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library(samr)
library(tidyverse)
# assumes modelling exploration is loaded in the R session
data1_sam <- data1 %>%
select(donor_id, outcome, Feature, value) %>%
mutate(outcome = factor(outcome, labels = c(1, 2))) %>%
pivot_wider(
names_from = Feature,
values_from = value
) %>%
select(-donor_id)
data1_sam_y <- data1_sam[['outcome']]
data1_sam_x <- t(as.data.frame(data1_sam[-1]))
samobj1 <- samr::SAM(
data1_sam_x,
data1_sam_y,
resp.type="Two class unpaired",
fdr.output = 0.5,
nperms = 1000,
genenames = rownames(data1_sam_x)
)
samobj1
############# DATASET 2
data2_sam <- data2 %>%
select(donor_id, outcome, Feature, value) %>%
mutate(outcome = factor(outcome, labels = c(1, 2))) %>%
pivot_wider(
names_from = Feature,
values_from = value
) %>%
select(-donor_id)
data2_sam_y <- data2_sam[['outcome']]
data2_sam_x <- t(as.data.frame(data2_sam[-1]))
samobj2 <- samr::SAM(
data2_sam_x,
data2_sam_y,
resp.type="Two class unpaired",
fdr.output = 0.01,
nperms = 1000,
genenames = rownames(data2_sam_x)
)
samobj2
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