diff --git a/scripts/10_correction_batch_effects_unfiltered.Rmd b/scripts/10_correction_batch_effects_unfiltered.Rmd
index dce65a0..f59a991 100644
--- a/scripts/10_correction_batch_effects_unfiltered.Rmd
+++ b/scripts/10_correction_batch_effects_unfiltered.Rmd
@@ -30,7 +30,7 @@ start_time <- Sys.time()
 ```
 
 ```{r setup, include=FALSE}
-source("../data/calculate_pc_cutoff.R") # source function for determining PC cutoff 
+source("../data/Calculate_PC_Cutoff.R") # source function for determining PC cutoff 
 needed_packages <- c("BiocManager", "dplyr", "knitr", "rmarkdown", "tibble", "ggplot2", 
                      "ggrepel", "broom", "gplots", "tidyr", "sva", "methods")
 user_choices <- readRDS("../data/user_choices.rds")
@@ -69,7 +69,7 @@ phenotype_data <- readRDS(paste0(user_choices$project_name, "/processed_data/phe
 
 ## Correction of technical batch effects with ComBat
 
-```{r, correction of first batch effect and PCA}
+```{r, correction of first batch effect and PCA, results='asis'}
 m_values <- apply(Betas_clean_unfiltered_quantile_bmiq, 2, function(x) log2((x)/(1-x))) # get M-values
 
 if (correction_variable_1 == "PLEASE FILL IN"){
@@ -230,7 +230,11 @@ if (correction_variable_1 == "PLEASE FILL IN"){
 }
 ```
 
-```{r, correction of second batch effect and PCA}
+```{r, print p-value table for batch effects after correction of first batch effect variable}
+anova_lm_pvalue_df %>% paged_table()
+```
+
+```{r, correction of second batch effect and PCA, results='asis'}
 if (correction_variable_2 == "PLEASE FILL IN"){
   print("No second batch correction variable was specified by user, data remains unchanged")
 } else {
@@ -389,7 +393,11 @@ if (correction_variable_2 == "PLEASE FILL IN"){
 }
 ```
 
-```{r, correction of third batch effect and PCA}
+```{r, print p-value table for batch effects after correction of second batch effect variable}
+anova_lm_pvalue_df %>% paged_table()
+```
+
+```{r, correction of third batch effect and PCA, results='asis'}
 if (correction_variable_3 == "PLEASE FILL IN"){
   print("No third batch correction variable was specified by user, data remains unchanged")
 } else {
@@ -548,7 +556,11 @@ if (correction_variable_3 == "PLEASE FILL IN"){
 }
 ```
 
-```{r, convertion M to Beta and save, include=FALSE}
+```{r, print p-value table for batch effects after correction of third batch effect variable}
+anova_lm_pvalue_df %>% paged_table()
+```
+
+```{r, conversion M to Beta and save, include=FALSE}
 if(exists("m_values_combat_3")){
   corrected_data <- m_values_combat_3
   changing_status <- "changed"
diff --git a/scripts/11_annotate_hg19_create_pseudo_epicv1_from_epicv2.Rmd b/scripts/11_annotate_hg19_create_pseudo_epicv1_from_epicv2.Rmd
index 2c57f79..34b1e51 100644
--- a/scripts/11_annotate_hg19_create_pseudo_epicv1_from_epicv2.Rmd
+++ b/scripts/11_annotate_hg19_create_pseudo_epicv1_from_epicv2.Rmd
@@ -1,6 +1,6 @@
 ---
 title: "Script 11: Create hg19 annotation for EPICv2 and pseudo-EPICv1 version from EPICv2 data"
-date: '`r format(Sys.time(), %d %B, %Y")`'
+date: '`r format(Sys.time(), "%d %B, %Y")`'
 output: html_document
 ---
 
@@ -27,11 +27,10 @@ start_time <- Sys.time()
 
 ```{r setup, include=FALSE}
 user_choices <- readRDS("../data/user_choices.rds")
-knitr::opts_knit$set(root.dir = paste0(user_choices$personal_path, "/"))
 knitr::opts_chunk$set(echo = FALSE)
 
-if(user_choices$package_directory != "PLEASE FILL IN"){
-  .libPaths(c(user_choices$package_directory, .libPaths()))
+if(user_choices$package_path != "PLEASE FILL IN"){
+  .libPaths(c(user_choices$package_path, .libPaths()))
 }
 
 needed_packages <- c("BiocManager", "dplyr", "knitr", "rmarkdown", "tibble", "minfi", "janitor")
@@ -57,6 +56,7 @@ load(paste0(user_choices$project_name, "/reports/annotations_clean_unfiltered.Rd
 load(paste0(user_choices$project_name, "/processed_data/PhenoData_clean.Rdata"))
 annotation_hg19 <- read.table(paste0(user_choices$personal_path, "/epic_preprocessing_k2h/data/EPICv2.hg19.manifest.tsv"))
 annotation_hg19 <- row_to_names(annotation_hg19, row_number = 1, remove_row = TRUE, remove_rows_above = TRUE)
+knitr::opts_knit$set(root.dir = paste0(user_choices$personal_path, "/"))
 ```
 
 ```{r, check array type, include = TRUE}
@@ -68,7 +68,7 @@ if (user_choices$array_type == "v1") {
 
 ```{r, hg19 annotation filtered data, include = FALSE}
 annations_clean_filtered_hg19 <- annotation_hg19 %>%
-  dplyr::filter(Probe_ID %in% rownames(Betas_clean_quantile_bmiq_filtered_combated))
+  dplyr::filter(Probe_ID %in% rownames(Betas_clean_filtered_quantile_bmiq_combated))
 save(annations_clean_filtered_hg19, file = paste0(user_choices$project_name, "/reports/annations_clean_filtered_hg19.Rdata"))
 ```
 
@@ -81,10 +81,10 @@ save(annotions_clean_unfiltered_hg19, file = paste0(user_choices$project_name, "
 ```{r, create pseudo-EPICv1 version for filtered data, include=FALSE}
 # create annotation only containing filtered CpGs that are also on v1 and exclude any duplicated loci
 annotations_clean_filtered_pseudo_v1 <- annotations_clean_filtered %>%
-  filter(Name %in% rownames(Betas_clean_filtered_quantile_bmiq_combated)) %>%
+  dplyr::filter(Name %in% rownames(Betas_clean_filtered_quantile_bmiq_combated)) %>%
   # only keep loci on EPICv1, filter wrongly annotated empty value
-  filter(EPICv1_Loci != "") %>%
-  filter(duplicated(EPICv1_Loci) == FALSE)
+  dplyr::filter(EPICv1_Loci != "") %>%
+  dplyr::filter(duplicated(EPICv1_Loci) == FALSE)
 
 # only keep betas that are on v1 and exclude any duplicated loci (keep first CpG)
 keep_betas <- (rownames(Betas_clean_filtered_quantile_bmiq_combated) %in% annotations_clean_filtered_pseudo_v1$Name)
@@ -103,7 +103,7 @@ save(Betas_clean_filtered_quantile_bmiq_combated_pseudo_v1, file = paste0(user_c
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
              " filtered CpGs were not found on EPICv1 and therefore excluded"), sep = "<br>\n")
   
-  dim_Betas_filtered <- dim(Betas_clean_quantile_bmiq_filtered_combated_pseudo_v1)
+  dim_Betas_filtered <- dim(Betas_clean_filtered_quantile_bmiq_combated_pseudo_v1)
   step_number <- c("14")
   step <- c("Create pseudo-EPICv1 version (filtered data)")
   data_class <- c("Betas")
@@ -117,9 +117,9 @@ save(Betas_clean_filtered_quantile_bmiq_combated_pseudo_v1, file = paste0(user_c
 ```{r, create pseudo-EPICv1 version for unfiltered data, include=FALSE}
 # create annotation only containing unfiltered CpGs that are also on v1 and exclude any duplicated loci
 annotations_clean_unfiltered_pseudo_v1 <- annotations_clean_unfiltered %>%
-  filter(Name %in% rownames(Betas_clean_unfiltered_quantile_bmiq_combated)) %>%
-  filter(EPICv1_Loci != "") %>%
-  filter(duplicated(EPICv1_Loci) == FALSE)
+  dplyr::filter(Name %in% rownames(Betas_clean_unfiltered_quantile_bmiq_combated)) %>%
+  dplyr::filter(EPICv1_Loci != "") %>%
+  dplyr::filter(duplicated(EPICv1_Loci) == FALSE)
 
 # only keep betas that are on v1 and exclude any duplicated loci (keep first CpG)
 keep_betas <- (rownames(Betas_clean_unfiltered_quantile_bmiq_combated) %in% annotations_clean_unfiltered_pseudo_v1$Name)
@@ -138,7 +138,7 @@ save(Betas_clean_unfiltered_quantile_bmiq_combated_pseudo_v1, file = paste0(user
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
              " unfiltered CpGs were not found on EPICv1 and therefore excluded"), sep = "<br>\n")
   
-  dim_Betas_unfiltered <- dim(Betas_clean_quantile_bmiq_unfiltered_combated_pseudo_v1)
+  dim_Betas_unfiltered <- dim(Betas_clean_unfiltered_quantile_bmiq_combated_pseudo_v1)
   step_number <- c("15")
   step <- c("Create pseudo-EPICv1 version (unfiltered data)")
   data_class <- c("Betas")
diff --git a/scripts/1_definitions_and_setup.Rmd b/scripts/1_definitions_and_setup.Rmd
index 404f356..0627cdf 100644
--- a/scripts/1_definitions_and_setup.Rmd
+++ b/scripts/1_definitions_and_setup.Rmd
@@ -43,6 +43,10 @@ This script asks the user to define or choose:
   + person id  
   + sex  
 * additional batch variables (up to 3)  
+* optionally:
+  + path for R packages
+  + path to the genotype data location
+  + prefix (project name) of the genotype files
 
 <br>  
 **Notes/Definitions:**  
@@ -52,9 +56,9 @@ The path name should not include the name of the github repository
 **Path to phenotype data:** The path to the data table containing phenotype information  
 The path name should include the name of data table itself. This file must be **.csv**  
 **Path to idat files:** The path to the raw files obtained from array  
-**Path to R package directories (optional):** The path to the personal directory for installing and loading packages. 
+**Path to R package directories (optional):** The path to a directory for installing and loading R packages. 
 This is needed if installation of R packages to the default R library are not permitted due to the lack of admin rights. 
-Thsi will enable R to install and load any needed packages without problems. 
+This will enable R to install and load any needed packages without problems. 
 Please make sure you have the needed writing permissions in this specified directory  
 **Array type:** "v1" or "v2". This stands for the different versions of the EPIC array  
 **Population ethnicity:** Choose "AFR", "AMR", "ASN", "EUR" or "Global". Global 
@@ -78,6 +82,9 @@ be listed as F, M, or W. If your sex data is described in a different way
 be considered in a batch check in script 8: plate, slide, array, row and column. Please 
 provide additional possible batch variables if needed as a character vector (up to 3, 
 add more if necessary). If not, please leave this field as is.  
++**Path to genotype files:** The path to the QC-ed genotype data in plink file format (.bed, .bim, .fam)  
++**Genotype project name:** Prefix of the genotype files, denoting the project (e.g. my_study_2020)  
+
 <br>  
 
 ## Directory structure
@@ -143,10 +150,11 @@ user_choices <- data.frame(
   "name_well_column" = name_well_column,
   "name_personid_column" = name_personid_column,
   "name_sex_column" = name_sex_column, 
-  "name_treatment_column" = name_treatment_column,
   "additional_batch_variable_1" = additional_batch_variable_1,
   "additional_batch_variable_2" = additional_batch_variable_2,
-  "additional_batch_variable_3" = additional_batch_variable_3
+  "additional_batch_variable_3" = additional_batch_variable_3,
+  "genotype_path" = genotype_path,
+  "genotype_projectname" = genotype_projectname
 )
 saveRDS(user_choices, "../data/user_choices.rds")
 
diff --git a/scripts/2_import_raw_DNAm_data.Rmd b/scripts/2_import_raw_DNAm_data.Rmd
index 3c84dd1..6b31569 100644
--- a/scripts/2_import_raw_DNAm_data.Rmd
+++ b/scripts/2_import_raw_DNAm_data.Rmd
@@ -29,7 +29,7 @@ if (any(installed_biocmanager_packages == FALSE)) {
 lapply(needed_biocmanager_packages, library, character.only = TRUE)
 
 if (user_choices$array_type == "v2") {
-  if (!("jokergoo/IlluminaHumanMethylationEPICv2manifest" %in% rownames(installed.packages()))) {
+  if (!("IlluminaHumanMethylationEPICv2manifest" %in% rownames(installed.packages()))) {
     BiocManager::install("jokergoo/IlluminaHumanMethylationEPICv2manifest")
   }
   if (!("IlluminaHumanMethylationEPICv2anno.20a1.hg38" %in% rownames(installed.packages()))) {
diff --git a/scripts/3_quality_control.Rmd b/scripts/3_quality_control.Rmd
index 8ef3db5..a22a324 100644
--- a/scripts/3_quality_control.Rmd
+++ b/scripts/3_quality_control.Rmd
@@ -27,7 +27,6 @@ library(minfi)
 
 knitr::opts_knit$set(root.dir = paste0(user_choices$personal_path, "/"))
 knitr::opts_chunk$set(echo = FALSE)
-phenotype_data <- readRDS(paste0(user_choices$project_name, "/processed_data/phenotype_data.rds"))
 ```
 
 ```{r load data, include=FALSE}
@@ -147,7 +146,7 @@ title_df <- title_df %>%
   dplyr::select(arrayid, personid)
 
 for (i in 1:ncol(RGSet_quality)){ 
-  title_df_current <- title_df %>% filter(arrayid == rownames(pData(RGSet_quality))[i] %>% pull(personid))
+  title_df_current <- title_df %>% filter(arrayid == rownames(pData(RGSet_quality))[i]) %>% pull(personid)
   titel <- paste0(rownames(pData(RGSet_quality))[i], " / ", title_df_current)
   densityPlot(as.matrix(Betas_quality[,i]), main = titel)
   print(i)
diff --git a/scripts/4_check_matching_of_epigenetic_sex.Rmd b/scripts/4_check_matching_of_epigenetic_sex.Rmd
index 736dafd..bf8ac07 100644
--- a/scripts/4_check_matching_of_epigenetic_sex.Rmd
+++ b/scripts/4_check_matching_of_epigenetic_sex.Rmd
@@ -54,6 +54,7 @@ fail the sex match check
 
 ```{r, predict sex with DNAm data, include= FALSE}
 predictedSex <- getSex(gRatioSet_quality, cutoff=-2)
+phenotype_data <- subset(phenotype_data, arrayid %in% rownames(predictedSex),)
 
 sex_df <- phenotype_data %>%
   select(personid, arrayid, reported_sex = sex) %>%
diff --git a/scripts/6_filtering_cpgs.Rmd b/scripts/6_filtering_cpgs.Rmd
index 5319162..865c403 100644
--- a/scripts/6_filtering_cpgs.Rmd
+++ b/scripts/6_filtering_cpgs.Rmd
@@ -25,25 +25,29 @@ if (!("minfi" %in% rownames(installed.packages()))) {
 }
 library(minfi)
 
-knitr::opts_knit$set(root.dir = paste0(user_choices$personal_path, "/"))
 knitr::opts_chunk$set(echo = FALSE)
 ```
 
-```{r load data, include=FALSE}
-summary_table_preprocessing <- readRDS(paste0(user_choices$project_name, "/reports/summary_table_preprocessing.rds"))
-load(paste0(user_choices$project_name, "/processed_data/RGSet_clean.Rdata"))
-load(paste0(user_choices$project_name, "/processed_data/gRatioSet_clean.Rdata"))
-load(paste0(user_choices$project_name, "/processed_data/Betas_clean.Rdata"))
-load(paste0(user_choices$project_name, "/processed_data/PhenoData_clean.Rdata"))
-load(paste0(user_choices$project_name, "/processed_data/detP_clean.Rdata"))
+```{r load data 1, include=FALSE}
 # Info about cross-hybridizing probes comes from Chen et al. 2013, DOI: 10.4161/epi.23470:
-load("data/ChenProbeIDs.rdata")
+load("../data/ChenProbeIDs.rdata")
 # Info about mapping inaccuracies for EPIC v2.0 from Illumina product information:
 if (user_choices$array_type == "v2") {
 v2_mapping_inaccuracy <- read.csv("../data/EPIC-8v2-0_A1-190MappingInaccuracies.csv")}
 # Info about flagged probes for EPIC v2.0 from Illumina product information:
 if (user_choices$array_type == "v2") {
 v2_flagged_probes <- read.csv("../data/EPIC-8v2-0_A1-FlaggedProbes.csv")}
+
+knitr::opts_knit$set(root.dir = paste0(user_choices$personal_path, "/"))
+```
+
+```{r load data 2, include=FALSE}
+summary_table_preprocessing <- readRDS(paste0(user_choices$project_name, "/reports/summary_table_preprocessing.rds"))
+load(paste0(user_choices$project_name, "/processed_data/RGSet_clean.Rdata"))
+load(paste0(user_choices$project_name, "/processed_data/gRatioSet_clean.Rdata"))
+load(paste0(user_choices$project_name, "/processed_data/Betas_clean.Rdata"))
+load(paste0(user_choices$project_name, "/processed_data/PhenoData_clean.Rdata"))
+load(paste0(user_choices$project_name, "/processed_data/detP_clean.Rdata"))
 ```
 
 # Description
@@ -127,13 +131,13 @@ if (user_choices$array_type == "v2") {
   keep_betas_df <- as.data.frame(keep_betas)
   cat(paste0(nrow(exclude_replicates), " replicate probes were removed"))
   
-  dim_gRatioSet_filtered <- dim(gRatioSet_clean)
-  dim_Betas_filtered <- dim(Betas_clean)
+  dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean)
+  dim_Betas_clean_filtered <- dim(Betas_clean)
   step_number <- c("4", "4")
   step <- c("Filter replicates", "Filter replicates")
   data_class <- c("gRatioSet", "Betas")
-  samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-  probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+  samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+  probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
 
   table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
   summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -150,7 +154,7 @@ Betas_clean_filtered <- Betas_clean[keep_betas,]
 
 # ensure probes are in the same order in cleaned gRatioSet (RGSet with annotations) and detP objects, then subset
 detP_clean_filtered2 <- detP_clean[match(featureNames(gRatioSet_clean),rownames(detP_clean)),]
-keep_gratioset <- rowSums(detP_clean_filtered < 0.01) == ncol(gRatioSet_clean)
+keep_gratioset <- rowSums(detP_clean_filtered2 < 0.01) == ncol(gRatioSet_clean)
 gRatioSet_clean_filtered <- gRatioSet_clean[keep_gratioset,]
 ```
 
@@ -159,13 +163,13 @@ keep_betas_df <- as.data.frame(keep_betas)
 cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
            " probes failed in one or more samples"), sep = "<br>\n")
 
-dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-dim_Betas_filtered <- dim(Betas_clean_filtered)
+dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
 step_number <- c("5", "5")
 step <- c("Filter technical", "Filter technical")
 data_class <- c("gRatioSet", "Betas")
-samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
 
 table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
 summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -188,13 +192,13 @@ keep_betas_df <- as.data.frame(keep_betas)
 cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
            " probes were on the X chromosome"), sep = "<br>\n")
 
-dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-dim_Betas_filtered <- dim(Betas_clean_filtered)
+dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
 step_number <- c("6", "6")
 step <- c("Filter X Chromosome", "Filter X Chromosome")
 data_class <- c("gRatioSet", "Betas")
-samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
 
 table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
 summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -215,13 +219,13 @@ keep_betas_df <- as.data.frame(keep_betas)
 cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
            " probes were on the Y chromosome"), sep = "<br>\n")
 
-dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-dim_Betas_filtered <- dim(Betas_clean_filtered)
+dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
 step_number <- c("7", "7")
 step <- c("Filter Y Chromosome", "Filter Y Chromosome")
 data_class <- c("gRatioSet", "Betas")
-samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
 
 table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
 summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -235,13 +239,13 @@ Betas_clean_filtered <- Betas_clean_filtered[rownames(Betas_clean_filtered) %in%
 ```
 
 ```{r, info output for user probes affected by snps, results='asis'}
-dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-dim_Betas_filtered <- dim(gRatioSet_clean_filtered)
+dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+dim_Betas_clean_filtered <- dim(gRatioSet_clean_filtered)
 step_number <- c("8", "8")
 step <- c("Filter SNP Affected", "Filter SNP Affected")
 data_class <- c("gRatioSet", "Betas")
-samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
   
 table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
 summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -283,13 +287,13 @@ if (user_choices$array_type == "v1") {
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
              " probes were cross-hybridizing according to Chen et al."), sep = "<br>\n")
   
-  dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-  dim_Betas_filtered <- dim(Betas_clean_filtered)
+  dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+  dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
   step_number <- c("9", "9")
   step <- c("Filter Cross-Hybridizing Chen", "Filter Cross-Hybridizing Chen")
   data_class <- c("gRatioSet", "Betas")
-  samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-  probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+  samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+  probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
   
   table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
   summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -305,8 +309,8 @@ if (user_choices$array_type == "v1") {
   keep_betas <- !(rownames(Betas_clean_filtered) %in% exclude_McCartney$V1)
   Betas_clean_filtered <- Betas_clean_filtered[keep_betas,]
   
-  keep_gratioset <- !(featureNames(dim_gRatioSet_filtered) %in% exclude_McCartney$V1) 
-  dim_gRatioSet_filtered <- dim_gRatioSet_filtered[keep_gratioset,]
+  keep_gratioset <- !(featureNames(dim_gRatioSet_clean_filtered) %in% exclude_McCartney$V1) 
+  gRatioSet_clean_filtered <- gRatioSet_clean_filtered[keep_gratioset,]
 }
 ```
 
@@ -316,13 +320,13 @@ if (user_choices$array_type == "v1") {
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
              " probes may be cross-hybridizing according to McCartney et al."), sep = "<br>\n")
   
-  dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-  dim_Betas_filtered <- dim(Betas_clean_filtered)
+  dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+  dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
   step_number <- c("10", "10")
   step <- c("Filter Cross-Hybridizing McCartney", "Filter Cross-Hybridizing McCartney")
   data_class <- c("gRatioSet", "Betas")
-  samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-  probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+  samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+  probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
   
   table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
   summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -351,13 +355,13 @@ if (user_choices$array_type == "v1") {
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(),
              " probes were polymorphic"), sep = "<br>\n")
   
-  dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-  dim_Betas_filtered <- dim(Betas_clean_filtered)
+  dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+  dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
   step_number <- c("11", "11")
   step <- c("Filter Polymorphic", "Filter Polymorphic")
   data_class <- c("gRatioSet", "Betas")
-  samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-  probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+  samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+  probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
   
   table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
   summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -369,10 +373,10 @@ saveRDS(summary_table_preprocessing, paste0(user_choices$project_name, "/reports
 
 ```{r, filter out mapping inaccuracies, include=FALSE}
 if (user_choices$array_type == "v2") {
-  keep_betas <- !(rownames(Betas_clean_filtered) %in% v2_mapping_inacc$IlmnID)
+  keep_betas <- !(rownames(Betas_clean_filtered) %in% v2_mapping_inaccuracy$IlmnID)
   Betas_clean_filtered <- Betas_clean_filtered[keep_betas,]
   
-  keep_gratioset <- !(featureNames(gRatioSet_clean_filtered) %in% v2_mapping_inacc$IlmnID) 
+  keep_gratioset <- !(featureNames(gRatioSet_clean_filtered) %in% v2_mapping_inaccuracy$IlmnID) 
   gRatioSet_clean_filtered <- gRatioSet_clean_filtered[keep_gratioset,]
 }
 ```
@@ -383,13 +387,13 @@ if (user_choices$array_type == "v2") {
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
              " CpGs show known mapping inaccuracies"), sep = "<br>\n")
   
-  dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-  dim_Betas_filtered <- dim(Betas_clean_filtered)
+  dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+  dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
   step_number <- c("12", "12")
   step <- c("Filter Mapping Inaccuracies", "Filter Mapping Inaccuracies")
   data_class <- c("gRatioSet", "Betas")
-  samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-  probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+  samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+  probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
   
   table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
   summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -414,13 +418,13 @@ if (user_choices$array_type == "v2") {
   cat(paste0(keep_betas_df %>% filter(keep_betas == FALSE) %>% nrow(), 
              " CpGs were flagged by Illumina"), sep = "<br>\n")
   
-  dim_gRatioSet_filtered <- dim(gRatioSet_clean_filtered)
-  dim_Betas_filtered <- dim(Betas_clean_filtered)
+  dim_gRatioSet_clean_filtered <- dim(gRatioSet_clean_filtered)
+  dim_Betas_clean_filtered <- dim(Betas_clean_filtered)
   step_number <- c("13", "13")
   step <- c("Filter Flagged Probes", "Filter Flagged Probes")
   data_class <- c("gRatioSet", "Betas")
-  samples <- c(dim_gRatioSet_filtered[2], dim_Betas_filtered[2])
-  probes <- c(dim_gRatioSet_filtered[1], dim_Betas_filtered[1])
+  samples <- c(dim_gRatioSet_clean_filtered[2], dim_Betas_clean_filtered[2])
+  probes <- c(dim_gRatioSet_clean_filtered[1], dim_Betas_clean_filtered[1])
   
   table_preprocessing_adding <- data.frame(step_number, step, data_class, samples, probes)
   summary_table_preprocessing <- bind_rows(summary_table_preprocessing, table_preprocessing_adding)
@@ -428,7 +432,7 @@ if (user_choices$array_type == "v2") {
 ```
 
 ```{r, save data, include=FALSE}
-save(gRatioSet_filtered, file = paste0(user_choices$project_name, "/processed_data/gRatioSet_clean_filtered.Rdata"))
+save(gRatioSet_clean_filtered, file = paste0(user_choices$project_name, "/processed_data/gRatioSet_clean_filtered.Rdata"))
 
 Betas_clean_filtered <- getBeta(gRatioSet_clean_filtered) # beta values
 save(Betas_clean_filtered, file = paste0(user_choices$project_name, "/processed_data/Betas_clean_filtered.Rdata"))
diff --git a/scripts/7_normalization.Rmd b/scripts/7_normalization.Rmd
index f9e8abf..583c20b 100644
--- a/scripts/7_normalization.Rmd
+++ b/scripts/7_normalization.Rmd
@@ -31,7 +31,7 @@ knitr::opts_knit$set(root.dir = paste0(user_choices$personal_path, "/"))
 knitr::opts_chunk$set(echo = FALSE)
 
 if (user_choices$array_type == "v2") {
-  if (!("jokergoo/IlluminaHumanMethylationEPICv2manifest" %in% rownames(installed.packages()))) {
+  if (!("IlluminaHumanMethylationEPICv2manifest" %in% rownames(installed.packages()))) {
     BiocManager::install("jokergoo/IlluminaHumanMethylationEPICv2manifest")
   }
   if (!("IlluminaHumanMethylationEPICv2anno.20a1.hg38" %in% rownames(installed.packages()))) {
@@ -84,27 +84,23 @@ save(gRatioSet_clean_filtered_quantile, file = paste0(user_choices$project_name,
 
 Betas_clean_filtered_quantile <- getBeta(gRatioSet_clean_filtered_quantile)
 save(Betas_clean_filtered_quantile, file = paste0(user_choices$project_name, 
-                                         "/processed_data/Betas_clean_filtered_quantile.Rdata"))
-Ms_clean_filtered_qunatile <- getM(gRatioSet_clean_filtered_quantile)
-save(Ms_clean_filtered_qunatile, file = paste0(user_choices$project_name, 
-                                        "/processed_data/Ms_clean_filtered_qunatile.Rdata"))
+                                        "/processed_data/Betas_clean_filtered_quantile.Rdata"))
+Ms_clean_filtered_quantile <- getM(gRatioSet_clean_filtered_quantile)
+save(Ms_clean_filtered_quantile, file = paste0(user_choices$project_name, 
+                                        "/processed_data/Ms_clean_filtered_quantile.Rdata"))
 # further normalization with BMIQ:
-probeType <- as.data.frame(annotations_clean_filtered[rownames(Betas_clean_filtered_quantile),c("Name","Type")])
+probeType <- as.data.frame(subset(annotations_clean_filtered, Name %in% rownames(Betas_clean_filtered_quantile), select=c("Name","Type")))
 probeType$probeType = ifelse(probeType$Type %in% "I", 1, 2)
 Betas_clean_filtered_quantile_bmiq <- apply(Betas_clean_filtered_quantile[,1:length(colnames(Betas_clean_filtered_quantile))],2,
                                    function(a) BMIQ(a,probeType$probeType,plots=FALSE)$nbeta) # sourced from script "BMIQ_1.6_Teschendorff.R"
-save(Betas_clean_filtered_quantile_bmiq, file = paste0(user_choices$project_name,
-                                              "/processed_data/Betas_clean_filtered_quantile_bmiq.Rdata"))
+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}
+```{r normalize unfiltered probes, include=FALSE}
 # Normalization of unfiltered data
-
 # 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")
+gRatioSet_clean_unfiltered_quantile <- preprocessQuantile(RGSet_clean)
 save(gRatioSet_clean_unfiltered_quantile, file = paste0(user_choices$project_name, 
                                          "/processed_data/gRatioSet_clean_unfiltered_quantile.Rdata")) # output: GenomicRatioSet
 
diff --git a/scripts/8_removal_of_outliers_detect_batch_effects.Rmd b/scripts/8_removal_of_outliers_detect_batch_effects.Rmd
index 6bbf7ec..8d176f6 100644
--- a/scripts/8_removal_of_outliers_detect_batch_effects.Rmd
+++ b/scripts/8_removal_of_outliers_detect_batch_effects.Rmd
@@ -46,7 +46,7 @@ start_time <- Sys.time()
 
 
 ```{r setup, include=FALSE}
-source("../data/calculate_pc_cutoff.R") # source function for determining PC cutoff 
+source("../data/Calculate_PC_Cutoff.R") # source function for determining PC cutoff 
 needed_packages <- c("BiocManager", "dplyr", "knitr", "rmarkdown", "tibble", "ggplot2", 
                      "ggrepel", "broom", "gplots", "tidyr", "sva", "methods")
 user_choices <- readRDS("../data/user_choices.rds")
diff --git a/scripts/9_correction_batch_effects_filtered.Rmd b/scripts/9_correction_batch_effects_filtered.Rmd
index a8e1daf..17500de 100644
--- a/scripts/9_correction_batch_effects_filtered.Rmd
+++ b/scripts/9_correction_batch_effects_filtered.Rmd
@@ -41,7 +41,7 @@ start_time <- Sys.time()
 ```
 
 ```{r setup, include=FALSE}
-source("../data/calculate_pc_cutoff.R") # source function for determining PC cutoff 
+source("../data/Calculate_PC_Cutoff.R") # source function for determining PC cutoff 
 needed_packages <- c("BiocManager", "dplyr", "knitr", "rmarkdown", "tibble", "ggplot2", 
                      "ggrepel", "broom", "gplots", "tidyr", "sva", "methods")
 user_choices <- readRDS("../data/user_choices.rds")
@@ -79,7 +79,7 @@ phenotype_data <- readRDS(paste0(user_choices$project_name, "/processed_data/phe
 
 ## Correction of technical batch effects with ComBat
 
-```{r, correction of first batch effect and PCA}
+```{r, correction of first batch effect and PCA, results='asis'}
 m_values <- apply(Betas_clean_filtered_quantile_bmiq, 2, function(x) log2((x)/(1-x))) # get M-values
 
 if (correction_variable_1 == "PLEASE FILL IN"){
@@ -240,7 +240,11 @@ if (correction_variable_1 == "PLEASE FILL IN"){
 }
 ```
 
-```{r, correction of second batch effect and PCA}
+```{r, print p-value table for batch effects after correction of first batch effect variable}
+anova_lm_pvalue_df %>% paged_table()
+```
+
+```{r, correction of second batch effect and PCA, results='asis'}
 if (correction_variable_2 == "PLEASE FILL IN"){
   print("No second batch correction variable was specified by user, data remains unchanged")
 } else {
@@ -399,6 +403,10 @@ if (correction_variable_2 == "PLEASE FILL IN"){
 }
 ```
 
+```{r, print p-value table for batch effects after correction of second batch effect variable}
+anova_lm_pvalue_df %>% paged_table()
+```
+
 ```{r, correction of third batch effect and PCA}
 if (correction_variable_3 == "PLEASE FILL IN"){
   print("No third batch correction variable was specified by user, data remains unchanged")
@@ -557,6 +565,10 @@ if (correction_variable_3 == "PLEASE FILL IN"){
 }
 ```
 
+```{r, print p-value table for batch effects after correction of third batch effect variable}
+anova_lm_pvalue_df %>% paged_table()
+```
+
 ```{r, convertion M to Beta and save, include=FALSE}
 if(exists("m_values_combat_3")){
   corrected_data <- m_values_combat_3