Correctly rendering the references in the vignettes (fixed missing bi…

…b in the yaml)

vignettes/detailed_example.Rmd

@@ -5,6 +5,7 @@ vignette: >
    %\VignetteIndexEntry{Detailed Example}
    %\VignetteEngine{knitr::rmarkdown}
    \usepackage[utf8]{inputenc}
+bibliography: references.bib
 ---
 
 ```{r, include=FALSE}
@@ -19,7 +20,7 @@ library(immunedeconv)
 library(tibble)
 ```
 
-*Immunedeconv* ships with an example dataset with samples from four patients with metastatic melanoma published in @EPIC2017. 
+*Immunedeconv* ships with an example dataset with samples from four patients with metastatic melanoma published in [@EPIC2017]. 
 It is available from `immunedeconv::dataset_racle`. It contains a gene expression matrix (`dataset_racle$expr_mat`) generated using bulk RNA-seq and 'gold standard' estimates of immune cell contents profiled with FACS (`dataset_racle$ref`). We are going to use the bulk RNA-seq data to run the deconvolution methods and will compare the results to the FACS data later on.   
 
 The gene expression data is a matrix with HGNC symbols in rows and samples in columns:
@@ -127,4 +128,6 @@ result %>%
     theme_bw()
 ```
 
-(MCP counter does not provide estimates for CD4+ T cells.)
+(MCP counter does not provide estimates for CD4+ T cells.)
+
+# References {-}

vignettes/immunedeconv.Rmd

@@ -39,15 +39,15 @@ Such methods can, in general, be classified in two categories:
  * Marker gene-based approaches and
  * deconvolution-based approaches.
 
-```{r fig_deconvolution_concepts, echo=FALSE, fig.cap="*Fig by @Finotello2018.*"}
+```{r fig_deconvolution_concepts, echo=FALSE, fig.cap="*Fig by [@Finotello2018].*"}
 knitr::include_graphics("img/concepts_deconvolution.gif")
 ```
 
 Marker gene based approaches (a) are based on a list of genes (signature), that are characteristic for a cell type. By looking at the expression values of signature genes, every cell type is quantified independently, either using the gene expression values directly (MCP-counter) or by performing a statistical test for enrichment of the signatures (xCell).
 
 Deconvolution methods (b) formulate the problem as a system of equations that describe the gene expression of a sample as the weighted sum of the contributions of the different cell types.  By solving the inverse problem, cell type fractions can be inferred given a signature matrix and the mixed gene expression. This can be accomplished using $\nu$-Support Vector Regression (SVR) (CIBERSORT) constrained least square regression (quanTIseq, EPIC) or linear least square regression (TIMER).
 
-For more information, check out the review by @Finotello2018.
+For more information, check out the review by [@Finotello2018].
 
 
 # Run the deconvolution
@@ -89,7 +89,7 @@ epic
 ```
 
 ### Example
-For this example, we use a dataset of four melanoma patients from @EPIC2017. 
+For this example, we use a dataset of four melanoma patients from [@EPIC2017]. 
 ```{r, message=FALSE}
 res = deconvolute(immunedeconv::dataset_racle$expr_mat, "quantiseq")
 knitr::kable(res, digits=2)
@@ -190,13 +190,13 @@ res = deconvolute(immunedeconv::dataset_racle$expr_mat, "quantiseq") %>%
 knitr::kable(res, digits=2)
 ```
 
-The algorithm is explained in detail in the methods section of @sturm2019.
+The algorithm is explained in detail in the methods section of [@sturm2019].
 
 # Interpretation of scores
 In general, cell-type scores allow for the comparison (1) between samples, (2) between cell-types or (3) both. 
 Between-sample comparisons allow to make statements such as *"In patient A, there are more CD8+ T cells than in patient B"*. 
 Between-cell-type comparisons allow to make statements such as *"In a certain patient, there are more B cells than T cells"*.
-For more information, see our Benchmark paper (@sturm2019). 
+For more information, see our Benchmark paper ([@sturm2019]). 
 
 ### Methods that allow between-sample comparisons