KDM_data_25_fisher

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library(dplyr)

## 
## 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

df <- read.csv("KDM_data_25.csv", stringsAsFactors = FALSE)
fisher_vs_A3 <- function(df, genotype_label) {
  d <- df %>% filter(Genotype == genotype_label)
  stopifnot("A3" %in% d$Mutation)
  A3 <- d %>% filter(Mutation == "A3") %>% slice(1)
  out_list <- lapply(setdiff(unique(d$Mutation), "A3"), function(mod) {
    mrow <- d %>% filter(Mutation == mod) %>% slice(1)
    mat <- matrix(
      c(mrow$Males, mrow$Females, A3$Males, A3$Females),
      nrow = 2, byrow = TRUE,
      dimnames = list(Mutation = c(mod, "A3"), Sex = c("M","F"))
    )
    ft_two  <- fisher.test(mat, alternative = "two.sided")
    ft_less <- fisher.test(mat, alternative = "less")  # modifier lower than LexA
    data.frame(
      Genotype      = genotype_label,
      Mutation          = mod,
      M_mod         = mrow$Males,
      F_mod         = mrow$Females,
      M_A3        = A3$Males,
      F_A3        = A3$Females,
      prop_mod      = mrow$Males / (mrow$Males + mrow$Females),
      prop_A3     = A3$Males / (A3$Males + A3$Females),
      OR_hat        = unname(ft_two$estimate),
      OR_LCL95      = ft_two$conf.int[1],
      OR_UCL95      = ft_two$conf.int[2],
      p_two_sided   = ft_two$p.value,
      p_one_less    = ft_less$p.value,
      stringsAsFactors = FALSE
    )
  })
  out_tbl <- bind_rows(out_list)
  out_tbl$p_FDR_two_sided <- p.adjust(out_tbl$p_two_sided, method = "BH")
  out_tbl$p_FDR_one_less  <- p.adjust(out_tbl$p_one_less,  method = "BH")
  out_tbl %>% arrange(p_FDR_one_less, p_one_less)
}
# Run separately for each genotype
fisher_HWT  <- fisher_vs_A3(df, "HWT")
fisher_K16R <- fisher_vs_A3(df, "K16R")
# Optional combined table
fisher_all <- bind_rows(fisher_HWT, fisher_K16R)
# Peek
fisher_HWT

fisher_K16R

# View(fisher_all)

```