update_v1.0.2 (#23)

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AllelicSeries
 Title: Allelic Series Test
-Version: 0.1.0.1
+Version: 0.1.0.2
 Authors@R:
     c(person(given = "Zachary",
            family = "McCaw",
@@ -33,7 +33,7 @@ LinkingTo:
     Rcpp,
     RcppArmadillo
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Suggests:
     knitr,
     rmarkdown,

NAMESPACE

@@ -6,16 +6,16 @@ export(ASBTSS)
 export(ASKAT)
 export(ASKATSS)
 export(Aggregator)
-export(BaseCountsSS)
+export(BaselineSS)
 export(COAST)
 export(COASTSS)
 export(CalcSumstats)
 export(Comparator)
 export(Counts)
 export(DGP)
 export(GenomicControl)
-export(IVWSS)
 export(OLS)
+export(SumCountSS)
 export(isPD)
 exportClasses(COAST)
 exportClasses(DfOrNULL)

R/RcppExports.R

@@ -105,46 +105,34 @@ isPD <- function(x, force_symmetry = FALSE, tau = 1e-8) {
     .Call(`_AllelicSeries_isPD`, x, force_symmetry, tau)
 }
 
-#' Inverse Variance Meta-Analysis
-#' 
-#' @param anno (snps x 1) annotation vector with values in c(0, 1, 2).
-#' @param beta (snps x 1) vector of effect sizes for 
-#'   the coding genetic variants within a gene.
-#' @param ld (snps x snps) matrix of correlations among the genetic variants.
-#' @param se (snps x 1) vector of standard errors for the effect sizes.
-#' @param weights (3 x 1) vector of annotation category weights.
-#' @return Data.frame with the following columns \itemize{
-#' \item anno: gene annotation
-#' \item beta_meta: meta-analyzed effect size
-#' \item se_meta: standard error of the meta-analyzed effect size
-#' }
-IVWCpp <- function(anno, beta, se, ld, weights) {
-    .Call(`_AllelicSeries_IVWCpp`, anno, beta, se, ld, weights)
+AnnoMat <- function(anno) {
+    .Call(`_AllelicSeries_AnnoMat`, anno)
 }
 
-#' Category Correlation
+#' Baseline Counts Test from Sumstats
 #'
-#' Calculates an estimate of the correlations across the total burdens
-#' of rare variants within each category {BMV, DMV, PTV}. To enable this
-#' calculation, an assumption of Hardy-Weinberg Equilibrium is made, which
-#' was found to provide good performance empirically. 
-#' 
-#' @param anno (snps x 1) annotation vector with values in c(0, 1, 2).
+#' @param anno (snps x 1) annotation vector.
+#' @param beta (snps x 1) vector of effect sizes for 
+#'   the coding genetic variants within a gene.
 #' @param ld (snps x snps) matrix of correlations among the genetic variants.
-#' @param maf (snps x 1) vector of minor allele frequencies.
-CatCor <- function(anno, ld, maf) {
-    .Call(`_AllelicSeries_CatCor`, anno, ld, maf)
+#' @param se (snps x 1) vector of standard errors for the effect sizes.
+#' @return Numeric p-value.
+#' @export
+BaselineSS <- function(anno, beta, ld, se) {
+    .Call(`_AllelicSeries_BaselineSS`, anno, beta, ld, se)
 }
 
-#' Baseline Counts Test from Sumstats
+#' Allelic Sum Test from Sumstats
 #'
+#' @param anno (snps x 1) annotation vector.
 #' @param beta (snps x 1) vector of effect sizes for 
 #'   the coding genetic variants within a gene.
 #' @param ld (snps x snps) matrix of correlations among the genetic variants.
 #' @param se (snps x 1) vector of standard errors for the effect sizes.
+#' @param weights (3 x 1) vector of annotation category weights.
 #' @return Numeric p-value.
 #' @export
-BaseCountsSS <- function(beta, ld, se) {
-    .Call(`_AllelicSeries_BaseCountsSS`, beta, ld, se)
+SumCountSS <- function(anno, beta, ld, se, weights) {
+    .Call(`_AllelicSeries_SumCountSS`, anno, beta, ld, se, weights)
 }
 

R/allelic_series_sumstats.R

@@ -22,7 +22,7 @@ DEFAULT_WEIGHTS <- c(1, 2, 3)
 #' @param maf (snps x 1) vector of minor allele frequencies. Although ideally
 #'   provided, defaults to the zero vector.
 #' @param method Method for aggregating across categories:
-#'   {"none", "sum"}. Default: "none".
+#'   ("none", "sum"). Default: "none".
 #' @param weights (3 x 1) vector of annotation category weights.
 #' @return Numeric p-value of the allelic series burden test.
 #' @examples
@@ -74,45 +74,24 @@ ASBTSS <- function(
   if (is.null(maf)) {maf <- rep(0, n_snps)}
   if (!is_pd) {ld <- ld + eps * diag(n_snps)}
 
-  # Per-category summary statistics.
-  category_sumstats <- IVWSS(
-    anno = anno,
-    beta = beta,
-    se = se,
-    ld = ld,
-    weights = weights
-  )
-
-  # Category correlation matrix.
-  r3 <- CatCor(anno = anno, ld = ld, maf = maf)
-  if (!isPD(r3)) {r3 <- r3 + eps * diag(3)}
-
   # Run burden test.
   if (method == "none") {
 
-    pval <- BaseCountsSS(
-      beta = category_sumstats$beta_meta,
-      ld = r3,
-      se = category_sumstats$se_meta
+    pval <- BaselineSS(
+      anno = anno,
+      beta = beta,
+      ld = ld,
+      se = se
     )
 
   } else if (method == "sum") {
 
-    key <- (category_sumstats$se_meta > 0)
-    n_nz <- sum(key)
-
-    gene_sumstats <- IVWSS(
-      anno = rep(0, n_nz),
-      beta = category_sumstats$beta_meta[key],
-      ld = r3[key, key, drop = FALSE],
-      se = category_sumstats$se_meta[key],
-      weights = rep(1, n_nz)
-    )
-
-    pval <- BaseCountsSS(
-      beta = gene_sumstats$beta_meta[1],
-      se = gene_sumstats$se_meta[1],
-      ld = diag(1)
+    pval <- SumCountSS(
+      anno = anno,
+      beta = beta,
+      ld = ld,
+      se = se,
+      weights = weights
     )
 
   } else {

R/allelic_series_test.R

@@ -13,7 +13,7 @@ DEFAULT_WEIGHTS <- c(1, 2, 3)
 #' @param drop_empty Drop empty columns? Default: TRUE.
 #' @param indicator Convert raw counts to indicators? Default: FALSE.
 #' @param method Method for aggregating across categories:
-#'   {"none", "max", "sum"}. Default: "none".
+#'   ("none", "max", "sum"). Default: "none".
 #' @param min_mac Minimum minor allele count for inclusion. Default: 0. 
 #' @param weights Annotation category weights.
 #' @return (n x 3) Numeric matrix without weighting, (n x 1) numeric matrix
@@ -104,8 +104,8 @@ Aggregator <- function(
 #' @param covar (n x p) covariate matrix. Defaults to an (n x 1) intercept.
 #' @param indicator Convert raw counts to indicators?
 #' @param is_pheno_binary Is the phenotype binary? Default: FALSE.
-#' @param method Method for aggregating across categories: {"none", "max",
-#'   "sum"}. Default: "none".
+#' @param method Method for aggregating across categories: ("none", "max",
+#'   "sum"). Default: "none".
 #' @param min_mac Minimum minor allele count for inclusion. Default: 0. 
 #' @param score_test Run a score test? If FALSE, performs a Wald test.
 #' @param weights (3 x 1) annotation category weights.