From 0c622bbfbec5a553058996c5a99aa24ed85ae969 Mon Sep 17 00:00:00 2001 From: scheidec Date: Mon, 21 Apr 2025 14:50:19 -0400 Subject: [PATCH] Fix typos in pre-processing vignette --- vignettes/articles/pre-processing.Rmd | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/vignettes/articles/pre-processing.Rmd b/vignettes/articles/pre-processing.Rmd index 18ec30b..c4fa4a0 100755 --- a/vignettes/articles/pre-processing.Rmd +++ b/vignettes/articles/pre-processing.Rmd @@ -52,7 +52,7 @@ recommended prior to executing an analysis on SomaScan data, along with ## Filtering Features The goal of this pre-processing step is to remove features (SeqIds) typically -not useful for analalysis from a SomaScan dataset, while also retaining +not useful for analysis from a SomaScan dataset, while also retaining *relevant* features that will enable broad discovery during downstream analysis. The filtering logic typically used for protein features (i.e. SOMAmer @@ -532,8 +532,8 @@ assessment and further evaluation. While centering and scaling standardizes the RFU distributions across all SeqIds for multivariate analysis, it is important to understand that this does *not* enable meaningful comparison of expression values between different -SeqIds. Take, for instance, hypothetical SeqId A with a z-socre of 2 and -hypothetical SeqId B with a z-socre of -1. One cannot infer that the protein +SeqIds. Take, for instance, hypothetical SeqId A with a z-score of 2 and +hypothetical SeqId B with a z-score of -1. One cannot infer that the protein target of SeqId A was present at a higher concentration than the target of SeqId B in the original sample prep. All comparisons should be made between sample groups *within* a given SeqId. The RFU value, as well as log10 RFU and @@ -550,11 +550,11 @@ factors intrinsic to the SOMAmer Reagent within the SomaScan assay. The `preProcessAdat()` function is available to perform the steps outlined in this vignette. By default, it will filter features and samples using the standard QC and normalization acceptance criteria -described earlier, and drop sample-level RFU outliers. It also has option to -perform log-10 and center & scale transformations to the untransformed RFU -values. If data QC plots by endpoints or clinical variables are desired, -the names of the variables should be explicitly passed to the `data.qc` -argument. Please see the `preProcessAdat()` function documentation for +described earlier, but _will not_ drop sample-level RFU outliers. It also has +the option to perform log-10 and center & scale transformations to the +untransformed RFU values. If data QC plots by endpoints or clinical variables +are desired,the names of the variables should be explicitly passed to the +`data.qc` argument. Please see the `preProcessAdat()` function documentation for more details. ```{r recreate-vignette-data}