Dev (#39)

* minor updates * update some of the hscn columns * changing Maj/Min to A/B * change actions * actions update
shahcompbio · Jan 21, 2023 · 5b920ca · 5b920ca
1 parent 90660e0
commit 5b920ca
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diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -19,13 +19,14 @@ jobs:
       GITHUB_PAT: ${{ secrets.PAT }}
       R_KEEP_PKG_SOURCE: yes
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: r-lib/actions/setup-r@v2
         with:
           use-public-rspm: true
 
       - if: runner.os == 'macOS'
+        uses: Homebrew/actions/setup-homebrew@master
         run: brew install --cask xquartz
 
       - uses: r-lib/actions/setup-pandoc@v2

diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
@@ -2,9 +2,11 @@ on:
   push:
     branches:
       - master
+      - main
   pull_request:
     branches:
       - master
+      - main
 
 name: test-coverage
 
@@ -16,9 +18,9 @@ jobs:
     steps:
       - uses: actions/checkout@v2
 
-      - uses: r-lib/actions/setup-r@master
+      - uses: r-lib/actions/setup-r@v2
 
-      - uses: r-lib/actions/setup-pandoc@master
+      - uses: r-lib/actions/setup-pandoc@v2
       - name: Install xquartz
         run: brew install --cask xquartz
       - name: Query dependencies

diff --git a/R/callASCN.R b/R/callASCN.R
@@ -134,7 +134,7 @@ switch_alleles <- function(cn) {
   phase_cn <- cn %>%
     as.data.table() %>%
     .[, switch := data.table::fifelse(
-      Min > Maj,
+      B > A,
       "switch",
       "stick"
     )] %>%
@@ -182,17 +182,20 @@ switch_alleles <- function(cn) {
 #'
 #' @return allele specific copy number object which includes dataframe similar to input with additional columns which include
 #'
-#' * `Maj` (Major allele copy number)
-#' * `Min` (Minor allele copy number)
-#' * `state_AS_phased` (phased state of the form Maj|Min )
-#' * `state_AS` (mirrored state of the form Maj|Min)
-#' * `LOH` (is bin LOH or not)
-#' * `state_phase` (state describing which is the dominant allele and whether it is LOH or not)
-#' * `state_BAF` (binned discretized BAF value calculated as Min / (Maj + Min))
-#' 
+#' * `A` A allele copy number
+#' * `B` B allele copy number
+#' * `state_AS_phased` A|B
+#' * `state_min` Minor allele copy number
+#' * `LOH` =LOH if bin is LOH, NO otherwise
+#' * `state_phase` Discretized haplotype specific states 
+#' * `phase` Whether the A allele or B allele is dominant
+#' * `alleleA` Counts for the A allele
+#' * `alleleB` Counts for the B allele
+#' * `totalcounts` Total number of counts
+#' * `BAF` B-allele frequency (alleleB / totalcounts)
 #'
 #' @details
-#' In the allele specific copy number inference Maj is always > Min and state_AS_phased == state_AS
+#' In the allele specific copy number inference A is always > B and state_AS_phased == state_AS
 #'
 #' @export
 callAlleleSpecificCN <- function(CNbins,
@@ -333,47 +336,47 @@ callAlleleSpecificCN <- function(CNbins,
   }
 
   alleleCN <- alleleCN %>%
-    .[, Maj1 := state - state_min] %>%
-    .[, Min1 := state_min] %>%
-    # catch edge cases where Min > Maj:
-    .[, Min := fifelse(Min1 > Maj1, Maj1, Min1)] %>%
-    .[, Maj := fifelse(Min1 > Maj1, Min1, Maj1)] %>%
-    .[, Maj1 := NULL] %>%
-    .[, Min1 := NULL] %>%
+    .[, A1 := state - state_min] %>%
+    .[, B1 := state_min] %>%
+    # catch edge cases where B > A:
+    .[, B := fifelse(B1 > A1, A1, B1)] %>%
+    .[, A := fifelse(B1 > A1, B1, A1)] %>%
+    .[, A1 := NULL] %>%
+    .[, B1 := NULL] %>%
     # catch edge cases of 0|1 and 1|0 states:
     .[, state_min := fifelse(state_min < 0, 0, state_min)] %>%
-    .[, Maj := state - state_min] %>%
-    .[, Min := state_min] %>%
-    .[, Min := fifelse(Min < 0, 0, Min)] %>%
-    .[, Maj := fifelse(Maj < 0, 0, Maj)] %>%
-    .[, Min := fifelse(Min > state, state, Min)] %>%
-    .[, Maj := fifelse(Maj > state, state, Maj)] %>%
-    .[, state_AS_phased := paste0(Maj, "|", Min)] %>%
-    .[, state_AS := paste0(pmax(state - Min, Min), "|", pmin(state - Min, Min))] %>%
-    .[, state_min := pmin(Maj, Min)] %>%
+    .[, A := state - state_min] %>%
+    .[, B := state_min] %>%
+    .[, B := fifelse(B < 0, 0, B)] %>%
+    .[, A := fifelse(A < 0, 0, A)] %>%
+    .[, B := fifelse(B > state, state, B)] %>%
+    .[, A := fifelse(A > state, state, A)] %>%
+    .[, state_AS_phased := paste0(A, "|", B)] %>%
+    .[, state_AS := paste0(pmax(state - B, B), "|", pmin(state - B, B))] %>%
+    .[, state_min := pmin(A, B)] %>%
     .[, state_AS := ifelse(state > 4, state, state_AS)] %>%
     .[, LOH := ifelse(state_min == 0, "LOH", "NO")] %>%
     .[, phase := c("Balanced", "A", "B")[1 +
-      1 * ((Min < Maj)) +
-      2 * ((Min > Maj))]] %>%
+      1 * ((B < A)) +
+      2 * ((B > A))]] %>%
     .[, state_phase := c("Balanced", "A-Gained", "B-Gained", "A-Hom", "B-Hom")[1 +
-      1 * ((Min < Maj) & (Min != 0)) +
-      2 * ((Min > Maj) & (Maj != 0)) +
-      3 * ((Min < Maj) & (Min == 0)) +
-      4 * ((Min > Maj) & (Maj == 0))]] %>%
+      1 * ((B < A) & (B != 0)) +
+      2 * ((B > A) & (A != 0)) +
+      3 * ((B < A) & (B == 0)) +
+      4 * ((B > A) & (A == 0))]] %>%
     .[order(cell_id, chr, start)] %>%
-    .[, state_BAF := round((Min / state) / 0.1) * 0.1] %>%
+    .[, state_BAF := round((B / state) / 0.1) * 0.1] %>%
     .[, state_BAF := fifelse(is.nan(state_BAF), 0.5, state_BAF)]
 
   # mirror BAF
   alleleCN <- alleleCN[, switch := data.table::fifelse(
-    Min > Maj,
+    B > A,
     "switch",
     "stick"
   )] %>%
     .[, alleleB := totalcounts - alleleA] %>%
-    .[, distA := abs(BAF - (Min / state))] %>%
-    .[, distB := abs(BAF - (Maj / state))] %>%
+    .[, distA := abs(BAF - (B / state))] %>%
+    .[, distB := abs(BAF - (A / state))] %>%
     .[, switch := fifelse((distB < distA) & (BAF != 0.5), "switch", "stick")] %>%
     .[, alleleA := data.table::fifelse(switch == "switch", alleleB, alleleA)] %>%
     .[, alleleB := totalcounts - alleleA] %>%
@@ -385,7 +388,7 @@ callAlleleSpecificCN <- function(CNbins,
 
   if (fillmissing){
     alleleCN <- dplyr::left_join(CNbins, alleleCN)
-    alleleCN <- tidyr::fill(alleleCN, c("state_min", "Maj", "Min", "state_phase", "state_AS", 
+    alleleCN <- tidyr::fill(alleleCN, c("state_min", "A", "B", "state_phase", "state_AS", 
                                         "state_AS_phased", "LOH", "state_BAF", "phase"), .direction = "downup")
   }
 
@@ -416,16 +419,16 @@ callAlleleSpecificCN <- function(CNbins,
 #'
 #' @return allele specific copy number object which includes dataframe similar to input with additional columns which include
 #'
-#' * `Maj` (Major allele copy number)
-#' * `Min` (Minor allele copy number)
-#' * `state_AS_phased` (phased state of the form Maj|Min )
-#' * `state_AS` (mirrored state of the form Maj|Min)
+#' * `A` (Major allele copy number)
+#' * `B` (Minor allele copy number)
+#' * `state_AS_phased` (phased state of the form A|B )
+#' * `state_AS` (mirrored state of the form A|B)
 #' * `LOH` (is bin LOH or not)
 #' * `state_phase` (state describing which is the dominant allele and whether it is LOH or not)
-#' * `state_BAF` (binned discretized BAF value calculated as Min / (Maj + Min))
+#' * `state_BAF` (binned discretized BAF value calculated as B / (A + B))
 #'
 #' @details
-#' In the allele specific copy number inference Maj is always > Min and state_AS_phased == state_AS
+#' In the allele specific copy number inference A is always > B and state_AS_phased == state_AS
 #'
 #' @export
 callAlleleSpecificCNfromHSCN <- function(hscn,
@@ -502,49 +505,49 @@ callAlleleSpecificCNfromHSCN <- function(hscn,
   }
 
   alleleCN <- alleleCN %>%
-    .[, Maj1 := state - state_min] %>%
-    .[, Min1 := state_min] %>%
-    # catch edge cases where Min > Maj:
-    .[, Min := fifelse(Min1 > Maj1, Maj1, Min1)] %>%
-    .[, Maj := fifelse(Min1 > Maj1, Min1, Maj1)] %>%
-    .[, Maj1 := NULL] %>%
-    .[, Min1 := NULL] %>%
+    .[, A1 := state - state_min] %>%
+    .[, B1 := state_min] %>%
+    # catch edge cases where B > A:
+    .[, B := fifelse(B1 > A1, A1, B1)] %>%
+    .[, A := fifelse(B1 > A1, B1, A1)] %>%
+    .[, A1 := NULL] %>%
+    .[, B1 := NULL] %>%
     # catch edge cases of 0|1 and 1|0 states:
-    .[, state_min := Min] %>% 
+    .[, state_min := B] %>% 
     .[, state_min := fifelse(state_min < 0, 0, state_min)] %>%
-    .[, Maj := state - state_min] %>%
-    .[, Min := state_min] %>%
-    .[, Min := fifelse(Min < 0, 0, Min)] %>%
-    .[, Maj := fifelse(Maj < 0, 0, Maj)] %>%
-    .[, Min := fifelse(Min > state, state, Min)] %>%
-    .[, Maj := fifelse(Maj > state, state, Maj)] %>%
-    .[, state_AS_phased := paste0(Maj, "|", Min)] %>%
-    .[, state_AS := paste0(pmax(state - Min, Min), "|", pmin(state - Min, Min))] %>%
-    .[, state_min := pmin(Maj, Min)] %>%
+    .[, A := state - state_min] %>%
+    .[, B := state_min] %>%
+    .[, B := fifelse(B < 0, 0, B)] %>%
+    .[, A := fifelse(A < 0, 0, A)] %>%
+    .[, B := fifelse(B > state, state, B)] %>%
+    .[, A := fifelse(A > state, state, A)] %>%
+    .[, state_AS_phased := paste0(A, "|", B)] %>%
+    .[, state_AS := paste0(pmax(state - B, B), "|", pmin(state - B, B))] %>%
+    .[, state_min := pmin(A, B)] %>%
     .[, state_AS := ifelse(state > 4, state, state_AS)] %>%
     .[, LOH := ifelse(state_min == 0, "LOH", "NO")] %>%
     .[, phase := c("Balanced", "A", "B")[1 +
-                                           1 * ((Min < Maj)) +
-                                           2 * ((Min > Maj))]] %>%
+                                           1 * ((B < A)) +
+                                           2 * ((B > A))]] %>%
     .[, state_phase := c("Balanced", "A-Gained", "B-Gained", "A-Hom", "B-Hom")[1 +
-                                                                                 1 * ((Min < Maj) & (Min != 0)) +
-                                                                                 2 * ((Min > Maj) & (Maj != 0)) +
-                                                                                 3 * ((Min < Maj) & (Min == 0)) +
-                                                                                 4 * ((Min > Maj) & (Maj == 0))]] %>%
+                                                                                 1 * ((B < A) & (B != 0)) +
+                                                                                 2 * ((B > A) & (A != 0)) +
+                                                                                 3 * ((B < A) & (B == 0)) +
+                                                                                 4 * ((B > A) & (A == 0))]] %>%
     .[order(cell_id, chr, start)] %>%
-    .[, state_BAF := round((Min / state) / 0.1) * 0.1] %>%
+    .[, state_BAF := round((B / state) / 0.1) * 0.1] %>%
     .[, state_BAF := fifelse(is.nan(state_BAF), 0.5, state_BAF)]
 
   # mirror BAF
   alleleCN <- alleleCN[, switch := data.table::fifelse(
-    Min > Maj,
+    B > A,
     "switch",
     "stick"
   )] %>%
     .[, alleleB := totalcounts - alleleA] %>%
     .[, origBAF := BAF] %>% 
-    .[, distA := abs(BAF - (Min / state))] %>%
-    .[, distB := abs(BAF - (Maj / state))] %>%
+    .[, distA := abs(BAF - (B / state))] %>%
+    .[, distB := abs(BAF - (A / state))] %>%
     .[, switch := fifelse((distB < distA) & (BAF != 0.5), "switch", "stick")] %>%
     .[, alleleA := data.table::fifelse(switch == "switch", alleleB, alleleA)] %>%
     .[, alleleB := totalcounts - alleleA] %>%
@@ -555,7 +558,7 @@ callAlleleSpecificCNfromHSCN <- function(hscn,
 
   if (fillmissing){
     alleleCN <- dplyr::left_join(CNbins, alleleCN)
-    alleleCN <- tidyr::fill(alleleCN, c("state_min", "Maj", "Min", "state_phase", "state_AS", 
+    alleleCN <- tidyr::fill(alleleCN, c("state_min", "A", "B", "state_phase", "state_AS", 
                                           "state_AS_phased", "LOH", "state_BAF", "phase"), .direction = "downup")
   }