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paired-wide-data.R
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paired-wide-data.R
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## Create data set in "wide" format, for easier:
## a) finding out best method
## b) finding out best possible performance
## c) paired t-tests et al.
## d) pairs plots (see pairs-plots.R)
rm(list = ls())
## library(dplyr)
library(data.table)
## Yes, we could load each, transform, rm, gc(), but except for
## one, the rest are not that big and makes my life a lot simpler.
## Created in makeTable-to-diff-wrt-null-2.R
load("genotype_diff_null.RData")
load("array_diff_null.RData")
load("genotype_diff_null_no_average.RData")
load("num_mut_diff_null.RData")
## Convert to data.table by reference
setDT(genotype_diff_null)
setDT(genotype_diff_null_no_average)
setDT(array_diff_null)
setDT(num_mut_diff_null)
## To have a common set of columns that are used below
## Verify first
stopifnot(is.null(genotype_diff_null$replicate))
genotype_diff_null$replicate <- NA
stopifnot(is.null(array_diff_null$sourceGenotype))
stopifnot(is.null(array_diff_null$sourceGenotype_nMut))
stopifnot(is.null(array_diff_null$sourceGenotype_accessible))
stopifnot(is.null(array_diff_null$replicate))
array_diff_null$sourceGenotype <- "any"
array_diff_null$sourceGenotype_nMut <- NA
array_diff_null$sourceGenotype_accessible <- NA
array_diff_null$replicate <- NA
## partial matching, though we could use more idiomatic DT expressions
stopifnot(is.null(num_mut_diff_null[["sourceGenotype"]]))
stopifnot(is.null(num_mut_diff_null$sourceGenotype_accessible))
stopifnot(is.null(num_mut_diff_null$replicate))
num_mut_diff_null$sourceGenotype <- NA
num_mut_diff_null$sourceGenotype_accessible <- NA
num_mut_diff_null$replicate <- NA
## df where columns are response vars in each method
## Helpful for paired t-tests and paired plots of method results
f_wide_out <- function(x) {
## This is VERT hackish, but things are tailored for the different
## input data
nn <- deparse(substitute(x))
if(nn == "array_diff_null") input <- "array"
if(nn == "genotype_diff_null") input <- "genotype"
if(nn == "genotype_diff_null_no_average") input <- "genotype_no_average"
if(nn == "num_mut_diff_null") input <- "num_mut"
if(input == "array") {
stopifnot(is.na(x$sourceGenotype_nMut))
stopifnot(is.na(x$sourceGenotype_accessible))
}
if(input == "genotype") {
stopifnot(!is.na(x$sourceGenotype_nMut))
## accessible is sometimes NA
## double check this
naaccess <- which(is.na(x$sourceGenotype_accessible))
## We are dealing with the data we think
stopifnot(length(naaccess) < nrow(x))
## When accessible is na the js is na too. Those are
## failures in analysis
nanjs <- which(is.nan(x$js))
stopifnot(identical(nanjs, naaccess))
}
## ## Figure out the data we are dealing with
## ## but check our guess is correct
## ## More checks later when we look at column names
## if(all(x$sourceGenotype == "any")) {
## input <- "array"
## stopifnot(is.na(x$sourceGenotype_nMut))
## stopifnot(is.na(x$sourceGenotype_accessible))
## } else if (all(is.na(x$sourceGenotype))) {
## input <- "num_mut"
## } else if (all(is.na(x$sourceGenotype)) &&
## ) {
## input <- "genotype"
## stopifnot(!is.na(x$sourceGenotype_nMut))
## ## accessible is sometimes NA
## ## double check this
## naaccess <- which(is.na(x$sourceGenotype_accessible))
## ## We are dealing with the data we think
## stopifnot(length(naaccess) < nrow(x))
## ## When accessible is na the js is na too. Those are
## ## failures in analysis
## nanjs <- which(is.nan(x$js))
## stopifnot(identical(nanjs, naaccess))
## }
common_columns <- c(
## Columns to match
"id", "sourceGenotype",
"detect", "size_split",
"sourceGenotype_nMut",
"replicate",
## Redundant, but keep to check
"rnst",
## Descriptors
"numGenes", "initSize",
"typeLandscape", "mutationRate",
## Genotype descriptors
## "sourceGenotype_accessible", ## FIXME: wrong!!! accessible is a method property ## see bottom
## Landscape and sample stats
"sampledGenotypesDiversity",
"sampledGenotypesNumber",
"sampledGenotypesGenesAbove_0.01",
"sampledGenotypesGenesAbove_0.1",
"numMuts_mean",
"numMuts_median", "numMuts_var",
"numMuts_stDev", "numMuts_kurtosis",
"numMuts_skewness", "pom_h",
"lod_h", "freq_most_freq_mean_no450",
"how_many_gt_5p_mean_no450",
"numAccessibleGenotypes",
"numLocalPeaks",
"numObservedPeaks",
"diversityObservedPeaks",
"epistMagn",
"epistSign", "epistRSign",
"w1", "w2",
"w3", "gamma")
if(input == "array") {
response_columns <- c("js_w_real", "js_w_sampl",
"js_w_real_diff", "js_w_sampl_diff")
common_columns <- c(common_columns,
"js_w_real_null",
"js_w_sampl_null"
)
stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
}
if(input == "num_mut") {
response_columns <- c("js_w_real", "js_w_sampl",
"js_w_real_diff", "js_w_sampl_diff")
common_columns <- c(common_columns,
"js_w_real_null",
"js_w_sampl_null"
)
stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
}
if(input == "genotype") {
response_columns <- c("js", "js_diff")
common_columns <- c(common_columns,
"sampledFreq",
"sampledFreq_unique",
"sampledProp",
"sampledProp_unique",
"trueFreq",
"trueFreq_unique",
"trueProp",
"trueProp_unique",
"observedFreq",
"observedFreq_unique",
"observedProp",
"observedProp_unique",
"fitnessRank",
"fitnessRank_unique",
"fitnessRankNoZero",
"fitnessRankNoZero_unique",
"freqLocalMax",
"freqLocalMax_unique",
"propLocalMax",
"propLocalMax_unique",
"sourceGenotype_accessible_unique",
"js_null"
)
stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
}
if(input == "genotype_no_average") {
response_columns <- c("js", "js_diff")
common_columns <- c(common_columns,
"sampledFreq",
"sampledProp",
"trueFreq",
"trueProp",
"observedFreq",
"observedProp",
"fitnessRank",
"fitnessRankNoZero",
"freqLocalMax",
"propLocalMax",
"js_null"
)
stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
}
x <- x[method != "null", ]
x$method <- factor(x$method,
levels=c(
"MHN_td",
"CBN_td",
"MCCBN_td",
"MHN",
"CBN",
"MCCBN",
"CBN_uw",
"MCCBN_uw",
"CAPRI_AIC",
"CAPRI_BIC",
"OT",
"OT_uw",
"CAPRESE"
)
)
data_tmp_s <- split(x, x$method)
## Instead of split, we could process using by? Too much of a mess?
## Obtain dfs with only the response and identifiers
## but rename columns
## js, js_eq, js_diff, js_eq_diff
## id, sourceGenotype,
## detect, size_split
## method
## To check: rnst
## Unique output
unique_out_cols <- c(
## Method
"method",
## Columns to match
"id", "sourceGenotype", "detect", "size_split",
"sourceGenotype_nMut",
"replicate",
## Redundant, for check
"rnst",
## Responses
response_columns
)
data_tmp_s_unique <-
lapply(data_tmp_s,
function(x) {
tmp <- x[, ..unique_out_cols]
## Rename response columns
colnames(tmp)[c(9:(9 + length(response_columns) - 1))] <-
paste0(unique(tmp$method), "_",
colnames(tmp)[c(9:(9 + length(response_columns) - 1))])
## No need for method anymore
tmp[, method:=NULL]
return(tmp)
}
)
## Extract common columns. Any method will do for that
data_tmp_common <- data_tmp_s[[1]][, ..common_columns]
## Join all method tables
all_m <- Reduce(
function(...) merge.data.table(..., by = c("id", "sourceGenotype",
"detect", "size_split",
"sourceGenotype_nMut",
"replicate",
"rnst" ## ok, using it
## to avoid warnings from DT
)),
data_tmp_s_unique)
## Checks
dim(all_m)
nrows <- lapply(data_tmp_s_unique, nrow)
stopifnot(length(unique(nrows)) == 1)
stopifnot(unique(nrows) == nrow(all_m))
## ## More checks: use rnst
## dummy2 <- all_m[, grep("rnst", colnames(all_m), fixed = TRUE)]
## for(i in 2:ncol(dummy2)) stopifnot(dummy2[, 1] == dummy2[, i])
## rm(dummy2)
## gc()
## rm the rnst columns
## all_m <- all_m[, -(grep("rnst", colnames(all_m), fixed = TRUE)[-1])]
stopifnot(nrow(all_m) == nrow(data_tmp_common))
## Join the method-specific output with the common columns
all_m_c <- merge(all_m, data_tmp_common,
by = c("id", "sourceGenotype",
"detect", "size_split",
"sourceGenotype_nMut",
"replicate",
"rnst"
))
stopifnot(nrow(all_m) == nrow(all_m_c))
## dummy2 <- all_m_c[, grep("rnst", colnames(all_m_c), fixed = TRUE)]
## stopifnot(dummy2[, 1] == dummy2[, 2])
## rm(dummy2)
gc()
return(all_m_c)
}
wide_array <- f_wide_out(array_diff_null)
wide_genotype <- f_wide_out(genotype_diff_null)
wide_num_mut <- f_wide_out(num_mut_diff_null)
wide_genotype_no_average <- f_wide_out(genotype_diff_null_no_average)
save(file = "wide_array.RData", wide_array, compress = FALSE)
save(file = "wide_genotype.RData", wide_genotype, compress = FALSE)
save(file = "wide_genotype_no_average.RData",
wide_genotype_no_average, compress = FALSE)
save(file = "wide_num_mut.RData", wide_num_mut, compress = FALSE)
## This gives and error. No problem I want it to stop
stop()
## ## Former dplyr code
## ## To have a common set of columns that are used below
## ## Verify first
## stopifnot(is.null(genotype_diff_null$replicate))
## genotype_diff_null$replicate <- NA
## stopifnot(is.null(array_diff_null$sourceGenotype))
## stopifnot(is.null(array_diff_null$sourceGenotype_nMut))
## stopifnot(is.null(array_diff_null$sourceGenotype_accessible))
## stopifnot(is.null(array_diff_null$replicate))
## array_diff_null$sourceGenotype <- "any"
## array_diff_null$sourceGenotype_nMut <- NA
## array_diff_null$sourceGenotype_accessible <- NA
## array_diff_null$replicate <- NA
## ## partial matching
## stopifnot(is.null(num_mut_diff_null[["sourceGenotype"]]))
## stopifnot(is.null(num_mut_diff_null$sourceGenotype_accessible))
## stopifnot(is.null(num_mut_diff_null$replicate))
## num_mut_diff_null$sourceGenotype <- NA
## num_mut_diff_null$sourceGenotype_accessible <- NA
## num_mut_diff_null$replicate <- NA
## f_wide_out <- function(x) {
## ## This is VERT hackish, but things are tailored for the different
## ## input data
## nn <- deparse(substitute(x))
## if(nn == "array_diff_null") input <- "array"
## if(nn == "genotype_diff_null") input <- "genotype"
## if(nn == "genotype_diff_null_no_average") input <- "genotype_no_average"
## if(nn == "num_mut_diff_null") input <- "num_mut"
## if(input == "array") {
## stopifnot(is.na(x$sourceGenotype_nMut))
## stopifnot(is.na(x$sourceGenotype_accessible))
## }
## if(input == "genotype") {
## stopifnot(!is.na(x$sourceGenotype_nMut))
## ## accessible is sometimes NA
## ## double check this
## naaccess <- which(is.na(x$sourceGenotype_accessible))
## ## We are dealing with the data we think
## stopifnot(length(naaccess) < nrow(x))
## ## When accessible is na the js is na too. Those are
## ## failures in analysis
## nanjs <- which(is.nan(x$js))
## stopifnot(identical(nanjs, naaccess))
## }
## ## ## Figure out the data we are dealing with
## ## ## but check our guess is correct
## ## ## More checks later when we look at column names
## ## if(all(x$sourceGenotype == "any")) {
## ## input <- "array"
## ## stopifnot(is.na(x$sourceGenotype_nMut))
## ## stopifnot(is.na(x$sourceGenotype_accessible))
## ## } else if (all(is.na(x$sourceGenotype))) {
## ## input <- "num_mut"
## ## } else if (all(is.na(x$sourceGenotype)) &&
## ## ) {
## ## input <- "genotype"
## ## stopifnot(!is.na(x$sourceGenotype_nMut))
## ## ## accessible is sometimes NA
## ## ## double check this
## ## naaccess <- which(is.na(x$sourceGenotype_accessible))
## ## ## We are dealing with the data we think
## ## stopifnot(length(naaccess) < nrow(x))
## ## ## When accessible is na the js is na too. Those are
## ## ## failures in analysis
## ## nanjs <- which(is.nan(x$js))
## ## stopifnot(identical(nanjs, naaccess))
## ## }
## common_columns <- c(
## ## Columns to match
## "id", "sourceGenotype",
## "detect", "size_split",
## "sourceGenotype_nMut",
## "replicate",
## ## Redundant, but keep to check
## "rnst",
## ## Descriptors
## "numGenes", "initSize",
## "typeLandscape", "mutationRate",
## ## Genotype descriptors
## "sourceGenotype_accessible",
## ## Landscape and sample stats
## "sampledGenotypesDiversity",
## "sampledGenotypesNumber",
## "sampledGenotypesGenesAbove_0.01",
## "sampledGenotypesGenesAbove_0.1",
## "numMuts_mean",
## "numMuts_median", "numMuts_var",
## "numMuts_stDev", "numMuts_kurtosis",
## "numMuts_skewness", "pom_h",
## "lod_h", "freq_most_freq_mean_no450",
## "how_many_gt_5p_mean_no450",
## "numAccessibleGenotypes",
## "numLocalPeaks",
## "numObservedPeaks",
## "diversityObservedPeaks",
## "epistMagn",
## "epistSign", "epistRSign",
## "w1", "w2",
## "w3", "gamma")
## if(input == "array") {
## response_columns <- c("js_w_real", "js_w_sampl",
## "js_w_real_diff", "js_w_sampl_diff")
## common_columns <- c(common_columns,
## "js_w_real_null",
## "js_w_sampl_null"
## )
## stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
## stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
## }
## if(input == "num_mut") {
## response_columns <- c("js_w_real", "js_w_sampl",
## "js_w_real_diff", "js_w_sampl_diff")
## common_columns <- c(common_columns,
## "js_w_real_null",
## "js_w_sampl_null"
## )
## stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
## stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
## }
## if(input == "genotype") {
## response_columns <- c("js", "js_diff")
## common_columns <- c(common_columns,
## "sampledFreq",
## "sampledFreq_unique",
## "sampledProp",
## "sampledProp_unique",
## "trueFreq",
## "trueFreq_unique",
## "trueProp",
## "trueProp_unique",
## "observedFreq",
## "observedFreq_unique",
## "observedProp",
## "observedProp_unique",
## "fitnessRank",
## "fitnessRank_unique",
## "fitnessRankNoZero",
## "fitnessRankNoZero_unique",
## "freqLocalMax",
## "freqLocalMax_unique",
## "propLocalMax",
## "propLocalMax_unique",
## "sourceGenotype_accessible",
## "sourceGenotype_accessible_unique",
## "diversityObservedPeaks",
## "js_null"
## )
## stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
## stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
## }
## if(input == "genotype_no_average") {
## response_columns <- c("js", "js_diff")
## common_columns <- c(common_columns,
## "sampledFreq",
## "sampledProp",
## "trueFreq",
## "trueProp",
## "observedFreq",
## "observedProp",
## "fitnessRank",
## "fitnessRankNoZero",
## "freqLocalMax",
## "propLocalMax",
## "sourceGenotype_accessible",
## "diversityObservedPeaks",
## "js_null"
## )
## stopifnot(length(setdiff(response_columns, colnames(x))) == 0)
## stopifnot(length(setdiff(common_columns, colnames(x))) == 0)
## }
## x <- dplyr::filter(x, method != "null")
## x$method <- factor(x$method,
## levels=c(
## "MHN_td",
## "CBN_td",
## "MCCBN_td",
## "MHN",
## "CBN",
## "MCCBN",
## "CBN_uw",
## "MCCBN_uw",
## "CAPRI_AIC",
## "CAPRI_BIC",
## "OT",
## "OT_uw",
## "CAPRESE"
## )
## )
## data_tmp_s <- split(x, x$method)
## ## Obtain dfs with only the response and identifiers
## ## but rename columns
## ## js, js_eq, js_diff, js_eq_diff
## ## id, sourceGenotype,
## ## detect, size_split
## ## method
## ## To check: rnst
## ## Unique output
## data_tmp_s_unique <-
## lapply(data_tmp_s,
## function(x) {
## tmp <- x[, c(
## ## Method
## "method",
## ## Columns to match
## "id", "sourceGenotype", "detect", "size_split",
## "sourceGenotype_nMut",
## "replicate",
## ## Redundant, for check
## "rnst",
## ## Responses
## response_columns
## )]
## ## Rename response columns
## colnames(tmp)[c(9:(9 + length(response_columns) - 1))] <-
## paste0(unique(tmp$method), "_",
## colnames(tmp)[c(9:(9 + length(response_columns) - 1))])
## ## No need for method anymore
## tmp <- tmp[, -1]
## return(tmp)
## }
## )
## ## Extract common columns. Any method will do for that
## data_tmp_common <- data_tmp_s[[1]][, common_columns]
## ## Join all method tables
## all_m <- Reduce(function(...) left_join(..., by = c("id", "sourceGenotype",
## "detect", "size_split",
## "sourceGenotype_nMut",
## "replicate"
## )),
## data_tmp_s_unique)
## ## Checks
## dim(all_m)
## nrows <- lapply(data_tmp_s_unique, nrow)
## stopifnot(length(unique(nrows)) == 1)
## stopifnot(unique(nrows) == nrow(all_m))
## ## More checks: use rnst
## dummy2 <- all_m[, grep("rnst", colnames(all_m), fixed = TRUE)]
## for(i in 2:ncol(dummy2)) stopifnot(dummy2[, 1] == dummy2[, i])
## rm(dummy2)
## gc()
## ## rm the rnst columns
## all_m <- all_m[, -(grep("rnst", colnames(all_m), fixed = TRUE)[-1])]
## stopifnot(nrow(all_m) == nrow(data_tmp_common))
## ## Join the method-specific output with the common columns
## all_m_c <- dplyr::full_join(all_m, data_tmp_common,
## by = c("id", "sourceGenotype",
## "detect", "size_split",
## "sourceGenotype_nMut",
## "replicate"
## ))
## stopifnot(nrow(all_m) == nrow(all_m_c))
## dummy2 <- all_m_c[, grep("rnst", colnames(all_m_c), fixed = TRUE)]
## stopifnot(dummy2[, 1] == dummy2[, 2])
## rm(dummy2)
## gc()
## return(all_m_c)
## }
## FIXME: the "sourceGenotype_accessible" issue
## It turns out that for the wide, we have been using the value from
## MHN. These can be verified this way
## load("genotype_diff_null_no_average.RData")
## load("wide_genotype_no_average_rank.RData")
## aggregate(sourceGenotype_accessible ~ method,
## FUN = function(x) sum(x, na.rm = TRUE),
## data = genotype_diff_null_no_average)
## ## method sourceGenotype_accessible
## ## 1 CAPRESE 1449137
## ## 2 CAPRI_AIC 757353
## ## 3 CAPRI_BIC 939277
## ## 4 CBN 1348033
## ## 5 CBN_td 1348033
## ## 6 CBN_uw 1348033
## ## 7 MCCBN 1043920
## ## 8 MCCBN_td 1043920
## ## 9 MCCBN_uw 1043920
## ## 10 MHN 2618524
## ## 11 MHN_td 2618524
## ## 12 null 200592
## ## 13 OT 1207031
## ## 14 OT_uw 1207031
## sum(wide_genotype_no_average_rank$sourceGenotype_accessible, na.rm = TRUE)
## ## [1] 2618524