More Spell Checking

SchlossLab · Nov 11, 2024 · bfac338 · bfac338
1 parent 1262250
commit bfac338
Show file tree

Hide file tree

Showing 3 changed files with 3 additions and 3 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -11,7 +11,7 @@ Authors@R: c(
             comment = c(ORCID = "0000-0002-6935-4275"))
     )
 Maintainer: Patrick Schloss <[email protected]>
-Description: A tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) <doi:10.1128/AEM.01541-09>). clustur make use of the 'cluster' and 'make.shared' command from mothur. The cluster command has five different algorithms implemented: 'opticlust', 'furthest', 'nearest', 'average', and 'weighted'. Opticlust is an optimized OTU clustering algorithm, and you can learn more here, (Westcott SL, Schloss PD (2017) <doi:10.1128/mspheredirect.00073-17>). The 'make.shared' command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently.
+Description: A tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) <doi:10.1128/AEM.01541-09>). clustur make use of the 'cluster' and 'make.shared' command from mothur. The cluster command has five different algorithms implemented: 'opticlust', 'furthest', 'nearest', 'average', and 'weighted'. OptiClust is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) <doi:10.1128/mspheredirect.00073-17>). The 'make.shared' command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Imports:

diff --git a/man/clustur-package.Rd b/man/clustur-package.Rd
diff --git a/vignettes/clustur.Rmd b/vignettes/clustur.Rmd
@@ -116,7 +116,7 @@ cluster_data <- cluster(column_distance, cutoff, method = "weighted")
 #### edit this paragraph further...
 All methods produce a list object with an indicator of the cutoff that was used
 (`label`), as well as cluster composition (`cluster`) and shared (`abundance`) data frames.
-The `clusters` data frame shows how which OTU each sequence was assigned to. The `abundance` data frame
+The `clusters` data frame shows which OTU (Operation Taxonomic Unit) each sequence was assigned to. The `abundance` data frame
 contains columns indicating the `OTU` and `sample` identifiers and the
 abundance of each OTU in each sample. The OptiClust method also includes the `metrics` data
 frame, which describe the optimization value for each iteration in the fitting