From fc3775dc9236fd4e94288d530191c6ef73bfcaee Mon Sep 17 00:00:00 2001
From: Davis McCarthy <davismcc@gmail.com>
Date: Tue, 1 Oct 2019 19:07:46 +1000
Subject: [PATCH] Turning off some eval to build site
---
course_files/de-real.Rmd | 2 +-
course_files/imputation.Rmd | 2 +-
course_files/remove-conf.Rmd | 14 ++++++++------
3 files changed, 10 insertions(+), 8 deletions(-)
diff --git a/course_files/de-real.Rmd b/course_files/de-real.Rmd
index 4550333..2e56699 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$set(root.dir = normalizePath(".."))
```
diff --git a/course_files/imputation.Rmd b/course_files/imputation.Rmd
index 739c9c9..c6e1200 100644
--- a/course_files/imputation.Rmd
+++ b/course_files/imputation.Rmd
@@ -3,7 +3,7 @@ output: html_document
---
```{r setup, echo=FALSE}
-knitr::opts_chunk$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 9486212..b89c3d6 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.
--
GitLab