This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. # Abstract This document contains all the code used to analyze the unpublished data from the .... # R packages **These are the R/bioconductor packages used for this analysis:** ```{r packages, message=FALSE, warning=FALSE, results='hide'} library(dplyr) library(reshape2) library(as_tibble) library(ggplot2) library(ggvis) library(GSEABase) library(GSVA) library(reshape2) library(readr) library(Biostrings) library(tximport) library(ensembldb) library(EnsDb.Mmusculus.v79) library(DT) library(limma) library(edgeR) library(gplots) library(RColorBrewer) library(scatterD3) library(STRINGdb) library(patchwork) ``` **This dynamic html summary report was compiled in Rmarkdown using the following packages:** ```{r packages.continued, results='hide', message=FALSE, warning=FALSE} library(rmarkdown) library(knitr) ``` # Data generation ## read mapping with Kallisto ```{r read mapping, message=FALSE, warning=FALSE, echo=FALSE, eval=FALSE} # paste you code for aligning reads here ``` ## read mapping summary ```{r mapping stats, message=FALSE, warning=FALSE} # create a graph of your read mapping stats if you want ``` ## annotation with Ensembl ```{r annotation, message=FALSE, warning=FALSE, eval=FALSE} # all the code for anootating transcripts (Biomart or Ensembl package) ``` ## importing data into R ```{r data import, message=FALSE, warning=FALSE, eval=FALSE} # importing data into R using Tximport package ``` # Preprocessing and normalization ## study design ```{r design, message=FALSE, warning=FALSE} #read in your study design file from local directory ``` ## raw data ```{r raw, message=FALSE, warning=FALSE} #read in your Txi_gene object and graph raw data ``` ## filtered data ```{r filtering, message=FALSE, warning=FALSE} #filter out lowly expressed genes or transcripts and regraph ``` ## normalized data ```{r normalize, message=FALSE, warning=FALSE} #normalize data using 'calcNormFactors' function, then regraph ``` # Exploratory data analysis ## Principal component analysis (PCA) ```{r PCA, message=FALSE, warning=FALSE} #carry out PCA of filtered normalized data and graph ``` # Differential gene analysis ## set up model matrix ```{r model, message=FALSE, warning=FALSE} #set-up your experimental design using the model.matrix ``` ## mean-variance and linear model ```{r linear model, message=FALSE, warning=FALSE} #use Limma VOOM to model mean-vairance trend ``` ## set up contrasts ```{r contrasts, message=FALSE, warning=FALSE} #fit linear model to deata and set-up contrast matrix with pairwise comparisons of interest ``` ## Bayesan stats ```{r Bayes, message=FALSE, warning=FALSE} #extract bayesian stats for linear model fit ``` ## Top 10 DEGS ```{r topTable, message=FALSE, warning=FALSE} #display top N genes for a single pairwise comparison #insert additional code chuncks for additional pairwise comparisons ``` ## Volcano plot - infected vs double neg ```{r volcano1, message=FALSE, warning=FALSE} #make volcano plot of pairwise comparison of interest #insert additional code chuncks for additional pairwise comparisons= ``` ## Venn ```{r Venn, message=FALSE, warning=FALSE} #run 'decideTests' function and plot results as Venn diagram ``` ## DEGs ```{r DEGs, message=FALSE, warning=FALSE} #extract DEGs and create data frame with group averages and logFCs. Display as table ``` # Visualizing DEGs ## heatmap of all DEGs ```{r heatmap, message=FALSE, warning=FALSE} #heatmap of all DEGs #create additional heatmaps for co-expressed modules or a priori list of genes ``` # Functional enrichment analysis ## Summary of GO enrichment analysis of DEGs ```{r GO bubble, message=FALSE, warning=FALSE} # Read in text file with results of GO enrichment carried out on DAVID or other websites ``` ## GSEA with CAMERA ```{r GSEA, message=FALSE, warning=FALSE, eval= FALSE, echo=FALSE} # carry out GSEA ``` # Session Info **Session Info:** ```{r sessionInfo, results='asis', message=FALSE, warning=FALSE, echo=FALSE} sessionInfo() ```