vignette

vignettes/numbat.Rmd

@@ -176,7 +176,7 @@ There are a few parameters you can consider tuning to a specific dataset.
 ## Detecting clonal LOH
 In samples with high tumor purity (e.g., tumor cell lines) without matched normal cells, heterozygous SNPs are challenging to identify in regions of LoH, leading to decreased power of CNV detection. Regions of clonal LoH have decreased SNP density and can be identified by a specialized HMM (see `detect_clonal_loh` in function reference). You can set `call_clonal_loh = TRUE` to automatically identify and exclude regions of clonal deletions/LoH in the main workflow. Alternatively, you can manually run `detect_clonal_loh` and provide the detected segments as a dataframe (with columns `CHROM`, `seg`, `seg_start`, `seg_end`) via the `segs_loh` argument.
 
-Alternatively, if you have matched DNA data (e.g. WGS/WES) of the same individual, you can directly use the SNP profile genotyped from DNA, which should avoid the above problem. See [Using prephased SNP profiles](#using-prephased-snp-profiles) for details.
+In addition, if you have matched DNA data (e.g. WGS/WES) of the same individual, you can directly use the SNP profile genotyped from DNA, which should avoid the above problem. See [Using prephased SNP profiles](#using-prephased-snp-profiles) for details.
 
 ## Using existing CNV calls
 Sometimes users already have CNV calls from bulk WGS, WES, or array analysis. In this case, you can supply the existing CNV profile via `segs_consensus_fix` parameter to fix the CNV boundaries and states. To do so, you may provide a dataframe with the following columns: