@@ -1,5 +1,5 @@

 Package: GSVA

-Version: 1.39.29

+Version: 1.39.30

 Title: Gene Set Variation Analysis for microarray and RNA-seq data

 Authors@R: c(person("Justin", "Guinney", role=c("aut", "cre"), email="justin.guinney@sagebase.org"),

              person("Robert", "Castelo", role="aut", email="robert.castelo@upf.edu"),

@@ -63,8 +63,9 @@ Once `r Biocpkg("GSVA")` is installed, it can be loaded with the following comma

 library(GSVA)

 ```

-Given a gene expression data matrix with rows corresponding to genes and columns

-to samples, such as this one simulated from random Gaussian data:

+Given a gene expression data matrix, which we shall call `X`, with rows

+corresponding to genes and columns to samples, such as this one simulated from

+random Gaussian data:

 ```{r}

 p <- 10000 ## number of genes

@@ -75,8 +76,9 @@ X <- matrix(rnorm(p*n), nrow=p,

 X[1:5, 1:5]

 ```

-Given a collection of gene sets stored, for instance, in a `list` object such as

-this one with genes sampled uniformly at random without replacement into the gene sets:

+Given a collection of gene sets stored, for instance, in a `list` object, which

+we shall call `gs`, with genes sampled uniformly at random without replacement

+into 100 different gene sets:

 ```{r}

 ## sample gene set sizes

@@ -95,24 +97,26 @@ dim(gsva.es)

 gsva.es[1:5, 1:5]

 ```

-So, the first argument to the `gsva()` function is the gene expression data matrix

-and the second the collection of gene sets. The `gsva()` function can take the input

-expression data and gene sets using different specialized containers that facilitate

-the access and manipulation of molecular and phenotype data, as well as their associated

-metadata. Another advanced features include the use of on-disk and parallel backends to

-enable using GSVA on large molecular data sets and speed up computing time. You will

-find information on all these features in this vignette.

+The first argument to the `gsva()` function is the gene expression data matrix

+and the second the collection of gene sets. The `gsva()` function can take the

+input expression data and gene sets using different specialized containers that

+facilitate the access and manipulation of molecular and phenotype data, as well

+as their associated metadata. Another advanced features include the use of

+on-disk and parallel backends to enable, respectively, using GSVA on large

+molecular data sets and speed up computing time. You will find information on

+these features in this vignette.

 # Introduction

 Gene set variation analysis (GSVA) provides an estimate of pathway activity

-by transforming an input gene-by-sample expression data matrix

-into a gene-set-by-sample one. This resulting expression data matrix can be

-then used with classical analytical methods such as differential expression,

-classification, survival analysis, clustering or correlation analysis in a

-pathway-centric manner. One can also perform sample-wise comparisons between

-pathways and other molecular data types such as microRNA expression or binding

-data, copy-number variation (CNV) data or single nucleotide polymorphisms (SNPs).

+by transforming an input gene-by-sample expression data matrix into a

+corresponding gene-set-by-sample expression data matrix. This resulting

+expression data matrix can be then used with classical analytical methods such

+as differential expression, classification, survival analysis, clustering or

+correlation analysis in a pathway-centric manner. One can also perform

+sample-wise comparisons between pathways and other molecular data types such

+as microRNA expression or binding data, copy-number variation (CNV) data or

+single nucleotide polymorphisms (SNPs).

 The GSVA package provides an implementation of this approach for the following

 methods:

@@ -151,7 +155,7 @@ methods:

 The interested user may find full technical details about how these methods

 work in their corresponding articles cited above. If you use any of them in a

-publication, please cite it with the given bibliographic reference.

+publication, please cite them with the given bibliographic reference.

 # Overview of the GSVA functionality

@@ -197,13 +201,14 @@ the following filters:

    minimum and maximum size, which by default is one for the minimum size and

    has no limit for the maximum size.

-If, as a result of this filter, either no genes or gene sets are left, the

-`gsva()` function will prompt an error. A common cause for an error at this

-stage is that gene identifiers between the expression data matrix and the gene

-sets do not belong to the same standard nomenclature and could not be mapped,

-because either the input data were not provided using some of the specialized

-containers described above or the necessary metadata in those containers to

-successfully map gene identifiers is missing.

+If, as a result of applying these three filters, either no genes or gene sets

+are left, the `gsva()` function will prompt an error. A common cause for such

+an error at this stage is that gene identifiers between the expression data

+matrix and the gene sets do not belong to the same standard nomenclature and

+could not be mapped. This may happen because either the input data were not

+provided using some of the specialized containers described above or the

+necessary metadata in those containers that allows the software to successfully

+map gene identifiers, is missing.

 By default, the `gsva()` function employs the method described by

 @haenzelmann_castelo_guinney_2013 but this can be changed using the argument

@@ -253,12 +258,12 @@ continuous expression values.

 # Gene set definitions and gene identifier mapping

-Gene sets constitute a simple, yet useful, way to define pathways, essentially

-because we use pathway membership definitions only, neglecting the information

-on molecular interactions. Gene set definitions are a crucial input to any gene

-set enrichment analysis because if our gene sets do not capture the biological

+Gene sets constitute a simple, yet useful, way to define pathways because we

+use pathway membership definitions only, neglecting the information on molecular

+interactions. Gene set definitions are a crucial input to any gene set

+enrichment analysis because if our gene sets do not capture the biological

 processes we are studying, we will likely not find any relevant insights in our

-data.

+data from an analysis based on these gene sets.

 There are multiple sources of gene sets, the most popular ones being

 [The Gene Ontology (GO) project](http://geneontology.org) and