ERG_Table”
DARK_ERG_1M <- map(DARK_ERG_1M,Format_ERG_Table)## Warning: package 'bindrcpp' was built under R version 3.4.4DARK_ERG_1M$`SA43 159 DARK`Associate S with the corresponding intensity
Mutate_S <- function(DARK_ERG){
DARK_Intensity <- DARK_ERG %>% mutate('Intensity' = case_when (S == 2 ~ -4,
S == 3 ~ -3,
S == 4 ~ -2,
S == 5 ~ -1,
S == 6 ~ 0,
S == 7 ~ 1))
}Call “Mutate_S” the function
DARK_ERG_1M <- map(DARK_ERG_1M,Mutate_S)
str(DARK_ERG_1M, max.level = 2)## List of 6
## $ SA43 159 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 24 obs. of 6 variables:
## ..$ Name : chr [1:24] "159 SA44 159" "159 SA44 159" "159 SA44 159" "159 SA44 159" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -19.3 4.74 1.23 116.8 388.4 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## $ SA43 160 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 24 obs. of 6 variables:
## ..$ Name : chr [1:24] "160 SA44 160" "160 SA44 160" "160 SA44 160" "160 SA44 160" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] 11.1 11.2 20.1 90.3 307.5 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## $ SA43 161 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 24 obs. of 6 variables:
## ..$ Name : chr [1:24] "161 SA43 161" "161 SA43 161" "161 SA43 161" "161 SA43 161" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] 4.55 3.21 13.73 72.21 232 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## $ SA43 162 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 24 obs. of 6 variables:
## ..$ Name : chr [1:24] "162 SA44 162" "162 SA44 162" "162 SA44 162" "162 SA44 162" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -5.889 38.5 -0.215 109.6 360.7 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## $ SA43 163 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 24 obs. of 6 variables:
## ..$ Name : chr [1:24] "163 SA44 163" "163 SA44 163" "163 SA44 163" "163 SA44 163" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -5.29 15.47 12.85 110.4 426.2 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## $ SA43 164 DARK:Classes 'tbl_df', 'tbl' and 'data.frame': 24 obs. of 6 variables:
## ..$ Name : chr [1:24] "164 SA44 164" "164 SA44 164" "164 SA44 164" "164 SA44 164" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -15.9 -12.1 26.6 112.2 375.4 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...Identify each element of the list with its genotype
# Create a new list for each genotype
HET_DARK_ERG_1M <- DARK_ERG_1M[grep("160|163|164",names(DARK_ERG_1M))]
# Create a function to add a column with the Genotype identification for each element
HET_genotype <- function(HET_ERG_list){
New_Genotype_column <- HET_ERG_list %>% cbind(Genotype='HET')
}
# Call the function
HET_DARK_ERG_1M <- map(HET_DARK_ERG_1M,HET_genotype)
str(HET_DARK_ERG_1M, max.level = 2)## List of 3
## $ SA43 160 DARK:'data.frame': 24 obs. of 7 variables:
## ..$ Name : chr [1:24] "160 SA44 160" "160 SA44 160" "160 SA44 160" "160 SA44 160" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] 11.1 11.2 20.1 90.3 307.5 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## ..$ Genotype : Factor w/ 1 level "HET": 1 1 1 1 1 1 1 1 1 1 ...
## $ SA43 163 DARK:'data.frame': 24 obs. of 7 variables:
## ..$ Name : chr [1:24] "163 SA44 163" "163 SA44 163" "163 SA44 163" "163 SA44 163" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -5.29 15.47 12.85 110.4 426.2 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## ..$ Genotype : Factor w/ 1 level "HET": 1 1 1 1 1 1 1 1 1 1 ...
## $ SA43 164 DARK:'data.frame': 24 obs. of 7 variables:
## ..$ Name : chr [1:24] "164 SA44 164" "164 SA44 164" "164 SA44 164" "164 SA44 164" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -15.9 -12.1 26.6 112.2 375.4 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## ..$ Genotype : Factor w/ 1 level "HET": 1 1 1 1 1 1 1 1 1 1 ...Add a column for KO genotype
KO_DARK_ERG_1M <- DARK_ERG_1M[grep("159|161|162",names(DARK_ERG_1M))]
KO_genotype <- function(KO_ERG_list){
New_Genotype_column <- KO_ERG_list %>% cbind(Genotype='KO')
}
KO_DARK_ERG_1M <- map(KO_DARK_ERG_1M,KO_genotype)
str(KO_DARK_ERG_1M, max.level = 2)## List of 3
## $ SA43 159 DARK:'data.frame': 24 obs. of 7 variables:
## ..$ Name : chr [1:24] "159 SA44 159" "159 SA44 159" "159 SA44 159" "159 SA44 159" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -19.3 4.74 1.23 116.8 388.4 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## ..$ Genotype : Factor w/ 1 level "KO": 1 1 1 1 1 1 1 1 1 1 ...
## $ SA43 161 DARK:'data.frame': 24 obs. of 7 variables:
## ..$ Name : chr [1:24] "161 SA43 161" "161 SA43 161" "161 SA43 161" "161 SA43 161" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] 4.55 3.21 13.73 72.21 232 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## ..$ Genotype : Factor w/ 1 level "KO": 1 1 1 1 1 1 1 1 1 1 ...
## $ SA43 162 DARK:'data.frame': 24 obs. of 7 variables:
## ..$ Name : chr [1:24] "162 SA44 162" "162 SA44 162" "162 SA44 162" "162 SA44 162" ...
## ..$ S : num [1:24] 2 3 4 5 6 7 2 3 4 5 ...
## ..$ Eye : chr [1:24] "OS" "OS" "OS" "OS" ...
## ..$ wave : chr [1:24] "a" "a" "a" "a" ...
## ..$ uV : num [1:24] -5.889 38.5 -0.215 109.6 360.7 ...
## ..$ Intensity: num [1:24] -4 -3 -2 -1 0 1 -4 -3 -2 -1 ...
## ..$ Genotype : Factor w/ 1 level "KO": 1 1 1 1 1 1 1 1 1 1 ...Create a single dataframe containing all elements
Final_ERG_1M <- HET_DARK_ERG_1M %>% bind_rows(KO_DARK_ERG_1M)## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vectorFinal_ERG_1MPlot ERG graph for each mice
Final_ERG_1M %>%
ggplot(aes(x=Intensity, y=uV, group=Eye, color=Eye)) +
geom_point() + geom_line() + labs(x='Intensity (log cd.s/m2)',y='Amplitude (uV)') +
facet_wrap(~Name+wave, scales = "free_y")
Determine if there is a significant between a and b wave between heterozyguous and knockout mice
Final_ERG_1M %>% group_by(Genotype,wave)%>%
summarize(Mean_uV = mean(uV, na.rm=T), SEM_uV = sd(uV, na.rm=T)/sqrt(sum(!is.na(uV))))t test for a wave
t.test(uV ~ Genotype, data=Final_ERG_1M, subset = wave== 'a')##
## Welch Two Sample t-test
##
## data: uV by Genotype
## t = 0.53691, df = 68.357, p-value = 0.5931
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -71.75209 124.58398
## sample estimates:
## mean in group HET mean in group KO
## 178.2543 151.8384t.test for b wave
t.test(uV ~ Genotype, data=Final_ERG_1M, subset = wave== 'b')##
## Welch Two Sample t-test
##
## data: uV by Genotype
## t = 1.2442, df = 68.734, p-value = 0.2176
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -64.56811 278.56644
## sample estimates:
## mean in group HET mean in group KO
## 578.0617 471.0625