@@ -617,16 +617,16 @@ negative (lower is better) signature.

   expression PCs. 

   Positive signature.

 * EXP_QC_COR: Removal of Alignment Artifacts. 

-  Maximum squared Spearman correlation between first 3 expression PCs and 

+  R^2 measure for regression of first 3 expression PCs on 

   first `k_qc` QPCs. 

   Negative signature.

 * EXP_UV_COR: Removal of Expression Artifacts. 

-  Maximum squared Spearman correlation between first 3 expression PCs and 

+  R^2 measure for regression of first 3 expression PCs on 

   first 3 PCs of the negative control (specified by `eval_negcon` or 

   `ruv_negcon` by default) sub-matrix of the original (raw) data. 

   Negative signature.

 * EXP_WV_COR: Preservation of Biological Variance.

-  Maximum squared Spearman correlation between first 3 expression PCs and

+  R^2 measure for regression of first 3 expression PCs on 

   first 3 PCs of the positive control (specified by `eval_poscon`) 

   sub-matrix of the original (raw) data. 

   Positive signature.

@@ -648,7 +648,7 @@ my_scone <- scone(my_scone,

                   run=TRUE,

                   eval_kclust = 2:6,stratified_pam = TRUE,

                   return_norm = "in_memory",

-                  zero = "preadjust")

+                  zero = "postadjust")

 ```

@@ -665,9 +665,9 @@ In the call above, we have set the following parameter arguments:

   Store all normalized matrices in addition to evaluation data. 

   Otherwise normalized data is not returned in the resulting 

   object.

-* zero = "preadjust". 

-  Restore data entries that are originally zeroes back to zero

-  after the scaling step.

+* zero = "postadjust". 

+  Restore data entries that are originally zeroes / negative after 

+  normalization to zero after the adjustment step.

 The output will contain various updated elements:

@@ -695,7 +695,7 @@ be recomputed.

 # Step 3: Selecting a normalization for downstream analysis

 Based on our sorting criteria, it would appear that

-`none,fq,no_uv,no_bio,no_batch` performs well compared to other normalization

+`none,uq,ruv_k=1,no_bio,no_batch` performs well compared to other normalization

 workflows. A useful way to visualize this method with respect to others is the

 `biplot_color` function