From 53f126b3f925144d34a22de8a5bdf6dd92fbe79f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Llu=C3=ADs?= Date: Mon, 3 Sep 2018 14:48:55 +0200 Subject: [PATCH] Correct rendering of titles Added space after the ## to correct visualize the titles --- vignettes/biobroom_vignette.Rmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vignettes/biobroom_vignette.Rmd b/vignettes/biobroom_vignette.Rmd index df30689..2ed307c 100644 --- a/vignettes/biobroom_vignette.Rmd +++ b/vignettes/biobroom_vignette.Rmd @@ -167,7 +167,7 @@ ggplot(tidy_results, aes(x=estimate, y=log(p.value), ggtitle("Volcano Plot For Airway Data via DESeq2") + theme_bw() ``` -##edgeR objects +## edgeR objects Here we use the `hammer` dataset included in `biobroom` package. `edgeR` can be used to perform differential expression analysis as follows: @@ -206,7 +206,7 @@ ggplot(tidy(et), aes(x=estimate, y=log(p.value), color=logCPM)) + theme_bw() ``` -##limma objects +## limma objects To demonstrate how `biobroom` works with `limma` objects, we generate some simulated data to test the tidier for `limma` objects. @@ -241,7 +241,7 @@ ggplot(tidy(eb), aes(x=estimate, y=log(p.value), color=statistic)) + ``` -##ExpressionSet objects +## ExpressionSet objects `tidy` can also be run directly on `ExpressionSet` objects, as described in another popular `Bioconductor` package `Biobase.` The `hammer` dataset we used above (which is included in the `biobroom` package) is an `ExpressionSet` object, so we'll use that to demonstrate. @@ -265,7 +265,7 @@ ggplot(tidy(hammer, addPheno=TRUE), aes(x=protocol, y=log(value))) + geom_boxplot() + ggtitle("Boxplot Showing Effect of Protocol on Expression") ``` -##MSnSet Objects +## MSnSet Objects `tidy` can also be run directly on `MSnSet` objects from `MSnbase`, which as specialised containers for quantitative proteomics data.