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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This document contains all the code used to analyze the unpublished data from the ….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:

library(dplyr)
library(reshape2)
library(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:

library(rmarkdown)
library(knitr) 

Data generation

read mapping with Kallisto

read mapping summary

# create a graph of your read mapping stats if you want

annotation with Ensembl

# all the code for anootating transcripts (Biomart or Ensembl package)

importing data into R

# importing data into R using Tximport package

Preprocessing and normalization

study design

#read in your study design file from local directory

raw data

#read in your Txi_gene object and graph raw data

filtered data

#filter out lowly expressed genes or transcripts and regraph

normalized data

#normalize data using 'calcNormFactors' function, then regraph

Exploratory data analysis

Principal component analysis (PCA)

#carry out PCA of filtered normalized data and graph

Differential gene analysis

set up model matrix

#set-up your experimental design using the model.matrix

mean-variance and linear model

#use Limma VOOM to model mean-vairance trend

set up contrasts

#fit linear model to deata and set-up contrast matrix with pairwise comparisons of interest

Bayesan stats

#extract bayesian stats for linear model fit

Top 10 DEGS

#display top N genes for a single pairwise comparison 
#insert additional code chuncks for additional pairwise comparisons

Volcano plot - infected vs double neg

#make volcano plot of pairwise comparison of interest
#insert additional code chuncks for additional pairwise comparisons=

Venn

#run 'decideTests' function and plot results as Venn diagram

DEGs

#extract DEGs and create data frame with group averages and logFCs.  Display as table

Visualizing DEGs

heatmap of all DEGs

#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

# Read in text file with results of GO enrichment carried out on DAVID or other websites

GSEA with CAMERA

Session Info

**Session Info:** R version 3.4.3 (2017-11-30) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS High Sierra 10.13.1

Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] knitr_1.19 rmarkdown_1.8
[3] patchwork_0.0.1 STRINGdb_1.18.0
[5] scatterD3_0.8.1 RColorBrewer_1.1-2
[7] gplots_3.0.1 edgeR_3.20.7
[9] limma_3.34.6 DT_0.3
[11] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.2.0
[13] AnnotationFilter_1.2.0 GenomicFeatures_1.30.1
[15] GenomicRanges_1.30.1 GenomeInfoDb_1.14.0
[17] tximport_1.6.0 Biostrings_2.46.0
[19] XVector_0.18.0 readr_1.1.1
[21] GSVA_1.26.0 GSEABase_1.40.1
[23] graph_1.56.0 annotate_1.56.1
[25] XML_3.98-1.9 AnnotationDbi_1.40.0
[27] IRanges_2.12.0 S4Vectors_0.16.0
[29] Biobase_2.38.0 BiocGenerics_0.24.0
[31] ggvis_0.4.3 ggplot2_2.2.1.9000
[33] tibble_1.4.2 reshape2_1.4.3
[35] dplyr_0.7.4

loaded via a namespace (and not attached): [1] ProtGenerics_1.10.0 bitops_1.0-6
[3] matrixStats_0.53.0 bit64_0.9-7
[5] progress_1.1.2 httr_1.3.1
[7] rprojroot_1.3-2 tools_3.4.3
[9] backports_1.1.2 R6_2.2.2
[11] KernSmooth_2.23-15 DBI_0.7
[13] lazyeval_0.2.1 colorspace_1.3-2
[15] prettyunits_1.0.2 RMySQL_0.10.13
[17] chron_2.3-52 bit_1.1-12
[19] curl_3.1 compiler_3.4.3
[21] DelayedArray_0.4.1 rtracklayer_1.38.3
[23] caTools_1.17.1 scales_0.5.0.9000
[25] stringr_1.2.0 digest_0.6.14
[27] Rsamtools_1.30.0 pkgconfig_2.0.1
[29] htmltools_0.3.6 plotrix_3.7
[31] htmlwidgets_1.0 rlang_0.1.6.9003
[33] RSQLite_2.0 BiocInstaller_1.28.0
[35] shiny_1.0.5 bindr_0.1
[37] gtools_3.5.0 BiocParallel_1.12.0
[39] RCurl_1.95-4.10 magrittr_1.5
[41] GenomeInfoDbData_1.0.0 Matrix_1.2-12
[43] Rcpp_0.12.15 munsell_0.4.3
[45] proto_1.0.0 sqldf_0.4-11
[47] stringi_1.1.6 yaml_2.1.16
[49] SummarizedExperiment_1.8.1 zlibbioc_1.24.0
[51] plyr_1.8.4 AnnotationHub_2.10.1
[53] grid_3.4.3 blob_1.1.0
[55] gdata_2.18.0 lattice_0.20-35
[57] hash_2.2.6 hms_0.4.1
[59] locfit_1.5-9.1 pillar_1.1.0
[61] igraph_1.1.2 geneplotter_1.56.0
[63] biomaRt_2.34.2 glue_1.2.0
[65] evaluate_0.10.1 png_0.1-7
[67] httpuv_1.3.5 gtable_0.2.0
[69] gsubfn_0.6-6 assertthat_0.2.0
[71] mime_0.5 xtable_1.8-2
[73] shinythemes_1.1.1 ellipse_0.4.1
[75] GenomicAlignments_1.14.1 memoise_1.1.0
[77] bindrcpp_0.2 interactiveDisplayBase_1.16.0