R/adjAF.R
In hendriau/Summix: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data
Defines functions
adjAF_calc
adjAF
Documented in
adjAF
adjAF_calc
#' adjAF
#'
#' @description
#' Adjusts allele frequencies for heterogeneous populations in genetic data given proportion of reference groups
#'
#' @param data dataframe of unadjusted allele frequency for observed group, K reference group allele frequencies for N SNPs
#' @param reference character vector of the column names for K reference groups.
#' @param observed character value for the column name of observed data group
#' @param pi.target numeric vector of the mixture proportions for K reference groups in the target individual or group.
#' @param pi.observed numeric vector of the mixture proportions for K reference groups in the observed group.
#' @param adj_method user choice of method for the allele frequency adjustment: options "average" and "leave_one_out" are available. Defaults to "average".
#' @param N_reference numeric vector of the sample sizes for each of the K reference groups.
#' @param N_observed numeric value of the sample size of the observed group.
#' @param filter sets adjusted allele frequencies equal to 1 if > 1, to 0 if > -.005 and < 0, and removes adjusted allele frequencies < -.005.
#'
#'
#' @return pi: table of input reference groups, pi.observed, and pi.target
#' @return observed.data: name of the data column for the observed group from which adjusted allele frequency is estimated
#' @return Nsnps: number of SNPs for which adjusted AF is estimated
#' @return adjusted.AF: data frame of original data with an appended column of adjusted allele frequencies
#' @return effective.sample.size: The sample size of individuals effectively represented by the adjusted allele frequencies
#' @importFrom stats "na.omit"
#' @importFrom utils "tail"
#'
#'
#' @author Adelle Price, \email{adelle.price@cuanschutz.edu}
#' @author Hayley Wolff, \email{hayley.wolff@cuanschutz.edu}
#' @author Audrey Hendricks, \email{audrey.hendricks@cuanschutz.edu}
#' @references https://github.com/hendriau/Summix2
#' @keywords genetics, mixture distribution, admixture, population stratification
#'
#' @seealso \url{https://github.com/hendriau/Summix2} for further documentation.
#'
#'
#' @examples
#' data(ancestryData)
#' adjusted_data<-adjAF(data = ancestryData,
#' reference = c("reference_AF_afr", "reference_AF_eur"),
#' observed = "gnomad_AF_afr",
#' pi.target = c(1, 0),
#' pi.observed = c(.85, .15),
#' adj_method = 'average',
#' N_reference = c(704,741),
#' N_observed = 20744,
#' filter = TRUE)
#' adjusted_data$adjusted.AF[1:5,]
#'
#'
#'
#' @export
adjAF <- function(data,
reference,
observed,
pi.target,
pi.observed,
adj_method = 'average',
N_reference = NULL,
N_observed = NULL,
filter = TRUE) {
#check for correct format of user inputs
if(length(N_reference) != length(pi.observed)){
stop("ERROR: Please make sure that the lengths of N_reference and pi.observed match.")
}
if(length(pi.target) != length(pi.observed)){
stop("ERROR: Please make sure that the lengths of pi.target and pi.observed match.")
}
if(!is(object = data, class2 = "data.frame")){
stop("ERROR: data must be a data.frame as described in the vignette")
}
if(typeof(observed)!="character"){
stop("ERROR: please enter the column name of the observed group")
}
if( !( observed %in% names(data) ) ){
stop("ERROR: please make sure that the observed group is included in the column names of the data.")
}
if( !all(reference %in% names(data) ) ){
stop("ERROR: Please make sure that all groups in reference are also column names in data")
}
if(typeof(reference)!="character"){
stop("ERROR: please enter the column names for the reference groups")
}
if( !(all(pi.observed<=1) & all(pi.target<=1)) | !(all(pi.observed>=0) & all(pi.target>=0)) ){
stop("ERROR: pi.observed and pi.target must contain ratios between 0 and 1.")
}
#Calculate average fold change of pi.observed to pi.target for across all reference groups
ave_fold_change = mean(abs(((pi.target+.001)-(pi.observed+.001))/(pi.observed+.001)))
#print(paste0('Average fold change between observed and target group proportions is: ', round(ave_fold_change, 2)))
#if average fold change is greater than 3 and less than or equal to 5, print warning
if (ave_fold_change > 3){
print("Warning: The average fold change between the observed and target group proportions is greater than 3. The effective sample size of the adjusted AF is likely substantially lower than the observed sample size.")
}
# #if average fold change is greater than 5, print warning. AF adjustment with average fold change of pi.observed to pi.target greater than 5 is not recommended
# if (ave_fold_change > 5){
# warning("The average fold change between the observed and target group proportions is greater than 5. It is not recommended to perform this AF adjustment.")
# }
fold_change = (floor(sum(pmin(pi.observed, pi.target)*N_observed + N_reference*min(1,pi.target*length(pi.target))))*pi.target - (N_observed*pi.observed+N_reference))/(N_observed*pi.observed+N_reference)
fold_change_refs = c()
large_fold_change=FALSE
#Iteratively check every pi.observed to pi.target fold change. If a given fold change is larger than 10, print warning showing which reference groups have too large of fold changes
for (i in 1:length(N_reference)){
if (fold_change[i] >= 10){
fold_change_refs = append(fold_change_refs, reference[i])
large_fold_change=TRUE
}
}
if (large_fold_change==TRUE){
warning(paste0('Based on the observed group and reference group sample sizes, the observed to target group proportion increase for the ', list(fold_change_refs),' group is too large. ', 'This AF adjustment is not recommended. The effective sample size of the adjusted AF is likely substantially lower than the observed sample size.'))
}
#check the effective sample size of the adjusted AF, if less than 50% of observed sample size, print warning
if(adj_method == "average"){
eff_samp_size = floor(sum(pmin(pi.observed, pi.target)*N_observed + N_reference*(pi.target)))
}
if(adj_method == "leave_one_out"){
eff_samp_size = floor(sum(pmin(tail(pi.observed, 1), tail(pi.target, 1))*N_observed + sum(as.vector((N_reference[1:length(N_reference)-1])*(pi.target[1:length(pi.target)-1])))))
}
cat('\n')
if (eff_samp_size < .5*N_observed){
warning('Your effective sample size of the adjusted alelle frequency is less than 50% of the observed sample size.')
}
allResults <- data.frame(matrix(nrow = nrow(data), ncol = length(reference)))
#If user has selected 'average' method for AF adjustment (this is default)
if (adj_method == "average"){
##when method is ave
if(length(reference) != length(pi.target)){
stop("ERROR: Please make sure that you are entering k groups in reference.")
}
for(i in 1:length(reference)) {
#Iteratively leave each reference group out and calculate adjusted AF
temp <- pi.target[i]
s.targ <- pi.target[-i]
s.targ <- c(s.targ, temp)
temp <- pi.observed[i]
s.obs <- pi.observed[-i]
s.obs <- c(s.obs, temp)
res <- invisible(adjAF_calc(data = data,
reference = reference[-i],
observed = observed,
pi.target = s.targ,
pi.observed = s.obs))
allResults[,i] <- res$adjusted.AF$adjustedAF
}
#Average all adjusted AFs that leave one reference group out at a time to get final adjusted AF
allResults$mean <- apply(allResults, 1, mean)
#if user has set filter == TRUE (this is default), remove negative values less than -.005 from final adjusted AFs, set adjusted AFs less than 0 and greater than -.005 to 0, and set adjusted AFs greater than 1 to 1
if (filter == TRUE){
before_filt_negative = length(allResults$mean)
before_filt_less = length(allResults$mean[allResults$mean < 0 & allResults$mean > -0.005])
before_filt_greater = length(allResults$mean[allResults$mean > 1])
allResults$mean[allResults$mean > 1] <- 1
allResults$mean[allResults$mean < 0 & allResults$mean > -0.005] <- 0
allResults$mean[allResults$mean <= -0.005] <- NA
#Let user know how many adjusted AFs were set to 0, 1, or removed from the final adjusted AF dataframe
cat('\n')
print(paste0("Note: In this AF adjustment, ", before_filt_less, " SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. ", before_filt_greater, " SNPs (with adjusted AF > 1) were rounded to 1, and ", length(allResults$mean[allResults$mean <= -0.005]), " SNPs (with adjusted AF <= -.005) were removed from the final results."))
cat('\n')
}
res$adjusted.AF$adjustedAF <- allResults$mean
#remove any NA's in dataframe (there will only be NA's if user has set filter ==TRUE and the adjusted AFs less than -.005 were set as NA in the previous code chunk)
res$adjusted.AF <- na.omit(res$adjusted.AF)
res$Nsnps <- length(res$adjusted.AF$adjustedAF)
res$effective.sample.size <- eff_samp_size
}
#If user has selected 'leave_one_out' method for AF adjustment
if (adj_method == "leave_one_out"){
##when method is LO
if(length(reference) != (length(pi.target)-1)){
stop("ERROR: Please make sure that you are entering k-1 groups in reference.")
}
res <- invisible(adjAF_calc(data = data,
reference = reference,
observed = observed,
pi.target = pi.target,
pi.observed = pi.observed))
LO_res <- as.vector(t(res$adjusted.AF$adjustedAF))
#if user has set filter == TRUE (this is default), remove negative values less than -.005 from final adjusted AFs, set adjusted AFs less than 0 and greater than -.005 to 0, and set adjusted AFs greater than 1 to 1
if (filter == TRUE){
before_filt_negative = length(LO_res)
before_filt_less = length(LO_res[LO_res < 0 & LO_res > -0.005])
before_filt_greater = length(LO_res[LO_res > 1])
LO_res[LO_res > 1] <- 1
LO_res[LO_res < 0 & LO_res > -0.005] <- 0
LO_res[LO_res <= -0.005] <- NA
#Let user know how many adjusted AFs were set to 0, 1, or removed from the final adjusted AF dataframe
cat('\n')
print(paste0("Note: In this AF adjustment, ", before_filt_less, " SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. ", before_filt_greater, " SNPs (with adjusted AF > 1) were rounded to 1, and ", length(res$adjusted.AF$adjustedAF[res$adjusted.AF$adjustedAF <= -0.005]), " SNPs (with adjusted AF <= -.005) were removed from the final results."))
cat('\n')
}
res$adjusted.AF$adjustedAF <- LO_res
#remove any NA's in dataframe (there will only be NA's if user has set filter ==TRUE and the adjusted AFs less than -.005 were set as NA in the previous code chunk)
res$adjusted.AF <- na.omit(res$adjusted.AF)
res$Nsnps <- length(res$adjusted.AF$adjustedAF)
res$effective.sample.size <- eff_samp_size
}
if(adj_method == "leave_one_out"){
reference = append(reference, "Left Out")
}
print(noquote('$pi'))
print(cbind.data.frame('ref.group' = reference, 'pi.observed' = pi.observed, 'pi.target' = pi.target))
cat('\n')
print(noquote('$observed.data'))
print(paste0("observed AF data to update: ", "'", observed, "'"))
cat('\n')
print(noquote('$Nsnps'))
print(length(res$adjusted.AF$adjustedAF))
cat('\n')
cat('\n')
print(noquote('$effective.sample.size'))
print(res$effective.sample.size)
cat('\n')
cat('\n')
print("use $adjusted.AF$adjustedAF to see adjusted AF data")
cat('\n')
cat('\n')
print("Note: The accuracy of the AF adjustment is likely lower for rare variants (< .5%).")
return(res)
}
#' adjAF_calc
#'
#' @description
#' Helper function for calculating allele frequencies for heterogeneous populations in genetic data given proportion of reference groups
#'
#' @param data dataframe of unadjusted allele frequency for observed group, K-1 reference group allele frequencies for N SNPs
#' @param reference character vector of the column names for K-1 reference groups. The name of the last reference group is not included as that group is not used to estimate the adjusted allele frequencies.
#' @param observed character value for the column name of observed data group
#' @param pi.target numeric vector of the mixture proportions for K reference groups in the target sample or subject. The order must match the order of the reference columns with the last entry matching the missing reference group.
#' @param pi.observed numeric vector of the mixture proportions for K reference groups for the observed group. The order must match the order of the reference columns with the last entry matching the missing reference group.
#'
#' @return pi: table of input reference groups, pi.observed, and pi.target
#' @return observed.data: name of the data column for the observed group from which adjusted allele frequency is estimated
#' @return Nsnps: number of SNPs for which adjusted AF is estimated
#' @return adjusted.AF: data frame of original data with an appended column of adjusted allele frequencies
#' @importFrom tidyselect "all_of"
#' @importFrom dplyr "select"
#'
#' @export
#NOTE: this is the summix v1 adjAF code, now used as a helper function
adjAF_calc <- function(
data ,
reference ,
observed ,
pi.target ,
pi.observed ){
k = length(reference) + 1
#Normalize pi. We need the pi_reference and pi.target to sum to 1
pi.target <- pi.target/sum(pi.target)
pi.observed <- pi.observed/sum(pi.observed)
#sum the reference group allele frequencies multiplied by pi.target and name it 'hatted'
hatted <- vector(mode = "double", length = dim(data)[1])
#sum up the reference group allele frequencies multiplied by pi_reference and name it 'starred'
starred <- vector(mode = "double", length = dim(data)[1])
#sum the K-1 reference groups multiplied by pi.observed.
#sum the k-1 reference groups multiplied by pi.target.
for (j in 1:(k - 1)) {
hatted <- hatted + (pi.observed[j] * data %>% select(reference[j]))
starred <- starred + (pi.target[j] * data %>% select(reference[j]))
}
#check pi.observed for the kth reference group is greater than 0 before dividing pi.target by pi.observed
if (pi.observed[k]>0){
data$adjustedAF <- ((pi.target[k]/pi.observed[k]) * (data %>% select(all_of(observed)) - hatted) + starred)
#if pi.observed is not greater than 0, set pi.target/pi.observed equal to 0
}else{
data$adjustedAF <- ((data %>% select(all_of(observed)) - hatted) + starred)
}
data.out <- data
observed.data=paste("observed data to update AF: '", observed, "'", sep="")
pi_table<-data.frame(ref.group=c(reference, "NONE"), pi.observed=pi.observed, pi.target=pi.target)
Nsnps=nrow(data)
tmp.out<-list("pi"=pi_table, "observed.data"=observed.data, "Nsnps"=Nsnps, "adjusted.AF"=data.out, "effective.sample.size"="0")
return(tmp.out)
}
#' calc_effective_N
#'
#' @description
#' Helper function to calculate effective sample size for the group that is left out when estimating the adjusted allele frequencies in each adjAF function iteration.
#
#' @param N_reference numeric vector of the sample sizes of each K reference groups.
#' @param N_observed numeric value of the sample size of the observed group.
#' @param pi.target numeric vector of the mixture proportions for K reference groups in the target sample or subject. The order must match the order of the reference columns with the last entry matching the missing reference group.
#' @param pi.observed numeric vector of the mixture proportions for K reference groups for the observed group. The order must match the order of the reference columns with the last entry matching the missing reference group.
#'
#' @return N_effective: effective sample size for the group that is left out when estimating the adjusted allele frequencies in each adjAF function iteration.
#'
#' @export
calc_effective_N <- function (N_reference, N_observed, pi.target, pi.observed){
#one reference group is left out for each calculation of N_effective, must ensure k is equal to the total number of reference groups in the AF adjustment
k = length(N_reference) + 1
#ensure pi.target and pi.observed are normalized
pi.target <- pi.target/sum(pi.target)
pi.observed <- pi.observed/sum(pi.observed)
#initiate and calculate the second half of the AF adjustment equation across all SNPs
hatted <- vector(mode = "double", length = 1)
starred <- vector(mode = "double", length = 1)
for (j in 1:(k - 1)) {
hatted <- hatted + (pi.observed[j] * N_reference[j])
starred <- starred + (pi.target[j] * N_reference[j])
}
#calculate first half of AF adjustment equation across all SNPs
#first check if pi.observed[k] is not equal to 0 (if pi.observed[k] is equal to 0, (pi.target[k])/(pi.observed[k]) will be undefined)
if (pi.observed[k] == 0){
N_effective = starred
} else{
N_effective <- (((pi.target[k])/(pi.observed[k])) * (N_observed - hatted) + starred)
}
#return effective sample size of this iteration of the AF adjustment leaving one reference group out
return(N_effective)
}
hendriau/Summix documentation built on Nov. 13, 2024, 6:53 a.m.
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Defines functions adjAF_calc adjAF
Documented in adjAF adjAF_calc
#' adjAF
#'
#' @description
#' Adjusts allele frequencies for heterogeneous populations in genetic data given proportion of reference groups
#'
#' @param data dataframe of unadjusted allele frequency for observed group, K reference group allele frequencies for N SNPs
#' @param reference character vector of the column names for K reference groups.
#' @param observed character value for the column name of observed data group
#' @param pi.target numeric vector of the mixture proportions for K reference groups in the target individual or group.
#' @param pi.observed numeric vector of the mixture proportions for K reference groups in the observed group.
#' @param adj_method user choice of method for the allele frequency adjustment: options "average" and "leave_one_out" are available. Defaults to "average".
#' @param N_reference numeric vector of the sample sizes for each of the K reference groups.
#' @param N_observed numeric value of the sample size of the observed group.
#' @param filter sets adjusted allele frequencies equal to 1 if > 1, to 0 if > -.005 and < 0, and removes adjusted allele frequencies < -.005.
#'
#'
#' @return pi: table of input reference groups, pi.observed, and pi.target
#' @return observed.data: name of the data column for the observed group from which adjusted allele frequency is estimated
#' @return Nsnps: number of SNPs for which adjusted AF is estimated
#' @return adjusted.AF: data frame of original data with an appended column of adjusted allele frequencies
#' @return effective.sample.size: The sample size of individuals effectively represented by the adjusted allele frequencies
#' @importFrom stats "na.omit"
#' @importFrom utils "tail"
#'
#'
#' @author Adelle Price, \email{adelle.price@cuanschutz.edu}
#' @author Hayley Wolff, \email{hayley.wolff@cuanschutz.edu}
#' @author Audrey Hendricks, \email{audrey.hendricks@cuanschutz.edu}
#' @references https://github.com/hendriau/Summix2
#' @keywords genetics, mixture distribution, admixture, population stratification
#'
#' @seealso \url{https://github.com/hendriau/Summix2} for further documentation.
#'
#'
#' @examples
#' data(ancestryData)
#' adjusted_data<-adjAF(data = ancestryData,
#' reference = c("reference_AF_afr", "reference_AF_eur"),
#' observed = "gnomad_AF_afr",
#' pi.target = c(1, 0),
#' pi.observed = c(.85, .15),
#' adj_method = 'average',
#' N_reference = c(704,741),
#' N_observed = 20744,
#' filter = TRUE)
#' adjusted_data$adjusted.AF[1:5,]
#'
#'
#'
#' @export
adjAF <- function(data,
reference,
observed,
pi.target,
pi.observed,
adj_method = 'average',
N_reference = NULL,
N_observed = NULL,
filter = TRUE) {
#check for correct format of user inputs
if(length(N_reference) != length(pi.observed)){
stop("ERROR: Please make sure that the lengths of N_reference and pi.observed match.")
}
if(length(pi.target) != length(pi.observed)){
stop("ERROR: Please make sure that the lengths of pi.target and pi.observed match.")
}
if(!is(object = data, class2 = "data.frame")){
stop("ERROR: data must be a data.frame as described in the vignette")
}
if(typeof(observed)!="character"){
stop("ERROR: please enter the column name of the observed group")
}
if( !( observed %in% names(data) ) ){
stop("ERROR: please make sure that the observed group is included in the column names of the data.")
}
if( !all(reference %in% names(data) ) ){
stop("ERROR: Please make sure that all groups in reference are also column names in data")
}
if(typeof(reference)!="character"){
stop("ERROR: please enter the column names for the reference groups")
}
if( !(all(pi.observed<=1) & all(pi.target<=1)) | !(all(pi.observed>=0) & all(pi.target>=0)) ){
stop("ERROR: pi.observed and pi.target must contain ratios between 0 and 1.")
}
#Calculate average fold change of pi.observed to pi.target for across all reference groups
ave_fold_change = mean(abs(((pi.target+.001)-(pi.observed+.001))/(pi.observed+.001)))
#print(paste0('Average fold change between observed and target group proportions is: ', round(ave_fold_change, 2)))
#if average fold change is greater than 3 and less than or equal to 5, print warning
if (ave_fold_change > 3){
print("Warning: The average fold change between the observed and target group proportions is greater than 3. The effective sample size of the adjusted AF is likely substantially lower than the observed sample size.")
}
# #if average fold change is greater than 5, print warning. AF adjustment with average fold change of pi.observed to pi.target greater than 5 is not recommended
# if (ave_fold_change > 5){
# warning("The average fold change between the observed and target group proportions is greater than 5. It is not recommended to perform this AF adjustment.")
# }
fold_change = (floor(sum(pmin(pi.observed, pi.target)*N_observed + N_reference*min(1,pi.target*length(pi.target))))*pi.target - (N_observed*pi.observed+N_reference))/(N_observed*pi.observed+N_reference)
fold_change_refs = c()
large_fold_change=FALSE
#Iteratively check every pi.observed to pi.target fold change. If a given fold change is larger than 10, print warning showing which reference groups have too large of fold changes
for (i in 1:length(N_reference)){
if (fold_change[i] >= 10){
fold_change_refs = append(fold_change_refs, reference[i])
large_fold_change=TRUE
}
}
if (large_fold_change==TRUE){
warning(paste0('Based on the observed group and reference group sample sizes, the observed to target group proportion increase for the ', list(fold_change_refs),' group is too large. ', 'This AF adjustment is not recommended. The effective sample size of the adjusted AF is likely substantially lower than the observed sample size.'))
}
#check the effective sample size of the adjusted AF, if less than 50% of observed sample size, print warning
if(adj_method == "average"){
eff_samp_size = floor(sum(pmin(pi.observed, pi.target)*N_observed + N_reference*(pi.target)))
}
if(adj_method == "leave_one_out"){
eff_samp_size = floor(sum(pmin(tail(pi.observed, 1), tail(pi.target, 1))*N_observed + sum(as.vector((N_reference[1:length(N_reference)-1])*(pi.target[1:length(pi.target)-1])))))
}
cat('\n')
if (eff_samp_size < .5*N_observed){
warning('Your effective sample size of the adjusted alelle frequency is less than 50% of the observed sample size.')
}
allResults <- data.frame(matrix(nrow = nrow(data), ncol = length(reference)))
#If user has selected 'average' method for AF adjustment (this is default)
if (adj_method == "average"){
##when method is ave
if(length(reference) != length(pi.target)){
stop("ERROR: Please make sure that you are entering k groups in reference.")
}
for(i in 1:length(reference)) {
#Iteratively leave each reference group out and calculate adjusted AF
temp <- pi.target[i]
s.targ <- pi.target[-i]
s.targ <- c(s.targ, temp)
temp <- pi.observed[i]
s.obs <- pi.observed[-i]
s.obs <- c(s.obs, temp)
res <- invisible(adjAF_calc(data = data,
reference = reference[-i],
observed = observed,
pi.target = s.targ,
pi.observed = s.obs))
allResults[,i] <- res$adjusted.AF$adjustedAF
}
#Average all adjusted AFs that leave one reference group out at a time to get final adjusted AF
allResults$mean <- apply(allResults, 1, mean)
#if user has set filter == TRUE (this is default), remove negative values less than -.005 from final adjusted AFs, set adjusted AFs less than 0 and greater than -.005 to 0, and set adjusted AFs greater than 1 to 1
if (filter == TRUE){
before_filt_negative = length(allResults$mean)
before_filt_less = length(allResults$mean[allResults$mean < 0 & allResults$mean > -0.005])
before_filt_greater = length(allResults$mean[allResults$mean > 1])
allResults$mean[allResults$mean > 1] <- 1
allResults$mean[allResults$mean < 0 & allResults$mean > -0.005] <- 0
allResults$mean[allResults$mean <= -0.005] <- NA
#Let user know how many adjusted AFs were set to 0, 1, or removed from the final adjusted AF dataframe
cat('\n')
print(paste0("Note: In this AF adjustment, ", before_filt_less, " SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. ", before_filt_greater, " SNPs (with adjusted AF > 1) were rounded to 1, and ", length(allResults$mean[allResults$mean <= -0.005]), " SNPs (with adjusted AF <= -.005) were removed from the final results."))
cat('\n')
}
res$adjusted.AF$adjustedAF <- allResults$mean
#remove any NA's in dataframe (there will only be NA's if user has set filter ==TRUE and the adjusted AFs less than -.005 were set as NA in the previous code chunk)
res$adjusted.AF <- na.omit(res$adjusted.AF)
res$Nsnps <- length(res$adjusted.AF$adjustedAF)
res$effective.sample.size <- eff_samp_size
}
#If user has selected 'leave_one_out' method for AF adjustment
if (adj_method == "leave_one_out"){
##when method is LO
if(length(reference) != (length(pi.target)-1)){
stop("ERROR: Please make sure that you are entering k-1 groups in reference.")
}
res <- invisible(adjAF_calc(data = data,
reference = reference,
observed = observed,
pi.target = pi.target,
pi.observed = pi.observed))
LO_res <- as.vector(t(res$adjusted.AF$adjustedAF))
#if user has set filter == TRUE (this is default), remove negative values less than -.005 from final adjusted AFs, set adjusted AFs less than 0 and greater than -.005 to 0, and set adjusted AFs greater than 1 to 1
if (filter == TRUE){
before_filt_negative = length(LO_res)
before_filt_less = length(LO_res[LO_res < 0 & LO_res > -0.005])
before_filt_greater = length(LO_res[LO_res > 1])
LO_res[LO_res > 1] <- 1
LO_res[LO_res < 0 & LO_res > -0.005] <- 0
LO_res[LO_res <= -0.005] <- NA
#Let user know how many adjusted AFs were set to 0, 1, or removed from the final adjusted AF dataframe
cat('\n')
print(paste0("Note: In this AF adjustment, ", before_filt_less, " SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. ", before_filt_greater, " SNPs (with adjusted AF > 1) were rounded to 1, and ", length(res$adjusted.AF$adjustedAF[res$adjusted.AF$adjustedAF <= -0.005]), " SNPs (with adjusted AF <= -.005) were removed from the final results."))
cat('\n')
}
res$adjusted.AF$adjustedAF <- LO_res
#remove any NA's in dataframe (there will only be NA's if user has set filter ==TRUE and the adjusted AFs less than -.005 were set as NA in the previous code chunk)
res$adjusted.AF <- na.omit(res$adjusted.AF)
res$Nsnps <- length(res$adjusted.AF$adjustedAF)
res$effective.sample.size <- eff_samp_size
}
if(adj_method == "leave_one_out"){
reference = append(reference, "Left Out")
}
print(noquote('$pi'))
print(cbind.data.frame('ref.group' = reference, 'pi.observed' = pi.observed, 'pi.target' = pi.target))
cat('\n')
print(noquote('$observed.data'))
print(paste0("observed AF data to update: ", "'", observed, "'"))
cat('\n')
print(noquote('$Nsnps'))
print(length(res$adjusted.AF$adjustedAF))
cat('\n')
cat('\n')
print(noquote('$effective.sample.size'))
print(res$effective.sample.size)
cat('\n')
cat('\n')
print("use $adjusted.AF$adjustedAF to see adjusted AF data")
cat('\n')
cat('\n')
print("Note: The accuracy of the AF adjustment is likely lower for rare variants (< .5%).")
return(res)
}
#' adjAF_calc
#'
#' @description
#' Helper function for calculating allele frequencies for heterogeneous populations in genetic data given proportion of reference groups
#'
#' @param data dataframe of unadjusted allele frequency for observed group, K-1 reference group allele frequencies for N SNPs
#' @param reference character vector of the column names for K-1 reference groups. The name of the last reference group is not included as that group is not used to estimate the adjusted allele frequencies.
#' @param observed character value for the column name of observed data group
#' @param pi.target numeric vector of the mixture proportions for K reference groups in the target sample or subject. The order must match the order of the reference columns with the last entry matching the missing reference group.
#' @param pi.observed numeric vector of the mixture proportions for K reference groups for the observed group. The order must match the order of the reference columns with the last entry matching the missing reference group.
#'
#' @return pi: table of input reference groups, pi.observed, and pi.target
#' @return observed.data: name of the data column for the observed group from which adjusted allele frequency is estimated
#' @return Nsnps: number of SNPs for which adjusted AF is estimated
#' @return adjusted.AF: data frame of original data with an appended column of adjusted allele frequencies
#' @importFrom tidyselect "all_of"
#' @importFrom dplyr "select"
#'
#' @export
#NOTE: this is the summix v1 adjAF code, now used as a helper function
adjAF_calc <- function(
data ,
reference ,
observed ,
pi.target ,
pi.observed ){
k = length(reference) + 1
#Normalize pi. We need the pi_reference and pi.target to sum to 1
pi.target <- pi.target/sum(pi.target)
pi.observed <- pi.observed/sum(pi.observed)
#sum the reference group allele frequencies multiplied by pi.target and name it 'hatted'
hatted <- vector(mode = "double", length = dim(data)[1])
#sum up the reference group allele frequencies multiplied by pi_reference and name it 'starred'
starred <- vector(mode = "double", length = dim(data)[1])
#sum the K-1 reference groups multiplied by pi.observed.
#sum the k-1 reference groups multiplied by pi.target.
for (j in 1:(k - 1)) {
hatted <- hatted + (pi.observed[j] * data %>% select(reference[j]))
starred <- starred + (pi.target[j] * data %>% select(reference[j]))
}
#check pi.observed for the kth reference group is greater than 0 before dividing pi.target by pi.observed
if (pi.observed[k]>0){
data$adjustedAF <- ((pi.target[k]/pi.observed[k]) * (data %>% select(all_of(observed)) - hatted) + starred)
#if pi.observed is not greater than 0, set pi.target/pi.observed equal to 0
}else{
data$adjustedAF <- ((data %>% select(all_of(observed)) - hatted) + starred)
}
data.out <- data
observed.data=paste("observed data to update AF: '", observed, "'", sep="")
pi_table<-data.frame(ref.group=c(reference, "NONE"), pi.observed=pi.observed, pi.target=pi.target)
Nsnps=nrow(data)
tmp.out<-list("pi"=pi_table, "observed.data"=observed.data, "Nsnps"=Nsnps, "adjusted.AF"=data.out, "effective.sample.size"="0")
return(tmp.out)
}
#' calc_effective_N
#'
#' @description
#' Helper function to calculate effective sample size for the group that is left out when estimating the adjusted allele frequencies in each adjAF function iteration.
#
#' @param N_reference numeric vector of the sample sizes of each K reference groups.
#' @param N_observed numeric value of the sample size of the observed group.
#' @param pi.target numeric vector of the mixture proportions for K reference groups in the target sample or subject. The order must match the order of the reference columns with the last entry matching the missing reference group.
#' @param pi.observed numeric vector of the mixture proportions for K reference groups for the observed group. The order must match the order of the reference columns with the last entry matching the missing reference group.
#'
#' @return N_effective: effective sample size for the group that is left out when estimating the adjusted allele frequencies in each adjAF function iteration.
#'
#' @export
calc_effective_N <- function (N_reference, N_observed, pi.target, pi.observed){
#one reference group is left out for each calculation of N_effective, must ensure k is equal to the total number of reference groups in the AF adjustment
k = length(N_reference) + 1
#ensure pi.target and pi.observed are normalized
pi.target <- pi.target/sum(pi.target)
pi.observed <- pi.observed/sum(pi.observed)
#initiate and calculate the second half of the AF adjustment equation across all SNPs
hatted <- vector(mode = "double", length = 1)
starred <- vector(mode = "double", length = 1)
for (j in 1:(k - 1)) {
hatted <- hatted + (pi.observed[j] * N_reference[j])
starred <- starred + (pi.target[j] * N_reference[j])
}
#calculate first half of AF adjustment equation across all SNPs
#first check if pi.observed[k] is not equal to 0 (if pi.observed[k] is equal to 0, (pi.target[k])/(pi.observed[k]) will be undefined)
if (pi.observed[k] == 0){
N_effective = starred
} else{
N_effective <- (((pi.target[k])/(pi.observed[k])) * (N_observed - hatted) + starred)
}
#return effective sample size of this iteration of the AF adjustment leaving one reference group out
return(N_effective)
}
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