---
title: "Characterization of miRNA and isomiR molecules"
shorttitle: "isomiRs analysis"
author: "Lorena Pantano"
date: "`r BiocStyle::doc_date()`"
package: "`r BiocStyle::pkg_ver('isomiRs')`"
abstract: >
  isomiRs package version: `r packageVersion("isomiRs")`
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    toc_depth: 3
    fig_width: 5
bibliography: isomirs.bib
vignette: >
  %\VignetteIndexEntry{miRNA and isomiR analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding[utf8]{inputenc}
  %\VignetteKeywords{smallRNA, miRNAs, isomiRs, DifferentailExpression}
---

```{r setup, echo=FALSE, results="hide"}
library(BiocStyle)
knitr::opts_chunk$set(tidy=FALSE, cache=FALSE,
                      dev="png",
                      message=FALSE, error=FALSE,
                      warning=TRUE)
```	


[Lorena Pantano](lorena.pantano@gmail.com) - Harvard TH Chan School of Public Health, Boston, US


Georgia Escaramis - CIBERESP (CIBER Epidemiologia y Salud Publica)


## Introduction

miRNAs are small RNA fragments (18-23 nt long) that influence gene expression
during development and cell stability. Morin et al [@morin], discovered
isomiRs first time after sequencing human stem cells.

IsomiRs are miRNAs that vary slightly in sequence, which result from variations
in the cleavage site during miRNA biogenesis (5’-trimming and 3’-trimming
variants), nucleotide additions to the 3’-end of the mature miRNA
(3’-addition variants) and nucleotide modifications (substitution variants)[@emarti].

There are many tools designed for isomiR detection, however the majority are
web application where user can not control the analysis. The two main command
tools for isomiRs mapping are SeqBuster and sRNAbench [@barturen2014]. `r Biocpkg("isomiRs")`.
package is designed to analyze the output of SeqBuster tool or any other
tool after converting to the desire format.

## Citing isomiRs

If you use the package, please cite this paper [@isomirs].

## Input format

The input should be the output of SeqBuster-miraligner tool (*.mirna files). It
is compatible with
[mirTOP](http://github.com/mirtop/mirtop) tool as well,
which parses BAM files with alignments against miRNA precursors.

For each sample the file should have the following format:

```
seq                     name           freq mir           start end  mism  add  t5  t3
TGTAAACATCCTACACTCAGCT  seq_100014_x23  23  hsa-miR-30b-5p  17  40   0       0  0   GT
TGTAAACATCCCTGACTGGAA   seq_100019_x4   4   hsa-miR-30d-5p  6   26   13TC    0  0   g
TGTAAACATCCCTGACTGGAA   seq_100019_x4   4   hsa-miR-30e-5p  17  37   12CT    0  0   g
CAAATTCGTATCTAGGGGATT   seq_100049_x1   1   hsa-miR-10a-3p  63  81   0       TT 0   ata
TGACCTAGGAATTGACAGCCAGT seq_100060_x1   1   hsa-miR-192-5p  25  47   8GT     0  c   agt
```

This is the standard output of SeqBuster-miraligner tool, but can be converted from
any other tool having the mapping information on the precursors. Read more on
[miraligner manual](http://seqcluster.readthedocs.org/mirna_annotation.html).

## IsomirDataSeq class

This object will store all raw data from the input files and some processed
information used for visualization and statistical analysis. It is a subclass of `SummarizedExperiment` with `colData` and `counts` methods. 
Beside that, the object contains
raw and normalized counts from miraligner allowing to update the summarization of miRNA expression.

### Access data

The user can access the normalized count matrix with `counts(object, norm=TRUE)`.

You can browse for the same miRNA or isomiRs in all samples with `isoSelect` method.

```{r package-select,message=FALSE}
library(isomiRs)
data(mirData)
head(isoSelect(mirData, mirna="hsa-let-7a-5p", 1000))
```

`metadata(mirData)` contains two lists: `rawList` is a list with same
length than number of samples and stores the input files
for each sample; `isoList` is a list with same length than
number of samples and stores information for each isomiR type summarizing
the different changes for the different isomiRs (trimming at 3',
trimming a 5', addition and substitution). For instance, you can get
the data stored in `isoList` for sample 1 and 5' changes
with this code `metadata(ids)[["isoList"]][[1]]["t5sum"]`.


### isomiRs annotation

IsomiR names follows this structure:


* miRNA name
* type: ref if the sequence is the same than the miRNA reference. `iso` if the sequence has variations.
*t5 tag: indicates variations at 5' position. The naming contains two words: `direction - nucleotides`, where direction can be UPPER CASE NT (changes upstream of the 5' reference position) or LOWER CASE NT (changes downstream of the 5' reference position). `0` indicates no variation, meaning the 5' position is the same than the reference. After `direction`, it follows the nucleotide/s that are added (for upstream changes) or deleted (for downstream changes).
*t3 tag: indicates variations at 3' position. The naming contains two words: `direction - nucleotides`, where direction can be LOWER CASE NT (upstream of the 3' reference position) or UPPER CASE NT (downstream of the 3' reference position). `0` indicates no variation, meaning the 3' position is the same than the reference. After `direction`, it follows the nucleotide/s that are added (for downstream changes) or deleted (for upstream chanes).
*ad tag: indicates nucleotides additions at 3' position. The naming contains two words: `direction - nucleotides`, where direction is UPPER CASE NT (upstream of the 5' reference position). `0` indicates no variation, meaning the 3' position has no additions. After `direction`, it follows the nucleotide/s that are added.
*mm tag: indicates nucleotides substitutions along the sequences. The naming contains three words: `position-nucleotideATsequence-nucleotideATreference`.
*seed tag: same than `mm` tag, but only if the change happens between nucleotide 2 and 8.


In general nucleotides in UPPER case mean insertions respect to the reference sequence, and nucleotides in LOWER case mean deletions respect to the reference sequence.

## Reading input

We are going to use a small RNAseq data from human
brain samples [@pantano2016] to give some basic examples of isomiRs analyses.

In this data set we will find two groups:

*pc: 7 control individuals
*pt: 7 patients with Parkinson's Disease in early stage.


```{r package-load,message=FALSE}
library(isomiRs)
data(mirData)
```


The function `IsomirDataSeqFromFiles` needs a vector with the paths for each file
and a data frame with the design experiment similar to the one used for
a mRNA differential expression analysis. Row names of the data frame should
be the names for each sample in the same order than the list of files.

```{r package-plot-iso,message=FALSE,eval=FALSE}
ids <- IsomirDataSeqFromFiles(fn_list, design=de)
```

## Manipulation

### Descriptive analysis

You can plot isomiRs expression with `isoPlot`. In this figure you
will see how abundant is each type of isomiRs at different positions
considering the total abundance and the total number of sequences.
The `type` parameter controls what type of isomiRs to show. It can be
trimming (iso5 and iso3), addition (add) or substitution (subs) changes.

```{r package-plot-iso-t5,message=FALSE}
ids <- isoCounts(mirData)
isoPlot(ids, type="all")
```

### Count data

`isoCounts` gets the count matrix that can be used for many
different downstream analyses changing the way isomiRs are collapsed. The
following command will merge all isomiRs into one feature: the reference
miRNA.

```{r package-count,message=FALSE}
head(counts(ids))
```

The normalization uses `rlog` from `r Biocpkg("DESeq2")` package and
allows quick integration to another analyses like heatmap, clustering or PCA.


```{r package-norm,message=FALSE}
library(pheatmap)
ids = isoNorm(ids, formula = ~ condition)
pheatmap(counts(ids, norm=TRUE)[1:100,], 
         annotation_col = data.frame(colData(ids)[,1,drop=FALSE]),
         show_rownames = FALSE, scale="row")
```

### Annotation

To get a detail description for each isomiR, the function
`isoAnnotate` can return the naming, sequence and
importance value for each sample and isomiR. The importance
is calculated by:

$$importance = \frac{isomiR\_reads}{miRNA\_reads}$$

The columns are:

* seq: sequence of the isomiR
* uid: isomiR name
* edit_mature_position: showing the position at the mature
  sequence where the nucleotide change happened: 
  `position:nt_ref:nt_isomiR`.
* one column for each sample with the importance value

```{r package-isoannotation}
head(isoAnnotate(ids))
```


## Classification

### Differential expression analysis

The `isoDE` uses functions from `r Biocpkg("DESeq2")` package.
This function has parameters to create a matrix using only the reference
miRNAs, all isomiRs, or some of them.
This matrix and the design matrix are the inputs for DESeq2. The output
will be a DESeqDataSet object, allowing to generate any plot or table
explained in DESeq2 package vignette.

```{r package-de,message=FALSE}
dds <- isoDE(ids, formula=~condition)
library(DESeq2)
plotMA(dds)
head(results(dds, format="DataFrame"))
```

You can differentiate between reference sequences and isomiRs at 5' end
with this command:

```{r package-de-iso5,message=FALSE}
dds = isoDE(ids, formula=~condition, ref=TRUE, iso5=TRUE)
head(results(dds, tidy=TRUE))
```

Alternative, for more complicated cases or if you want to control
more the differential expression analysis paramters you can use
directly `r Biocpkg("DESeq2")` package feeding it with the output of
`counts(ids)` and `colData(ids)` like this:

```{r package-de-with-deseq2}
dds = DESeqDataSetFromMatrix(counts(ids),
                             colData(ids), design = ~condition)
```


### Supervised classification

Partial Least Squares Discriminant Analysis (PLS-DA) is a technique specifically
appropriate for analysis of high dimensionality data sets and multicollineality
[@perezenciso]. PLS-DA is a supervised method (i.e. makes use of class
labels) with the aim to provide a dimension reduction strategy in a situation
where we want to relate a binary response variable (in our case young or old
status) to a set of predictor variables. Dimensionality reduction procedure is
based on orthogonal transformations of the original variables (isomiRs) into a
set of linearly uncorrelated latent variables (usually termed as components)
such that maximizes the separation between the different classes in the first
few components [@xia]. We used sum of squares captured by the model (R2) as
a goodness of fit measure. We implemented this method using the
`DiscriMiner` into `isoPLSDA` function. The output
p-value of this function will tell about the statistical
significant of the group separation using miRNA expression data.
Moreover, the function `isoPLSDAplot`
helps to visualize the results. It will plot the samples using the
significant components (t1, t2, t3 ...) from the PLS-DA analysis and the
samples distribution along the components.


```{r package-pls}
ids = isoCounts(ids, iso5=TRUE, minc=10, mins=6)
ids = isoNorm(ids, formula = ~ condition)
pls.ids = isoPLSDA(ids, "condition", nperm = 2)
df = isoPLSDAplot(pls.ids)
```

The analysis can be done again using only the most important discriminant
isomiRS from the PLS-DA models based on the analysis. We used Variable
Importance for the Projection (VIP) criterion to select the most important
features, since takes into account the contribution of a specific predictor
for both the explained variability on the response and the explained
variability on the predictors.


```{r package-plsplot, message=FALSE, eval=FALSE}
pls.ids = isoPLSDA(ids,"condition", refinment = FALSE, vip = 0.8)
```


### Gene - miRNA integration

The package offers a correlation analysis of miRNA and gene expression data.
Having two SummarizedExperiments objects with their expression, the target
prediction for each miRNA, the function `isoNetwork` and `isoPlotNetwork` can
generate a summarized figure showing the relationship between expression
profile, miRNA repression and enrichment analysis:

```{r isonetwork, message=FALSE, error=FALSE, fig.height=12, fig.width=7}
# library(org.Mm.eg.db)
# library(clusterProfiler)
data(isoExample)

# ego <- enrichGO(row.names(assay(gene_ex_rse, "norm")),
#                 org.Mm.eg.db, "ENSEMBL", ont = "BP")
data = isoNetwork(mirna_ex_rse, gene_ex_rse, target = ma_ex,
                  enrich = ego, summarize = "group")
isoPlotNet(data,  minGenes = 4)
```


As an option, `org` can be `org.Mm.eg.db` and `genename` can be
`ENSEMBL` and it will run `enrcihGO` internally.

To create the ma_ex matrix, the user can use `findTargets`:

```{r findtarget}
mirna_ma <- data.frame(gene = names(gene_ex_rse)[1:20],
                       mir = names(mirna_ex_rse))
ma_ex <- findTargets(mirna_ex_rse, gene_ex_rse, mirna_ma)
head(ma_ex[,1:4])
```

And to get the `mirna_ma` data.frame with the miRNA-target information,
the user can use `mirna2targetscan` function:

```{r mirna2targetscan}
library(targetscan.Hs.eg.db)
mirna_ma <- mirna2targetscan(c("hsa-miR-34c-5p"))
head(mirna_ma)
```


## Session info

Here is the output of `sessionInfo` on the system on which
this document was compiled:

```{r sessionInfo}
sessionInfo()
```


# References
