use built-in transposition for cosine computation example

`coop::tcosine`

Author: cpauvert

R/delineate_with_similarity.R

@@ -27,7 +27,7 @@
 #'  2. It uses base R functions to reduce the dependencies
 #'
 #'
-#' @seealso For similarity metrics: [`coop::cosine`](https://rdrr.io/cran/coop/man/cosine.html), [stats::cor], [`Hmisc::rcorr`](https://rdrr.io/cran/Hmisc/man/rcorr.html). For using taxonomic identifications for clusters : [delineate_with_identification]. For further analyses: [set_reference_spectra].
+#' @seealso For similarity metrics: [`coop::tcosine`](https://rdrr.io/cran/coop/man/cosine.html), [stats::cor], [`Hmisc::rcorr`](https://rdrr.io/cran/Hmisc/man/rcorr.html). For using taxonomic identifications for clusters : [delineate_with_identification]. For further analyses: [set_reference_spectra].
 #' @export
 #' @examples
 #' # Toy similarity matrix between the six example spectra of

README.Rmd

@@ -55,8 +55,7 @@ processed <- spectra_dir %>%
 processed %>%
   list() %>%
   merge_processed_spectra() %>%
-  t() %>%
-  coop::cosine() %>%
+  coop::tcosine() %>%
   delineate_with_similarity(threshold = 0.92) %>%
   set_reference_spectra(processed$metadata) %>%
   pick_spectra() %>%

README.md

@@ -50,8 +50,7 @@ processed <- spectra_dir %>%
 processed %>%
   list() %>%
   merge_processed_spectra() %>%
-  t() %>%
-  coop::cosine() %>%
+  coop::tcosine() %>%
   delineate_with_similarity(threshold = 0.92) %>%
   set_reference_spectra(processed$metadata) %>%
   pick_spectra() %>%

dev/dereplicate-spectra.Rmd

@@ -362,7 +362,7 @@ Once all the batches of spectra have been processed together, we can use a dista
 
 While we do not provide specific functions to generate the similarity matrix, we illustrate below how it can be easily computed.
 Note that the feature matrix from [`merge_processed_spectra()`](https://clavellab.github.io/maldipickr/reference/merge_processed_spectra.html) has spectra as rows and peaks values as columns.
-So to get a similarity matrix between spectra, the feature matrix must be transposed before cosine computation.
+So to get a similarity matrix between spectra, either the feature matrix must be transposed or a dedicated function must be used.
 
 ```{r similarity, eval=FALSE}
 # A. Compute the similarity matrix on the transposed feature matrix
@@ -372,8 +372,8 @@ sim_matrix <- stats::cor(t(fm), method = "pearson")
 # B.1 Install the coop package
 # install.packages("coop")
 
-# B.2 Compute the similarity matrix on the transposed feature matrix
-sim_matrix <- coop::cosine(t(fm))
+# B.2 Compute the similarity matrix on the rows of the feature matrix
+sim_matrix <- coop::tcosine(fm)
 ```
 
 # Delineate clusters of spectra
@@ -418,7 +418,7 @@ This representation allows to infer the clusters easily. A table summarizes for
 #'  2. It uses base R functions to reduce the dependencies
 #'
 #'
-#' @seealso For similarity metrics: [`coop::cosine`](https://rdrr.io/cran/coop/man/cosine.html), [stats::cor], [`Hmisc::rcorr`](https://rdrr.io/cran/Hmisc/man/rcorr.html). For using taxonomic identifications for clusters : [delineate_with_identification]. For further analyses: [set_reference_spectra].
+#' @seealso For similarity metrics: [`coop::tcosine`](https://rdrr.io/cran/coop/man/cosine.html), [stats::cor], [`Hmisc::rcorr`](https://rdrr.io/cran/Hmisc/man/rcorr.html). For using taxonomic identifications for clusters : [delineate_with_identification]. For further analyses: [set_reference_spectra].
 #' @export
 delineate_with_similarity <- function(sim_matrix, threshold) {
   if (!is.matrix(sim_matrix)) {

man/delineate_with_similarity.Rd

@@ -120,7 +120,7 @@ Once all the batches of spectra have been processed together, we can use a dista
 
 While we do not provide specific functions to generate the similarity matrix, we illustrate below how it can be easily computed.
 Note that the feature matrix from [`merge_processed_spectra()`](https://clavellab.github.io/maldipickr/reference/merge_processed_spectra.html) has spectra as rows and peaks values as columns.
-So to get a similarity matrix between spectra, the feature matrix must be transposed before cosine computation.
+So to get a similarity matrix between spectra, either the feature matrix must be transposed or a dedicated function must be used.
 
 
 ```{r similarity, eval = FALSE}
@@ -131,8 +131,8 @@ sim_matrix <- stats::cor(t(fm), method = "pearson")
 # B.1 Install the coop package
 # install.packages("coop")
 
-# B.2 Compute the similarity matrix on the transposed feature matrix
-sim_matrix <- coop::cosine(t(fm))
+# B.2 Compute the similarity matrix on the rows of the feature matrix
+sim_matrix <- coop::tcosine(fm)
 ```
 
 # Delineate clusters of spectra