diff --git a/course_files/de-real.Rmd b/course_files/de-real.Rmd
index 4550333a2178cd4a8c7216de231f24fecf18a247..2e566998fd770e6e1470629bd7f70dedda974250 100644
--- a/course_files/de-real.Rmd
+++ b/course_files/de-real.Rmd
@@ -4,7 +4,7 @@ output: html_document
 ---
 
 ```{r setup, echo=FALSE}
-knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=TRUE)
+knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=FALSE)
 knitr::opts_knit$set(root.dir = normalizePath(".."))
 ```
 
diff --git a/course_files/imputation.Rmd b/course_files/imputation.Rmd
index 739c9c9d4233c3d2f2e184f87a558264a06fd89d..c6e1200caf09fd80e4c365b8a7997b3a22486f2e 100644
--- a/course_files/imputation.Rmd
+++ b/course_files/imputation.Rmd
@@ -3,7 +3,7 @@ output: html_document
 ---
 
 ```{r setup, echo=FALSE}
-knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=TRUE)
+knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=FALSE)
 knitr::opts_knit$set(root.dir = normalizePath(".."))
 ```
 
diff --git a/course_files/remove-conf.Rmd b/course_files/remove-conf.Rmd
index 9486212c5e742a0400c6f1b34343b75cf1c5c6b5..b89c3d68700927c5a3cbfbe3590304f8a57120f8 100644
--- a/course_files/remove-conf.Rmd
+++ b/course_files/remove-conf.Rmd
@@ -125,7 +125,7 @@ residuals (this may be reasonable for normalized log-counts in many cases; but
 it may not be---debate continues in the literature), then we can apply `limma`
 to regress out (known) unwanted sources of variation as follows.
 
-```{r limma-lm}
+```{r limma-lm, eval=FALSE}
 ## fit a model just accounting for batch
 lm_design_batch <- model.matrix(~0 + batch, data = colData(umi.qc))
 fit_lm_batch <- lmFit(logcounts(umi.qc), lm_design_batch)
@@ -160,7 +160,7 @@ __Exercise 2__
 Perform LM correction for each individual separately. Store the final corrected
 matrix in the `lm_batch_indi` slot.
 
-```{r limma-lm-indi, echo=TRUE}
+```{r limma-lm-indi, echo=TRUE, eval=FALSE}
 ## define cellular detection rate (cdr), i.e. proportion of genes expressed in each cell
 umi.qc$cdr <- umi.qc$total_features_by_counts_endogenous / nrow(umi.qc)
 ## fit a model just accounting for batch by individual
@@ -273,10 +273,12 @@ $W$, $\alpha$, $\beta$, and $k$ is infeasible. For a given $k$, instead the
 following three approaches to estimate the factors of unwanted variation $W$ are
 used:
 
-* _RUVg_ uses negative control genes (e.g. ERCCs), assumed to have constant expression across samples;
-* _RUVs_ uses centered (technical) replicate/negative control samples for which the covariates of interest are
-constant;
-* _RUVr_ uses residuals, e.g., from a first-pass GLM regression of the counts on the covariates of interest.
+* _RUVg_ uses negative control genes (e.g. ERCCs), assumed to have constant
+  expression across samples;
+* _RUVs_ uses centered (technical) replicate/negative control samples for which
+the covariates of interest are constant;
+* _RUVr_ uses residuals, e.g., from a first-pass GLM regression of the counts on
+  the covariates of interest.
 
 We will concentrate on the first two approaches.