---
title: "Importing transcript abundance datasets with tximport"
author: "Michael I. Love, Charlotte Soneson, Mark D. Robinson"
date: "`r Sys.Date()`"
package: "`r packageVersion('tximport')`"
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    fig_width: 5
bibliography: library.bib
vignette: >
  %\VignetteIndexEntry{Importing transcript abundance datasets with tximport}
  %\VignetteEngine{knitr::rmarkdown}
---

Import and summarize transcript-level abundance estimates for
transcript- and gene-level analysis with Bioconductor packages, such as *edgeR*,
*DESeq2*, and *limma-voom*.
The motivation and methods for the functions
provided by the *tximport* package are described in the following
article [@Soneson2015]:

> Charlotte Soneson, Michael I. Love, Mark D. Robinson (2015):
> Differential analyses for RNA-seq: transcript-level estimates
> improve gene-level inferences. *F1000Research*
> http://dx.doi.org/10.12688/f1000research.7563.1

In particular, the *tximport* pipeline offers the following benefits:
(i) this approach corrects for potential changes in gene length across samples 
(e.g. from differential isoform usage) [@Trapnell2013Differential],
(ii) some of the upstream quantification methods (*Salmon*, *Sailfish*, *kallisto*) 
are substantially faster and require less memory
and disk usage compared to alignment-based methods that require
creation and storage of BAM files, and
(iii) it is possible to avoid discarding those fragments that can
align to multiple genes with homologous sequence, thus increasing
sensitivity [@Robert2015Errors].

```{r, echo=FALSE}
library(knitr)
opts_chunk$set(tidy=TRUE,message=FALSE)
```

## Import transcript-level estimates

We begin by locating some prepared files that contain
transcript abundance estimates for six samples, from the
*tximportData* package. The *tximport* pipeline will be nearly
identical for various quantification tools, usually only
requiring one change the `type` argument.
We begin with quantification files generated by the *Salmon*
software, and later show the use of *tximport*
with any of: 

* *Salmon* [@Patro2016Salmon]
* *Sailfish* [@Patro2014Sailfish]
* *kallisto* [@Bray2016Near]
* *RSEM* [@Li2011RSEM]

First, we locate the directory containing the files. (Here we use
`system.file` to locate the package directory, but for a typical use,
we would just provide a path, e.g. `"/path/to/dir"`.)

```{r}
library(tximportData)
dir <- system.file("extdata", package="tximportData")
list.files(dir)
```

Next, we create a named vector pointing to the quantification
files. We will create a vector of filenames first by reading in a
table that contains the sample IDs, and then combining this with `dir`
and `"quant.sf"`.

```{r}
samples <- read.table(file.path(dir,"samples.txt"), header=TRUE)
samples
files <- file.path(dir, "salmon", samples$run, "quant.sf")
names(files) <- paste0("sample",1:6)
all(file.exists(files))
```

Transcripts need to be associated with gene IDs for gene-level
summarization. If that information is present in the files, we can
skip this step. For Salmon, Sailfish, and kallisto the files only
provide the transcript ID.  We first make a data.frame called
`tx2gene` with two columns: 1) transcript ID and 2) gene ID. The column names do not
matter but this column order must be used.  The transcript ID must be
the same one used in the abundance files. 

Creating this `tx2gene` data.frame can be accomplished from a *TxDb* object
and the `select` function from the *AnnotationDbi* package. The
following code could be used to construct such a table:

```{r}
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
k <- keys(txdb, keytype="GENEID")
df <- select(txdb, keys=k, keytype="GENEID", columns="TXNAME")
tx2gene <- df[,2:1] # tx ID, then gene ID
```

Note: if you are using an *Ensembl* transcriptome, the easiest way to
create the `tx2gene` data.frame is to use the
[ensembldb](http://bioconductor.org/packages/ensembldb) packages.
The annotation packages can be found by version number, and use
the pattern `EnsDb.Hsapiens.vXX`. The `transcripts` function can
be used with `return.type="DataFrame"`, in order to obtain
something like the `df` object constructed in the code chunk above.
See the *ensembldb* package vignette for more details.

Here we read in a pre-constructed `tx2gene` table:

```{r}
tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))
head(tx2gene)
```

The *tximport* package has a single function for importing
transcript-level estimates.  The `type` argument is used to specify
what software was used for estimation ("kallisto", "salmon",
"sailfish", and "rsem" are implemented).  A simple list with
matrices, "abundance", "counts", and "length", is returned, where the
transcript level information is summarized to the gene-level.  The
"length" matrix can be used to generate an offset matrix for
downstream gene-level differential analysis of count matrices, as
shown below.

**Note**: While *tximport* works without any dependencies, it is
significantly faster to read in files using the *readr* package.  If
*tximport* detects that *readr* is installed, then it will use the
`readr::read_tsv` function by default. A change from version 1.2 to
1.4 is that the reader is not specified by the user anymore, but
chosen automatically based on the availability of the *readr*
package. Advanced users can still customize the import of files using
the `importer` argument.

```{r}
library(tximport)
library(readr)
txi <- tximport(files, type="salmon", tx2gene=tx2gene)
names(txi)
head(txi$counts)
```

We could alternatively generate counts from abundances, using the
argument `countsFromAbundance`, scaled to library size, `"scaledTPM"`,
or additionally scaled using the average transcript length, averaged
over samples and to library size, `"lengthScaledTPM"`.  Using either
of these approaches, the counts are not correlated with length, and so
the length matrix should not be provided as an offset for
downstream analysis packages. For more details on these approaches,
see the article listed under `citation("tximport")`.

We can avoid gene-level summarization by setting `txOut=TRUE`, giving
the original transcript level estimates as a list of matrices.

```{r}
txi.tx <- tximport(files, type="salmon", txOut=TRUE, tx2gene=tx2gene)
```

These matrices can then be summarized afterwards using the function
`summarizeToGene`. This then gives the identical list of matrices as using
`txOut=FALSE` (default) in the first `tximport` call.

```{r}
txi.sum <- summarizeToGene(txi.tx, tx2gene)
all.equal(txi$counts, txi.sum$counts)
```

## Salmon / Sailfish

Salmon or Sailfish `quant.sf` files can be imported by setting type to
`"salmon"` or `"sailfish"`.

```{r}
files <- file.path(dir,"salmon", samples$run, "quant.sf")
names(files) <- paste0("sample",1:6)
txi.salmon <- tximport(files, type="salmon", tx2gene=tx2gene)
head(txi.salmon$counts)
```

```{r}
files <- file.path(dir,"sailfish", samples$run, "quant.sf")
names(files) <- paste0("sample",1:6)
txi.sailfish <- tximport(files, type="sailfish", tx2gene=tx2gene)
head(txi.sailfish$counts)
```

*Note*: for previous version of Salmon or Sailfish, in which the
 `quant.sf` files start with comment lines, it is recommended to
 specify the `importer` argument as a function which reads in the 
 lines beginning with the header. For example:
 
```{r eval=FALSE}
txi <- tximport("quant.sf", type="none", txOut=TRUE,
                txIdCol="Name", abundanceCol="TPM",
                countsCol="NumReads", lengthCol="Length",
                importer=function(x) read_tsv(x, skip=8))
```

## Salmon / Sailfish with inferential replicates

If inferential replicates (Gibbs or bootstrap samples) are present in
expected locations relative to the `quant.sf` file, *tximport* will 
import these as well, if `txOut=TRUE`. *tximport* will not summarize
inferential replicate information to the gene-level. Here we
demonstrate using Salmon, run with only 5 Gibbs replicates (usually
more Gibbs samples would be useful for estimating variability).

```{r}
files <- file.path(dir,"salmon_gibbs", samples$run, "quant.sf")
names(files) <- paste0("sample",1:6)
txi.inf.rep <- tximport(files, type="salmon", txOut=TRUE)
names(txi.inf.rep)
names(txi.inf.rep$infReps)
dim(txi.inf.rep$infReps$sample1)
```

The *tximport* arguments `varReduce` and `dropInfReps` can be used to
summarize the inferential replicates into a single variance per
transcript and per sample, or to not import inferential replicates,
respectively. 

## kallisto

kallisto `abundance.h5` files can be imported by setting type to
`"kallisto"`. Note that this requires that you have the Bioconductor package 
[rhdf5](http://bioconductor.org/packages/rhdf5) installed.
(Here we only demonstrate reading in transcript-level information.)

```{r}
files <- file.path(dir, "kallisto_boot", samples$run, "abundance.h5")
names(files) <- paste0("sample",1:6)
txi.kallisto <- tximport(files, type="kallisto", txOut=TRUE)
head(txi.kallisto$counts)
```

## kallisto with inferential replicates

Because the `kallisto_boot` directory also has inferential replicate
information, it was imported as well (and because `txOut=TRUE`).
As with Salmon, inferential replicate information will not be
summarized to the gene level.

```{r}
names(txi.kallisto)
names(txi.kallisto$infReps)
dim(txi.kallisto$infReps$sample1)
```

## kallisto with TSV files

kallisto `abundance.tsv` files can be imported as well, but this is
typically slower than the approach above.

```{r}
files <- file.path(dir, "kallisto", samples$run, "abundance.tsv")
names(files) <- paste0("sample",1:6)
txi.kallisto.tsv <- tximport(files, type="kallisto", tx2gene=tx2gene)
head(txi.kallisto.tsv$counts)
```

## RSEM

RSEM `sample.genes.results` files can be imported by setting
type to `"rsem"`.

```{r}
files <- file.path(dir,"rsem", samples$run, paste0(samples$run, ".genes.results"))
names(files) <- paste0("sample",1:6)
txi.rsem <- tximport(files, type="rsem")
head(txi.rsem$counts)
```

## Use with downstream Bioconductor differential expression packages

**Note**: there are two suggested ways of importing estimates for use
with gene-level differential expression methods. The first method,
which we show below for *edgeR* and for *DESeq2*, is to use the
estimated counts from the quantification tools, and additionally to
use the transcript-level abundance estimates to calculate an offset
that corrects for changes to the average transcript length across
samples.  The code examples below accomplish these steps for you,
keeping track of appropriate matrices and calculating these
offsets. 

The second method is to use the `tximport` argument
`countsFromAbundance="lengthScaledTPM"` or `"scaledTPM"`, and then to
use the count matrix `txi$counts` directly as you would a regular
count matrix with these software.

## edgeR

An example of creating a `DGEList` for use with *edgeR* [@Robinson2010]:

```{r, results="hide", messages=FALSE}
library(edgeR)
```

```{r}
cts <- txi$counts
normMat <- txi$length
normMat <- normMat / exp(rowMeans(log(normMat)))
library(edgeR)
o <- log(calcNormFactors(cts/normMat)) + log(colSums(cts/normMat))
y <- DGEList(cts)
y$offset <- t(t(log(normMat)) + o)
# y is now ready for estimate dispersion functions
# see edgeR User's Guide
```

## DESeq2

An example of creating a `DESeqDataSet` for use with *DESeq2* [@Love2014]:

```{r, results="hide", messages=FALSE}
library(DESeq2)
```

The user should make sure the rownames of `sampleTable` align with the
colnames of `txi$counts`, if there are colnames. The best practice is
to read `sampleTable` from a CSV file, and to construct `files` from a
column of `sampleTable`, as was shown in the *tximport* examples above.

```{r}
sampleTable <- data.frame(condition=factor(rep(c("A","B"),each=3)))
rownames(sampleTable) <- colnames(txi$counts)
```

```{r}
dds <- DESeqDataSetFromTximport(txi, sampleTable, ~ condition)
# dds is now ready for DESeq()
# see DESeq2 vignette
```

## limma-voom

An example of creating a data object for use with *limma-voom* [@Law2014]. 
Because limma-voom does not use the offset matrix stored in
`y$offset`, we recommend using the scaled counts generated from
abundances, either `"scaledTPM"` or `"lengthScaledTPM"`:

```{r}
files <- file.path(dir,"salmon", samples$run, "quant.sf")
names(files) <- paste0("sample",1:6)
txi <- tximport(files, type="salmon",
                tx2gene=tx2gene,
                countsFromAbundance="lengthScaledTPM")
library(limma)
y <- DGEList(txi$counts)
y <- calcNormFactors(y)
design <- model.matrix(~ condition, data=sampleTable)
v <- voom(y, design)
# v is now ready for lmFit()
# see limma User's Guide
```

## Acknowledgments

The development of *tximport* has benefited from contributions and
suggestions from:

[Rob Patro](https://twitter.com/nomad421) (inferential replicates import),
[Andrew Parker Morgan](https://github.com/andrewparkermorgan) (RHDF5 support),
[Ryan C. Thompson](https://github.com/DarwinAwardWinner) (RHDF5 support),
[Matt Shirley](https://twitter.com/mdshw5) (ignoreTxVersion),
[Stephen Turner](https://twitter.com/genetics_blog),
[Richard Smith-Unna](https://twitter.com/blahah404),
[Rory Kirchner](https://twitter.com/RoryKirchner),
[Martin Morgan](https://twitter.com/mt_morgan),

## Session info

```{r}
sessionInfo()
```
 
## References