Merge pull request #87 from bcbio/feature_rnaseq_qc_methods_shs

Add methods draft and restructure similarity analysis

inst/templates/rnaseq/01_quality_assesment/QC.Rmd

@@ -418,19 +418,6 @@ pca2 + scale_color_grafify(palette = "kelly")
 
 ```
 
-# Covariates analysis
-
-When there are multiple factors that can influence the results of a given experiment, it is useful to assess which of them is responsible for the most variance as determined by PCA. This method adapts the method described by Daily et al. for which they integrated a method to correlate covariates with principal components values to determine the importance of each factor. 
-
-```{r covariate-plot,fig.height=12, fig.width=10}
-## Remove non-useful columns output by nf-core
-coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness", "qualimap_5_3_bias"))])
-
-# Remove missing data
-coldat_2 <- na.omit(coldat_2)
-degCovariates(vst, metadata = coldat_2)
-```
-
 ## Hierarchical clustering
 
 Inter-correlation analysis (ICA) is another way to look at how well samples
@@ -461,6 +448,35 @@ p <- pheatmap(vst_cor,
 p
 ```
 
+# Covariates analysis
+
+When there are multiple factors that can influence the results of a given experiment, it is useful to assess which of them is responsible for the most variance as determined by PCA. This method adapts the method described by Daily et al. for which they integrated a method to correlate covariates with principal components values to determine the importance of each factor. 
+
+```{r covariate-plot,fig.height=12, fig.width=10}
+## Remove non-useful columns output by nf-core
+coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness", "qualimap_5_3_bias"))])
+
+# Remove missing data
+coldat_2 <- na.omit(coldat_2)
+degCovariates(vst, metadata = coldat_2)
+```
+
+# Conclusions
+
+
+
+# Methods
+
+RNA-seq counts were generated by the nf-core rnaseq pipeline [version] using Salmon (Patro et al. 2017). Downstream analyses were performed using `r version$version.string`. Counts were imported into R using DESeq2 version `r packageVersion("DESeq2")` (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2016). Heatmaps were generated by pheatmap (Kolde 2019).
+
+## R package references
+
+```{r citations}
+citation("DESeq2")
+citation("ggplot2")
+citation("pheatmap")
+```
+
 # R session
 
 List and version of tools used for the QC report generation.