diff --git a/README.md b/README.md
index 55fb7a4..6517912 100644
--- a/README.md
+++ b/README.md
@@ -19,8 +19,8 @@ Assuming you have completed all pre-processing and normalization procedures, her
     + Ensure your methylation data is loaded into R's Global Environment as a numeric matrix in *probe by sample* form: with probe IDs as your row names and sample IDs as the column names
     + Your phenotype / clinical data should be a data frame with a column called `Sample` (spelled exactly with an upper-case "S"); these sample IDs should match the column names of the methylation data
 2. **Pre-Defined Regions**: Load the list of pre-calculated regions of "contiguous" CpGs which matches your Illumina data type. We have pre-calculated some of these lists of regions. We used the `CloseByAllRegions()` function with `maxGap = 200` (genomic locations within 200 base pairs are placed in the same cluster) and `minCpGs = 3` (we need at least 3 CpGs to retain the location). These data files are:
-    + Both Genic and Inter-genic regions for 450k array: downlaod the supplemental data file [hg19 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/450k_All_3_200_hg19.rds) or [hg38 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/450k_All_3_200_hg38.rds)
-    + Both Genic and Inter-genic regions, EPIC array: downlaod the supplemental data file [hg19 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/EPIC_10b4_All_3_200_hg19.rds) or [hg38 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/EPIC_10b4_All_3_200_hg38.rds)
+    + For 450k array analysis: downlaod the supplemental data file [hg19 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/450k_All_3_200_hg19.rds) or [hg38 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/450k_All_3_200_hg38.rds)
+    + For EPIC array analysis: downlaod the supplemental data file [hg19 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/EPIC_10b4_All_3_200_hg19.rds) or [hg38 regions](https://github.com/TransBioInfoLab/coMethDMR_data/blob/main/data/EPIC_10b4_All_3_200_hg38.rds)
     + The code that generated the above files are here 
 3. **Adjust Methylation for Covariates** with the `GetResiduals()` function; your methylation values may be confounded by clinical variables unrelated to your treatment, such as sex, age, or even [the square of age](https://www.nature.com/articles/s41598-021-88504-0)
     + *Input*: your methylation data and covariates from **Step 1**