Rename 'standard' to 'top1000' for clarity and consistency with 'top1…

…500'

R/getDegTx.R

@@ -9,10 +9,10 @@
 #' object containing the transcript data desired to be studied.
 #' @param type A `character(1)` specifying the transcripts set type.
 #' These were determined by Joshua M. Stolz et al, 2022. Here the names "cell_component", "top1500",
-#' and "standard" refer to models that were determined to be effective in removing degradation effects.
-#' The "standard" model involves taking the union of the top 1000 transcripts
+#' and "top1000" refer to models that were determined to be effective in removing degradation effects.
+#' The "top1000" model involves taking the union of the top 1000 transcripts
 #' associated with degradation from the interaction model and the main effect model.
-#' The "top1500" model is the same as the "standard model except the
+#' The "top1500" model is the same as the "top1000 model except the
 #' union of the top 1500 genes associated with degradation is selected.
 #' The most effective of our models, "cell_component", involved deconvolution of
 #' the degradation matrix to determine the proportion of cell types within our studied tissue.
@@ -32,8 +32,8 @@
 #' @import rlang
 #'
 #' @examples
-#' degTx <- getDegTx(rse_tx, "standard")
-getDegTx <- function(rse_tx, type = c("cell_component", "standard", "top1500"),
+#' degTx <- getDegTx(rse_tx, "top1000")
+getDegTx <- function(rse_tx, type = c("cell_component", "top1000", "top1500"),
     sig_transcripts = NULL, assayname = "tpm", verbose = TRUE) {
     # type = arg_match(type)
     if (is.null(sig_transcripts)) {

R/qSVA.R

@@ -35,7 +35,7 @@
 #' qSVA(rse_tx = rse_tx, type = "cell_component", mod = mod, assayname = "tpm")
 #'
 qSVA <-
-    function(rse_tx, type = c("cell_component", "standard", "top1500"),
+    function(rse_tx, type = c("cell_component", "top1000", "top1500"),
     sig_transcripts = NULL, mod, assayname) {
         if (is.null(sig_transcripts)) {
             type <- arg_match(type) # must be one of those in the list if sig_transcripts is NULL

R/select_transcripts.R

@@ -4,11 +4,11 @@
 #'
 #' @param type A `character(1)` specifying the transcripts set type.
 #' These were determined by Joshua M. Stolz
-#' et al, 2022. Here the names "cell_component", "top1500", and "standard" refer
+#' et al, 2022. Here the names "cell_component", "top1500", and "top1000" refer
 #' to models that were determined to be effective in removing degradation
-#' effects. The "standard" model involves taking the union of the top 1000
+#' effects. The "top1000" model involves taking the union of the top 1000
 #' transcripts associated with degradation from the interaction model and the
-#' main effect model. The "top1500" model is the same as the "standard" model
+#' main effect model. The "top1500" model is the same as the "top1000" model
 #' except the union of the top 1500 genes associated with degradation is
 #' selected. The most effective of our models, "cell_component", involved
 #' deconvolution of the degradation matrix to determine the proportion of cell
@@ -28,13 +28,13 @@
 #'
 #' ## Example where match.arg() auto-completes
 #' select_transcripts("top")
-select_transcripts <- function(type = c("cell_component", "top1500", "standard")) {
+select_transcripts <- function(type = c("cell_component", "top1500", "top1000")) {
     type <- match.arg(type)
     if (type == "cell_component") {
         return(qsvaR::transcripts$cell_component)
     } else if (type == "top1500") {
         return(qsvaR::transcripts$tx1500)
-    } else if (type == "standard") {
+    } else if (type == "top1000") {
         return(qsvaR::transcripts$standard)
     }
 }

R/transcripts-data.R

@@ -2,11 +2,11 @@
 #'
 #' An object storing three lists of transcripts each corresponding to a model
 #' used in the degradation experiment. These were determined by Joshua M. Stolz
-#' et al, 2022. Here the names "cell_component", "top1500", and "standard" refer
+#' et al, 2022. Here the names "cell_component", "top1500", and "top1000" refer
 #' to models that were determined to be effective in removing degradation
-#' effects. The "standard" model involves taking the union of the top 1000
+#' effects. The "top1000" model involves taking the union of the top 1000
 #' transcripts associated with degradation from the interaction model and the
-#' main effect model. The "top1500" model is the same as the "standard" model
+#' main effect model. The "top1500" model is the same as the "top1000" model
 #' except the union of the top 1500 genes associated with degradation is
 #' selected. The most effective of our models, "cell_component", involved
 #' deconvolution of the degradation matrix to determine the proportion of cell

vignettes/Intro_qsvaR.Rmd

@@ -151,7 +151,7 @@ rse_tx <- rse_tx[rowMeans(assays(rse_tx)$tpm) > 0.3, ]
 
 ## Get Degradation Matrix
 
-In this next step we subset for the transcripts associated with degradation. These were determined by Joshua M. Stolz et al, 2022. We have provided three models to choose from. Here the names `"cell_component"`, `"top1500"`, and `"standard"` refer to models that were determined to be effective in removing degradation effects. The `"standard"` model involves taking the union of the top 1000 transcripts associated with degradation from the interaction model and the main effect model. The `"top1500"` model is the same as the `"standard"` model except the union of the top 1500 genes associated with degradation is selected. The most effective of our models, `"cell_component"`, involved deconvolution of the degradation matrix to determine the proportion of cell types within our studied tissue. These proportions were then added to our `model.matrix()` and the union of the top 1000 transcripts in the interaction model, the main effect model, and the cell proportions model were used to generate this model of quality surrogate variables (qSVs). In this example we will choose `"cell_component"` when using the `getDegTx()` and `select_transcripts()` functions.
+In this next step we subset for the transcripts associated with degradation. These were determined by Joshua M. Stolz et al, 2022. We have provided three models to choose from. Here the names `"cell_component"`, `"top1500"`, and `"top1000"` refer to models that were determined to be effective in removing degradation effects. The `"top1000"` model involves taking the union of the top 1000 transcripts associated with degradation from the interaction model and the main effect model. The `"top1500"` model is the same as the `"top1000"` model except the union of the top 1500 genes associated with degradation is selected. The most effective of our models, `"cell_component"`, involved deconvolution of the degradation matrix to determine the proportion of cell types within our studied tissue. These proportions were then added to our `model.matrix()` and the union of the top 1000 transcripts in the interaction model, the main effect model, and the cell proportions model were used to generate this model of quality surrogate variables (qSVs). In this example we will choose `"cell_component"` when using the `getDegTx()` and `select_transcripts()` functions.
 
 ```{r VennDiagram,fig.cap="The above venn diagram shows the overlap between transcripts in each of the previously mentioned models.", echo=FALSE}
 knitr::include_graphics("../man/figures/transcripts_venn_diagramm.png")