--- a/minfi_analysis.xml	Tue Feb 11 09:14:55 2020 -0500
+++ b/minfi_analysis.xml	Sun Feb 23 17:00:42 2020 -0500
@@ -42,16 +42,16 @@
 	if ('$optpp' == 'na' ) {
 	GRSet <- mapToGenome(RSet) #mapping Ilumina methylation array data to the genome
 	} else if ('$optpp' == 'ppfun'  ) {
-	GRSet <- preprocessFunnorm(RGSet) #optional - implements the functional normalization algorithm
+	GRSet <- preprocessFunnorm(RGSet) #optional - implements the functional normalisation algorithm
 	} else  if ('$optpp' == 'ppq' ) {
 	GRSet <- preprocessQuantile(RGSet, fixOutliers = TRUE,
         removeBadSamples = TRUE, badSampleCutoff = 10.5,
         quantileNormalize = TRUE, stratified = TRUE,
-        mergeManifest = FALSE, sex = NULL) #optional - implements stratified quantile normalization preprocessing
+        mergeManifest = FALSE, sex = NULL) #optional - implements stratified quantile normalisation preprocessing
 	}  else if ('$optpp' == 'ppsnp' ) {
 	snps <- getSnpInfo(GRSet) #optional - retrieve the chromosome and the position of each SNP
 	write.table(snps, '$table')
-	GRSet <- dropLociWithSnps(GRSet, snps=c('SBE','CpG'), maf=0) #optional - drop the probes that contain either a SNP at the CpG interrogation or at the single nucleotide extensions
+	GRSet <- dropLociWithSnps(GRSet, snps=c('SBE','CpG'), maf=0) #optional - drop the probes that contain either an SNP at the CpG interrogation or at the single nucleotide extensions
 	}
 	pheno <- read.table('$phenotype_table',skip = 1)
         group <- pheno\$V2
@@ -154,13 +154,13 @@
 	
 **What it does**
 
-The workflow combines 5 main steps, starting with raw intensity data loading (.idat) and then optional preprocessing and normalisation of the data. The next quality control step performs an additional sample check to remove low-quality data, which normalisation cannot detect. The workflow gives the user the opportunity to perform any of these preparation and data cleaning steps, including highly recommended genetic variation annotation step resulting in single nucleotide polymorphism identification and removal. Finally, the dataset generated through all of these steps can be used to hunt (find) differentially-methylated positions (DMP)and regions (DMR) with respect to a phenotype covariate.
+The workflow combines 5 main steps, starting with raw intensity data loading (.idat) and then optional preprocessing and normalisation of the data. The next quality control step performs an additional sample check to remove low-quality data, which normalisation cannot detect. The workflow gives the user the opportunity to perform any of these preparation and data cleaning steps, including the highly recommended genetic variation annotation step resulting in single nucleotide polymorphism identification and removal. Finally, the dataset generated through all of these steps can be used to hunt (find) differentially-methylated positions (DMP)and regions (DMR) with respect to a phenotype covariate.
 
 ***Inputs***    
 
-*Series of .IDAT files*: red and green .idat file for each sample on the chip intensity data.    
+*Series of .IDAT files*: matching red and green .idat file for each sample on the chip intensity data.    
 
-*(optional) Preprocessing Methods*: by this step probes can be stratified by region via quantile normalization or by extended implementation of functional normalisation recommended for cases where global changes are expected such as in cancer-normal comparisons. In addition unwanted probes containing either a SNP at the CpG interrogation or at the single nucleotide extension can be removed (recommended).   
+*(optional) Preprocessing Methods*: by this step probes can be stratified by region via quantile normalisation or by extended implementation of functional normalisation recommended for cases where global changes are expected such as in cancer-normal comparisons. In addition unwanted probes containing either an SNP at the CpG interrogation or at the single nucleotide extension can be removed (recommended).   
 
 *Phenotype Table*: table of compared samples and their characteristics, may be categorical (e.g. cancer vs. normal) or continuous (e.g. blood pressure).