Question about filtering methylation data using sesame

[wolfs4e]

Hi, I now have methylation data generated from the Mammalian methyl array. I'm learning how to normalize this data using sesame, and I have a few questions. I'm generally following this tutorial from Wilkinson, Horvath, and Adams, but I'm getting some errors. I'm wondering whether this is because sesame has since been updated.

I've been able to generate the normalized beta values but cannot figure out how to generate a detection p-value r data file. The tutorial suggests that I do this (below), but pval is not a function within sesame. Do you have any advice on how to do this? Furthermore, should I just rely on masking by pOOBAH for filtering, or should I use the detection p-value dataframe to filter probes in a different way? Thanks for any advice you can give.

Cheers,
Sarah

SeSaMe also computes detection p values for each probe, which you can obtain into a data frame using the code below.

# First have to read all the samples into a list of sigsets because that is the data structure sesame operates with.
# We can't use the openSesame pipeline because we have to actually get the underlying sigset, not directly a data frame of beta values
ssets <- sample_sheet %>% 
  select(Basename) %>% 
  as_vector %>% 
  unname %>% 
  map(~readIDATpair(., 'custom', manifest_sesame)) 

# Constructing a detection p value data frame sample by sample
detection_P_values_sesame <- tibble(CGid = manifest_sesame$Probe_ID)

for (i in c(1:length(sample_sheet$idat_name))) {
  sample_ID <- sample_sheet$idat_name[i]
  sesame_p_vals <- ssets[[i]] %>% 
    pOOBAH %>% 
    noob %>%
    dyeBiasCorrTypeINorm %>% 
    pval %>% 
    enframe(name="CGid", value="sesame_pval") 
  colnames(sesame_p_vals)[2] = sample_ID
  detection_P_values_sesame <- left_join(detection_P_values_sesame, sesame_p_vals, by="CGid")
}