Small/Large RNAseq of M.musculus cells and exosomes.

These documents are a series of Mus musculus analyses seeking to find changes in small-RNA and polyA-RNA across wild-type(4TI/Conventional) and mutant(IL1B/Inflammatory) samples.

This is presented in a few pieces:

1. Preprocessing The steps performed to preprocess the data.
2. Annotation Data shared by all experiments (annotations, genomes, etc).
3. MiRNA/mRNA Sample Estimation Sample estimation of the various samples.
4. Retest sample estimation A Retesting of sample estimation.
5. Combining Experiments Combining previous and current experiments into one.
6. Differential Expression Transcripts Differential expression analyses of transcripts.
7. Differential Expression miRNA Differential expression analyses of miRNA.
8. Mature Analyses Mature miRNA tests
9. Ontology Analyses Transcripts Ontology analyses, transcript edition.
10. Ontology Analyses miRNA Ontology analyses, miRNA edition.

1 TODO

1.1 2017-01-09

1. miRNA targets as per email from Lucia: “I was just wondering if you had ran the miRNA targets for the top 5 (not predicted, the predicted ones don’t have targets) and for all the miRNAs?”

1.2 2016-10-28

-1. Modify extract_significant_genes() to include some text stating exactly what happened when filtering. -1a.Modify combine_de_tables() to state exactly what model is in the data. 0. Copy new miRNA tables with hopefully improved ma plots. 1. PCA plots no labels. 2. Sequence mapping #s in the sample sheet. 3. Take raw reads and do a median by condition, then do a cpm() or something to get the number of mRNAs and miRNAs found by sample type above a given threshold. 4. miRNA targets completed.

1.3 2016-10-04

1. Decide on p-value to use
2. once #1 is done, send along final DE tables
3. Perform GO/KEGG analyses
4. miRNAtap targets
5. Modify GO/KEGG plots as per Lucia’s needs
6. Integrate miRNA-targets/mRNA/protein gene sets explicitly as genes, then perform GO/KEGG on that.

1.4 2016-09-23

1. Venn diagram of the DE-genes of contrasts showing similarities/differences among EdgeR/DESeq2/limma.

1.5 2016-09-15

1. Complete word doc
2. Print out contrasts as per 2016-09-08.c
   1. These are to be explicitly unswitched.
3. Choose some loci for IGV.
4. For the significant mature miRNAs, perform target prediction.

1.6 2016-09-08

1. Send Lucia document with materials/methods of what I did.
2. Perform a/b / c/d for exomut/cellmut / exowt/cellwt
3. Make sure we have clear contrasts of:

1. Exo(mut) - cell(mut)
2. Exot(wt) - cell(wt)
3. Cell(mut) - cell(wt)
4. Exo(mut) - Exo(wt) / Cell(mut) - Cell(wt)

4. Repeat b. for mRNA
5. Choose ways of showing if yes/no that the exosomes have specificity in (mi/m)RNA components.

1.7 2016-08

1. Sample previous isolations down to similar coverage and compare.
2. Complete re-re-re-processing of new samples with changed adapter. (done)
3. Look for unannotated miRNAs, check Embl website.
4. Merge previous data and this data
5. Ontology searches
6. miRNA target searches
7. Expand to other RNA species types (as per previous analyses)
8. Count numbers of miRNA observed in cells / exosomes.
9. Count numbers of transcripts observed in cells / exosomes.

2 Installation and setup

These are rmarkdown documents which make heavy use of the hpgltools package. The following section demonstrates how to set that up in a clean R environment.

## Use R's install.packages to install devtools.
install.packages("devtools")
## Use devtools to install hpgltools.
devtools::install_github("elsayedlab/hpgltools")
## Load hpgltools into the R environment.
library(hpgltools)
## Use hpgltools' autoloads_all() function to install the many packages used by hpgltools.

This document is rather short. In some others I did the preprocessing, shared, etc steps as ‘child’ documents of this and they would be appended here. In this case I did not.

pander::pander(sessionInfo())

R version 3.3.2 (2016-10-31)

**Platform:** x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: hpgltools(v.2017.01)

loaded via a namespace (and not attached): Rcpp(v.0.12.9), knitr(v.1.15.1), magrittr(v.1.5), roxygen2(v.5.0.1), BiocGenerics(v.0.20.0), devtools(v.1.12.0), munsell(v.0.4.3), colorspace(v.1.3-2), R6(v.2.2.0), foreach(v.1.4.3), stringr(v.1.1.0), plyr(v.1.8.4), tools(v.3.3.2), parallel(v.3.3.2), grid(v.3.3.2), Biobase(v.2.34.0), data.table(v.1.10.0), gtable(v.0.2.0), withr(v.1.0.2), htmltools(v.0.3.5), iterators(v.1.0.8), yaml(v.2.1.14), rprojroot(v.1.2), lazyeval(v.0.2.0), assertthat(v.0.1), digest(v.0.6.12), tibble(v.1.2), crayon(v.1.3.2), ggplot2(v.2.2.1), base64enc(v.0.1-3), codetools(v.0.2-15), testthat(v.1.0.2), evaluate(v.0.10), memoise(v.1.0.0), rmarkdown(v.1.3), stringi(v.1.1.2), pander(v.0.6.0), backports(v.1.0.5) and scales(v.0.4.1)