Moved annotation of filtered probes to script 6, moved unfiltered nor…

…malization into separate chunk, manually set all samples to female to prevent minfi error during normalization.

scripts/7_normalization.Rmd

@@ -49,13 +49,12 @@ load(paste0(user_choices$project_name, "/processed_data/PhenoData_clean.Rdata"))
 load(paste0(user_choices$project_name, "/processed_data/RGSet_clean.Rdata"))
 load(paste0(user_choices$project_name, "/processed_data/Betas_clean.Rdata"))
 load(paste0(user_choices$project_name, "/reports/annotations_clean.Rdata"))
+load(paste0(user_choices$project_name, "/reports/annotations_clean_filtered.Rdata"))
 ```
 
 # Description
 
 This script:  
-
-* annotates the probes  
 * normalizes data with stratified quantile normalization for filtered and unfiltered data  
 (followed by BMIQ = beta-mixtrure quantile normalization) to minimize unwanted 
 technique-related variation within and between samples  
@@ -67,21 +66,19 @@ data for further investigation. These files are saved to reports folder.
 **Note**  
 The user can further alter the normalization methods if needed (e.g. include noob etc.)  
 
-```{r get annotation of filtered probes, include=FALSE}
-annotations_clean_filtered = getAnnotation(gRatioSet_clean_filtered)
-```
-
 ## Normalization of data performed
 
-```{r normalize probes, include=FALSE}
+```{r normalize filtered probes, include=FALSE}
 # Normalization of filtered data
 
 # Matrix of probes removed in all prior filtering steps
 exclusion_matrix <- Betas_clean[!rownames(Betas_clean) %in% rownames(Betas_clean_filtered), ]
 # Exclude all probes filtered script 5 from steps before to improve preprocessing
 RGSet_clean_filtered <- subsetByLoci(RGSet_clean, excludeLoci = rownames(exclusion_matrix))
-
-gRatioSet_clean_filtered_quantile <- preprocessQuantile(RGSet_clean_filtered)
+# Functional normalization 
+# Note: sex is set to "F" for all samples since sex chromosomes were already removed in script 6
+# Note: This does not affect the normalization or the phenotype data, it simply stops preprocessQuantile() from producing an error
+gRatioSet_clean_filtered_quantile <- preprocessQuantile(RGSet_clean_filtered, sex = "F")
 save(gRatioSet_clean_filtered_quantile, file = paste0(user_choices$project_name, 
                                          "/processed_data/gRatioSet_clean_filtered_quantile.Rdata")) # output: GenomicRatioSet
 
@@ -99,8 +96,15 @@ Betas_clean_filtered_quantile_bmiq <- apply(Betas_clean_filtered_quantile[,1:len
 save(Betas_clean_filtered_quantile_bmiq, file = paste0(user_choices$project_name,
                                               "/processed_data/Betas_clean_filtered_quantile_bmiq.Rdata"))
 
+```
+
+```{r. normalize unfiltered probes, include=FALSE}
 # Normalization of unfiltered data
-gRatioSet_clean_unfiltered_quantile <- preprocessQuantile(RGSet_clean)
+
+# Functional normalization 
+# Note: sex is set to "F" for all samples since sex chromosomes were already removed in script 6
+# Note: This does not affect the normalization or the phenotype data, it simply stops preprocessQuantile() from producing an error
+gRatioSet_clean_unfiltered_quantile <- preprocessQuantile(RGSet_clean, sex = "F")
 save(gRatioSet_clean_unfiltered_quantile, file = paste0(user_choices$project_name, 
                                          "/processed_data/gRatioSet_clean_unfiltered_quantile.Rdata")) # output: GenomicRatioSet
 
@@ -117,10 +121,9 @@ Betas_clean_unfiltered_quantile_bmiq <- apply(Betas_clean_unfiltered_quantile[,1
                                    function(a) BMIQ(a,probeType$probeType,plots=FALSE)$nbeta) # sourced from script "BMIQ_1.6_Teschendorff.R"
 save(Betas_clean_unfiltered_quantile_bmiq, file = paste0(user_choices$project_name,
                                               "/processed_data/Betas_clean_unfiltered_quantile_bmiq.Rdata"))
-
-save(annotations_clean_filtered, file = paste0(user_choices$project_name, "/reports/annotations_clean_filtered.Rdata"))
 ```
 
+
 ## Beta values distribution report
 
 ```{r beta values distribution}