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So I'm trying to perform multiple t-tests (or ANOVA if that works too) against multiple interventions (compared to a control) at multiple concentrations.

Below is a mock-up of my data that I've gotten into long-form (but I have multiple rows for each sample and concentration). Ideally, it would run multiple t-tests comparing each test sample at each concentration to its respective control concentration. i.e. Sample A-2-Sample B-2 = 0.001, Sample A-4-SampleB-4 = 0.005, Sample A-16-Sample B-16, 0.01, Sample A-2-Sample C-2, = 0.967, etc. Comparisons between two test groups (e.g. Sample B-Sample C or Sample B-2-Sample B-4) would be irrelevant.

I have many data sets to do this to so I don't want to have to split them up manually. I've seen lots of examples creating t-tests with 2 variables but not 3. Is there a better way to handle this? Should I just do a three-way ANOVA and ignore what I don't need?

Name Control(y/n) Concentration Output
Sample A Control 2 0.123
Sample A Control 4 0.567
Sample A Control 16 1.075
Sample B Test 2 0.956
Sample B Test 4 5.435
Sample B Test 16 20.157
Sample C Test 2 0.354
Sample C Test 4 2.156
Sample C Test 16 2.569
Sample D Test 2 0.001
Sample D Test 4 0.231
Sample D Test 16 0.451
mikephel
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1 Answers1

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Answering my own question here. I actually figured out that, for my experiments, there wasn't equal variance between the different concentration groups. I ended up splitting them into individual dataframes, then running ANOVA on each separately, getting a list of p-values with Tukey's then recombining the list. Inevitably, there's a multiple comparisons problem I run into with that but it wouldn't have worked individually and I can always apply a separate post-hoc correction after. Code looked a little like the below. I could have used for loops for after the ANOVA steps but I didn't have many concentrations so I just didn't bother with it. It performs ANOVA by all permutations respective to each concentration, but I by selecting [1:4,] during the transform step, it only shows the p-values of the permutations containing Sample A.


##Using melt tutorial from here: https://stackoverflow.com/questions/2185252/reshaping-data-frame-from-wide-to-long-format
##Using ANOVA tutorial from here: http://www.sthda.com/english/wiki/two-way-anova-test-in-r

setwd("c:/R/STACKOVERFLOW")

library(data.table)
library(ggpubr)


sample <- read.csv("DATA.csv", check.names = FALSE, fileEncoding = 'UTF-8-BOM')

View(sample)

longo <- melt(setDT(sample), id.vars = c("Concentration"), variable.name = "Name")

View(longo)

ggboxplot(longo, x = "Concentration", y = "Output", color = "Name")

ggline(longo, x = "Concentration", y = "Output", color = "Name",
       add = c("mean_se", "dotplot"))

##Split these out so ANOVA can be performed on individual concentrations

ConSplit <- split(longo, f = longo$Concentration, drop = TRUE)

list2env(ConSplit,envir=.GlobalEnv)


#16

D16 <- as.data.frame(`16`)

A16<-aov(Output ~ Name, data = D16)

summary(A16)

T16 <- TukeyHSD(A16)

##Whatever, let's just make all of em


#4

D4 <- as.data.frame(`4`)

A4<-aov(Output ~ Name, data = D4)

summary(A4)

T4 <- TukeyHSD(A4)


#2

D2 <- as.data.frame(`2`)

A2<-aov(Output ~ Name, data = D2)

summary(A2)

T2 <- TukeyHSD(A2)


### LET'S OUTPUT THIS IN A USEABLE MANNER

data16 <- as.data.frame(T16[1])
data4 <- as.data.frame(T4[1])
data2 <- as.data.frame(T2[1])

#make dataframes for merge

M16 <- data.frame(Concentration = c(16))
M4 <- data.frame(Concentration = c(4))
M2 <- data.frame(Concentration = c(2))


#merge em 

B16 <- transform(merge(data16[1:4,],M16,by=0,all=TRUE), row.names=Row.names, Row.names=NULL)
B4 <- transform(merge(data4[1:4,],M4,by=0,all=TRUE), row.names=Row.names, Row.names=NULL)
B2 <- transform(merge(data2[1:4,],M2,by=0,all=TRUE), row.names=Row.names, Row.names=NULL)

#Stack em

AllTogetherNow <- rbind(B16,B4,B2)

#See em

View(AllTogetherNow)

#Write  em

write.csv(AllTogetherNow, "COMBINEDPVALS.csv")
mikephel
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