---
title: "Tximeta: transcript quantification import with automatic metadata"
author: "Michael I. Love, Charlotte Soneson, Peter F. Hickey, Rob Patro"
date: "`r format(Sys.time(), '%m/%d/%Y')`"
output: 
  rmarkdown::html_document:
    highlight: tango
    toc: true
    toc_float: true
abstract: >
  Tximeta performs numerous annotation and metadata gathering tasks on
  behalf of users during the import of transcript quantifications from
  *Salmon*, *alevin*, or *piscem-infer* into R/Bioconductor. Metadata 
  and transcript ranges are added automatically, facilitating genomic 
  analyses and assisting in computational reproducibility.
bibliography: library.bib
vignette: |
  %\VignetteIndexEntry{Transcript quantification import with automatic metadata}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Introduction

The `tximeta` package [@Love2020] extends the `tximport` package
[@Soneson2015] for import of transcript-level quantification data into
R/Bioconductor. It automatically adds annotation metadata when the
RNA-seq data has been quantified with *Salmon* [@Patro2017] or 
[piscem-infer](https://piscem-infer.readthedocs.io/en/latest/), or the
scRNA-seq data quantified with *alevin* [@Srivastava2019]. To our
knowledge, `tximeta` is the only package for RNA-seq data import that
can automatically identify and attach transcriptome metadata based on
the unique sequence of the reference transcripts.
For more details on these packages -- including the motivation for
`tximeta` and description of similar work -- consult the
**References** below.

**Note:** `tximeta` requires that the **entire output** of
*Salmon* / *piscem-infer* / *alevin* is present and unmodified in
order to identify the provenance of the reference transcripts. In
general, it's a good idea to not modify or re-arrange the output
directory of bioinformatic software as other downstream software rely
on and assume a consistent directory structure. For sharing multiple
samples, one can use, for example, `tar -czf` to bundle up a set of
Salmon output directories, or to bundle one alevin output
directory. For tips on using `tximeta` with other quantifiers see the
[other quantifiers](#other_quantifiers) section below.

```{r echo=FALSE}
knitr::include_graphics("images/diagram.png")
```

# Tximeta import starts with sample table

The first step using `tximeta` is to read in the sample table, which
will become the *column data*, `colData`, of the final object,
a *SummarizedExperiment*. The sample table should contain all the
information we need to identify the *Salmon* quantification
directories. 
For *alevin* quantification, one should point to the `quants_mat.gz`
file that contains the counts for all of the cells (also, in order to
`tximeta` to work with *alevin* quantification, it requires that
*alevin* was run using gene IDs in the `tgMap` step and not gene
symbols).

Here we will use a *Salmon* quantification file in the
*tximportData* package to demonstrate the usage of `tximeta`. We do
not have a sample table, so we construct one in R. It is recommended
to keep a sample table as a CSV or TSV file while working on an
RNA-seq project with multiple samples.

```{r}
dir <- system.file("extdata/salmon_dm", package="tximportData")
files <- file.path(dir, "SRR1197474", "quant.sf") 
file.exists(files)
coldata <- data.frame(files, names="SRR1197474", condition="A", stringsAsFactors=FALSE)
coldata
```

`tximeta` expects at least two columns in `coldata`: 

1. `files` - a pointer to the `quant.sf` files
2. `names` - the unique names that should be used to identify samples

# Running tximeta

Normally, we would just run `tximeta` like so:

```{r eval=FALSE}
library(tximeta)
se <- tximeta(coldata)
```

However, to avoid downloading remote GTF files during this vignette,
we will point to a GTF file saved locally (in the *tximportData*
package). We link the transcriptome of the *Salmon* index to its
locally saved GTF. The standard recommended usage of `tximeta` would
be the code chunk above, or to specify a remote GTF source, not a
local one. **This following code is therefore not recommended for a
typically workflow, but is particular to the vignette code.**

```{r}
indexDir <- file.path(dir, "Dm.BDGP6.22.98_salmon-0.14.1")
fastaFTP <- c("ftp://ftp.ensembl.org/pub/release-98/fasta/drosophila_melanogaster/cdna/Drosophila_melanogaster.BDGP6.22.cdna.all.fa.gz",
              "ftp://ftp.ensembl.org/pub/release-98/fasta/drosophila_melanogaster/ncrna/Drosophila_melanogaster.BDGP6.22.ncrna.fa.gz")
gtfPath <- file.path(dir,"Drosophila_melanogaster.BDGP6.22.98.gtf.gz")
suppressPackageStartupMessages(library(tximeta))
makeLinkedTxome(indexDir=indexDir,
                source="LocalEnsembl",
                organism="Drosophila melanogaster",
                release="98",
                genome="BDGP6.22",
                fasta=fastaFTP,
                gtf=gtfPath,
                write=FALSE)
```

```{r message=FALSE}
library(tximeta)
```

```{r}
se <- tximeta(coldata)
```

# What happened? 

`tximeta` recognized the hashed checksum of the transcriptome that the files
were quantified against, it accessed the GTF file of the transcriptome
source, found and attached the transcript ranges, and added the
appropriate transcriptome and genome metadata.  A remote GTF is only
downloaded once, and a local or remote GTF is only parsed to build a
*TxDb* or *EnsDb* once: if `tximeta` recognizes that it has seen this *Salmon*
index before, it will use a cached version of the metadata and
transcript ranges.

Note the warning above that 5 of the transcripts are missing from the
GTF file and so are dropped from the final output. This is a problem
coming from the annotation source, and not easily avoided by
`tximeta`. 

# TxDb, EnsDb, and AnnotationHub

`tximeta` makes use of Bioconductor packages for storing
transcript databases as *TxDb* or *EnsDb* objects, which both are 
connected by default to `sqlite` backends.
For GENCODE and RefSeq GTF files, `tximeta` uses the *GenomicFeatures*
package [@granges] to parse the GTF and build a *TxDb*. For Ensembl
GTF files, `tximeta` will first attempt to obtain the correct *EnsDb*
object using *AnnotationHub*. The *ensembldb* package [@ensembldb]
contains classes and methods for extracting relevant data from Ensembl
files. If the *EnsDb* has already been made available on
AnnotationHub, `tximeta` will download the database directly, which
saves the user time parsing the GTF into a database (to avoid this,
set `useHub=FALSE`). If the relevant *EnsDb* is not available on
AnnotationHub, `tximeta` will build an *EnsDb* using *ensembldb* after
downloading the GTF file. Again, the download/construction of a
transcript database occurs only once, and upon subsequent usage of
*tximeta* functions, the cached version will be used.

# Pre-computed checksums

We plan to support a wide variety of sources and organisms for
transcriptomes with pre-computed checksums, though for now the
software focuses on predominantly human and mouse transcriptomes 

The following checksums are supported in this version of `tximeta`:

```{r echo=FALSE}
dir2 <- system.file("extdata", package="tximeta")
tab <- read.csv(file.path(dir2, "hashtable.csv"),
                stringsAsFactors=FALSE)
release.range <- function(tab, source, organism) {
  tab.idx <- tab$organism == organism & tab$source == source
  rels <- tab$release[tab.idx]
  if (organism == "Mus musculus" & source == "GENCODE") {
    paste0("M", range(as.numeric(sub("M","",rels))))
  } else if (source == "RefSeq") {
    paste0("p", range(as.numeric(sub(".*p","",rels))))
  } else {
    range(as.numeric(rels))
  }
}
dat <- data.frame(
  source=rep(c("GENCODE","Ensembl","RefSeq"),c(2,3,2)),
  organism=c("Homo sapiens","Mus musculus",
             "Drosophila melanogaster")[c(1:2,1:3,1:2)]
)
rng <- t(sapply(seq_len(nrow(dat)), function(i)
  release.range(tab, dat[i,1], dat[i,2])))
dat$releases <- paste0(rng[,1], "-", rng[,2])
knitr::kable(dat)
```

For Ensembl transcriptomes, we support the combined protein coding
(cDNA) and non-coding (ncRNA) sequences, as well as the protein coding
alone (although the former approach combining coding and non-coding
transcripts is recommended for more accurate quantification).

`tximeta` also has functions to support *linked transcriptomes*,
where one or more sources for transcript sequences have been combined
or filtered. See the **Linked transcriptome** section below for a
demonstration. (The *makeLinkedTxome* function was used above to avoid
downloading the GTF during the vignette building process.)

# SummarizedExperiment output

We have our coldata from before. Note that we've removed `files`.

```{r}
suppressPackageStartupMessages(library(SummarizedExperiment))
colData(se)
```

Here we show the three matrices that were imported. 

```{r}
assayNames(se)
```

If there were inferential replicates (Gibbs samples or
bootstrap samples), these would be imported as additional assays named
`"infRep1"`, `"infRep2"`, ...

`tximeta` has imported the correct ranges for the transcripts:

```{r}
rowRanges(se)
```

We have appropriate genome information, which prevents us from making 
bioinformatic mistakes:

```{r}
seqinfo(se)
```

# Retrieve the transcript database

The `se` object has associated metadata that allows `tximeta` to link
to locally stored cached databases and other Bioconductor objects. In
further sections, we will show examples functions that leverage this
databases for adding exon information, summarize transcript-level data
to the gene level, or add identifiers. However, first we mention that
the user can easily access the cached database with the following
helper function. In this case, `tximeta` has an associated *EnsDb*
object that we can retrieve and use in our R session:

```{r}
edb <- retrieveDb(se)
class(edb)
```

The database returned by `retrieveDb` is either a *TxDb* in the case
of GENCODE or RefSeq GTF annotation file, or an *EnsDb* in the case of
an Ensembl GTF annotation file. For further use of these two database
objects, consult the *GenomicFeatures* vignettes and the *ensembldb*
vignettes, respectively (both Bioconductor packages).

# Add exons per transcript

Because the SummarizedExperiment maintains all the metadata of its
creation, it also keeps a pointer to the necessary database for
pulling out additional information, as demonstrated in the following
sections. 

If necessary, the *tximeta* package can pull down the remote source to
build a TxDb, but given that we've already built a TxDb once, it
simply loads the cached version. In order to remove the cached TxDb
and regenerate, one can remove the relevant entry from the `tximeta`
file cache that resides at the location given by `getTximetaBFC()`.

The `se` object created by `tximeta`, has the start, end, and strand
information for each transcript. Here, we swap out the transcript
*GRanges* for exons-by-transcript *GRangesList* (it is a list of
*GRanges*, where each element of the list gives the exons for a
particular transcript).

```{r}
se.exons <- addExons(se)
rowRanges(se.exons)[[1]]
```

As with the transcript ranges, the exon ranges will be generated once
and cached locally. As it takes a non-negligible amount of time to
generate the exon-by-transcript *GRangesList*, this local caching
offers substantial time savings for repeated usage of `addExons` with
the same transcriptome.

We have implemented `addExons` to work only on the transcript-level
*SummarizedExperiment* object. We provide some motivation for this
choice in `?addExons`. Briefly, if it is desired to know the exons
associated with a particular gene, we feel that it makes more sense to
pull out the relevant set of exons-by-transcript for the transcripts
for this gene, rather than losing the hierarchical structure (exons to
transcripts to genes) that would occur with a *GRangesList* of exons
grouped per gene.

# Easy summarization to gene-level

Likewise, the *tximeta* package can make use of the cached TxDb
database for the purpose of summarizing transcript-level
quantifications and bias corrections to the gene-level. After
summarization, the `rowRanges` reflect the start and end position of
the gene, which in Bioconductor are defined by the leftmost and
rightmost genomic coordinates of all the transcripts. As with the
transcript and exons, the gene ranges are cached locally for repeated
usage. The transcript IDs are stored as a *CharacterList* column
`tx_ids`.

```{r}
gse <- summarizeToGene(se)
rowRanges(gse)
```

## Assign ranges by abundance

We also offer a new type of range assignment, based on the most
abundant isoform rather than the leftmost to rightmost coordinate. See
the `assignRanges` argument of `?summarizeToGene`. Using the most
abundant isoform arguably will reflect more accurate genomic distances
than the default option.

```{r eval=FALSE}
# unevaluated code chunk
gse <- summarizeToGene(se, assignRanges="abundant")
```

For more explanation about why this may be a better choice, see the
following tutorial chapter:

<https://tidyomics.github.io/tidy-ranges-tutorial/gene-ranges-in-tximeta.html>

In the below diagram, the pink feature is the set of all exons
belonging to any isoform of the gene, such that the TSS is on the
right side of this minus strand feature. However, the blue feature is
the most abundant isoform (the brown features are the next most
abundant isoforms). The pink feature is therefore not a good
representation for the locus.

```{r echo=FALSE}
knitr::include_graphics("images/assignRanges-abundant.png")
```

# Add different identifiers

We would like to add support to easily map transcript or gene
identifiers from one annotation to another. This is just a prototype
function, but we show how we can easily add alternate IDs given that we
know the organism and the source of the transcriptome. (This function
currently only works for GENCODE and Ensembl gene or transcript IDs
but could be extended to work for arbitrary sources.)

```{r}
library(org.Dm.eg.db)
gse <- addIds(gse, "REFSEQ", gene=TRUE)
mcols(gse)
```

# Differential expression analysis

The following code chunk demonstrates how to build a *DESeqDataSet*
and begin a differential expression analysis. 

```{r}
suppressPackageStartupMessages(library(DESeq2))
# here there is a single sample so we use ~1.
# expect a warning that there is only a single sample...
suppressWarnings({dds <- DESeqDataSet(gse, ~1)})
# ... see DESeq2 vignette
```

The *Swish* method in the *fishpond* package directly works with the
*SummarizedExperiment* output from *tximeta*, and can perform
differential analysis on transcript expression taking into account
inferential replicates, e.g. bootstrap or Gibbs samples, which are
imported and arranged by `tximeta` if these were generated during
quantification.

```{r}
library(fishpond)
y <- se
# y <- scaleInfReps(y)
# y <- labelKeep(y)
# y <- swish(y, x="condition")
# ... see Swish vignette in fishpond package
```

We have a convenient wrapper function that will build a *DGEList*
object for use with *edgeR*.

```{r}
suppressPackageStartupMessages(library(edgeR))
y <- makeDGEList(gse)
# ... see edgeR User's Guide for further steps
```

The following code chunk demonstrates the code inside of the above
wrapper function, and produces the same output.

```{r}
cts <- assays(gse)[["counts"]]
normMat <- assays(gse)[["length"]]
normMat <- normMat / exp(rowMeans(log(normMat)))
o <- log(calcNormFactors(cts/normMat)) + log(colSums(cts/normMat))
y <- DGEList(cts)
y <- scaleOffset(y, t(t(log(normMat)) + o))
# ... see edgeR User's Guide for further steps
```

The following code chunk demonstrates how one could use the *Swish*
method in the fishpond Bioconductor package. Here we use the
transcript-level object `se`. This dataset only has a single sample
and no inferential replicates, but the analysis would begin with such
code. See the Swish vignette in the fishpond package for a complete
example: 

```{r eval=FALSE}
y <- se # rename the object to 'y'
library(fishpond)
# if inferential replicates existed in the data,
# analysis would begin with:
#
# y <- scaleInfReps(y)
# ... see Swish vignette in the fishpond package
```

For *limma* with *voom* transformation we recommend, as in the
*tximport* vignette to generate counts-from-abundance instead of
providing an offset for average transcript length.

```{r}
gse <- summarizeToGene(se, countsFromAbundance="lengthScaledTPM")
library(limma)
y <- DGEList(assays(gse)[["counts"]])
# see limma User's Guide for further steps
```

Above we generated counts-from-abundance when calling
`summarizeToGene`. The counts-from-abundance status is then stored in
the metadata:

```{r}
metadata(gse)$countsFromAbundance 
```

# Additional metadata

The following information is attached to the *SummarizedExperiment* by
`tximeta`: 

```{r}
names(metadata(se))
str(metadata(se)[["quantInfo"]])
str(metadata(se)[["txomeInfo"]])
str(metadata(se)[["tximetaInfo"]])
str(metadata(se)[["txdbInfo"]])
```

# Errors connecting to a database

`tximeta` makes use of *BiocFileCache* to store transcript and other
databases, so saving the relevant databases in a centralized location
used by other Bioconductor packages as well. It is possible that an
error can occur in connecting to these databases, either if the files
were accidentally removed from the file system, or if there was an
error generating or writing the database to the cache location. In
each of these cases, it is easy to remove the entry in the
*BiocFileCache* so that `tximeta` will know to regenerate the
transcript database or any other missing database.

If you have used the default cache location, then you can obtain
access to your BiocFileCache with:

```{r}
library(BiocFileCache)
bfc <- BiocFileCache()
```

Otherwise, you can recall your particular `tximeta` cache location
with `getTximetaBFC()`.

You can then inspect the entries in your BiocFileCache using `bfcinfo`
and remove the entry associated with the missing database with `bfcremove`. 
See the BiocFileCache vignette for more details on finding and
removing entries from a BiocFileCache.

Note that there may be many entries in the BiocFileCache location,
including `.sqlite` database files and serialized `.rds` files. You
should only remove the entry associated with the missing database,
e.g. if R gave an error when trying to connect to the TxDb associated
with GENCODE v99 human transcripts, you should look for the `rid` of
the entry associated with the human v99 GTF from GENCODE.

# What if checksum isn't known?

`tximeta` automatically imports relevant metadata when the
transcriptome matches a known source -- *known* in the sense that it
is in the set of pre-computed hashed checksums in `tximeta` (GENCODE,
Ensembl, and RefSeq for human and mouse). `tximeta` also facilitates the
linking of transcriptomes used in building the *Salmon* index with
relevant public sources, in the case that these are not part of this
pre-computed set known to `tximeta`.
The linking of the transcriptome source with the quantification files is
important in the case that the transcript sequence no longer matches a
known source (uniquely combined or filtered FASTA files), or if the
source is not known to `tximeta`. Combinations of coding and
non-coding human, mouse, and fruit fly *Ensembl* transcripts should be
automatically recognized by `tximeta` and does not require making a
*linkedTxome*. As the package is further developed, we plan to roll
out support for all common transcriptomes, from all sources.

**Note:** if you are using Salmon in alignment mode, then there is no
Salmon index, and without the Salmon index, there is no checksum. We
don't have a perfect solution for this yet, but you can still
summarize transcript counts to gene with a `tx2gene` table that you
construct manually (see `tximport` vignette for example code). 
Just specify the arguments,
`skipMeta=TRUE, txOut=FALSE, tx2gene=tx2gene`, 
when calling `tximeta` and it will perform summarization to gene level
as in `tximport`.

We now demonstrate how to make a *linkedTxome* and how to share and
load a *linkedTxome*.
We point to a *Salmon* quantification file which was quantified
against a transcriptome that included the coding and non-coding
*Drosophila melanogaster* transcripts, as well as an artificial
transcript of 960 bp (for demonstration purposes only).

```{r}
file <- file.path(dir, "SRR1197474.plus", "quant.sf")
file.exists(file)
coldata <- data.frame(files=file, names="SRR1197474", sample="1",
                      stringsAsFactors=FALSE)
```

Trying to import the files gives a message that `tximeta` couldn't find
a matching transcriptome, so it returns an non-ranged
*SummarizedExperiment*. 

```{r}
se <- tximeta(coldata)
```

# Linked transcriptomes

If the transcriptome used to generate the *Salmon* index does not
match any transcriptomes from known sources (e.g. from combining or
filtering known transcriptome files), there is not much that can be
done to automatically populate the metadata during quantification
import. However, we can facilitate the following two cases:

1) the transcriptome was created locally and has been linked to its
public source(s) 
2) the transcriptome was produced by another group, and
they have produced and shared a file that links the transcriptome to
public source(s)

`tximeta` offers functionality to assist reproducible analysis in both
of these cases.

To make this quantification reproducible, we make a `linkedTxome`
which records key information about the sources of the transcript
FASTA files, and the location of the relevant GTF file. It also
records the checksum of the transcriptome that was computed by
*Salmon* during the `index` step.

**Source:** when creating the `linkedTxome` one must specify the 
`source` of the transcriptome. See `?linkedTxome` for a note on 
the implications of this text string. For canonical GENCODE or
Ensembl transcriptomes, one can use `"GENCODE"` or `"Ensembl"`,
but for modified or otherwise any transcriptomes defined by a local
database, it is recommended to use a different string,
`"LocalGENCODE"` or `"LocalEnsembl", which will avoid *tximeta*
pulling canonical GENCODE or Ensembl resources from AnnotationHub.

**Multiple GTF/GFF files:** `linkedTxome` and `tximeta` do not
currently support multiple GTF/GFF files, which is a more complicated
case than multiple FASTA, which is supported. Currently, we recommend
that users should add or combine GTF/GFF files themselves to create
a single GTF/GFF file that contains all features used in
quantification, and then upload such a file to *Zenodo*, which can
then be linked as shown below. Feel free to contact the developers on
the Bioconductor support site or GitHub Issue page for further details
or feature requests.

**Stringtie:** A special note for building on top of
Stringtie-generated transcripts: it is a good idea to change gene
identifiers, to _not_ include a period `.`, as the period will later
be used to separate transcript versions from gene identifiers. This
can be done before building the Salmon index, by changing periods in
the gene identifier to an underscore. See 
[this GitHub issue](https://github.com/thelovelab/tximeta/issues/68) for details.



By default, `linkedTxome` will write out a JSON file which can be
shared with others, linking the checksum of the index with the other
metadata, including FASTA and GTF sources. By default, it will write
out to a file with the same name as the `indexDir`, but with a `.json`
extension added. This can be prevented with `write=FALSE`, and the
file location can be changed with `jsonFile`.

First we specify the path where the *Salmon* index is located. 

Typically you would not use `system.file` and `file.path` to locate
this directory, but simply define `indexDir` to be the path of the
*Salmon* directory on your machine. Here we use `system.file` and
`file.path` because we have included parts of a *Salmon* index
directory in the *tximeta* package itself for demonstration of
functionality in this vignette.

```{r}
indexDir <- file.path(dir, "Dm.BDGP6.22.98.plus_salmon-0.14.1")
```

Now we provide the location of the FASTA files and the GTF file for
this transcriptome. 

**Note:** the basename for the GTF file is used as a unique identifier
for the cached versions of the *TxDb* and the transcript ranges, which
are stored on the user's behalf via *BiocFileCache*. This is not an
issue, as GENCODE, Ensembl, and RefSeq all provide GTF files which are
uniquely identified by their filename,
e.g. `Drosophila_melanogaster.BDGP6.22.98.gtf.gz`.

The recommended usage of `tximeta` would be to specify a remote GTF
source, as seen in the commented-out line below: 

```{r}
fastaFTP <- c("ftp://ftp.ensembl.org/pub/release-98/fasta/drosophila_melanogaster/cdna/Drosophila_melanogaster.BDGP6.22.cdna.all.fa.gz",
              "ftp://ftp.ensembl.org/pub/release-98/fasta/drosophila_melanogaster/ncrna/Drosophila_melanogaster.BDGP6.22.ncrna.fa.gz",
              "extra_transcript.fa.gz")
#gtfFTP <- "ftp://path/to/custom/Drosophila_melanogaster.BDGP6.22.98.plus.gtf.gz"
```

Instead of the above commented-out FTP location for the GTF file, we
specify a location within an R package. This step is just to avoid
downloading from a remote FTP during vignette building. This use of
`file.path` to point to a file in an R package is specific to this
vignette and should not be used in a typical workflow. The following
GTF file is a modified version of the release 98 from Ensembl, which
includes description of a one transcript, one exon artificial gene
which was inserted into the transcriptome (for demonstration purposes
only). 

```{r}
gtfPath <- file.path(dir,"Drosophila_melanogaster.BDGP6.22.98.plus.gtf.gz")
```

Finally, we create a *linkedTxome*.  In this vignette, we point to a
temporary directory for the JSON file, but a more typical workflow
would write the JSON file to the same location as the *Salmon* index
by not specifying `jsonFile`.

`makeLinkedTxome` performs two operation: (1) it creates a new entry in
an internal table that links the transcriptome used in the *Salmon*
index to its sources, and (2) it creates a JSON file such that this
*linkedTxome* can be shared.

```{r}
tmp <- tempdir()
jsonFile <- file.path(tmp, paste0(basename(indexDir), ".json"))
makeLinkedTxome(indexDir=indexDir,
                source="LocalEnsembl", organism="Drosophila melanogaster",
                release="98", genome="BDGP6.22",
                fasta=fastaFTP, gtf=gtfPath,
                jsonFile=jsonFile)
```

After running `makeLinkedTxome`, the connection between this *Salmon*
index (and its checksum) with the sources is saved for persistent
usage. Note that because we added a single transcript of 960bp to the
FASTA file used for quantification, `tximeta` could tell that this was
not quantified against release 98 of the Ensembl transcripts for
*Drosophila melanogaster*. Only when the correct set of transcripts
were specified does `tximeta` recognize and import the correct
metadata.

With use of `tximeta` and a *linkedTxome*, the software
figures out if the remote GTF has been accessed and compiled into a
*TxDb* before, and on future calls, it will simply load the
pre-computed metadata and transcript ranges.

Note the warning that 5 of the transcripts are missing from the GTF
file and so are dropped from the final output. This is a problem
coming from the annotation source, and not easily avoided by
`tximeta`. 

```{r}
se <- tximeta(coldata)
```

We can see that the appropriate metadata and transcript ranges are
attached.

```{r}
rowRanges(se)
seqinfo(se)
```

# Clear *linkedTxomes*

The following code removes the entire table with information about the
*linkedTxomes*. This is just for demonstration, so that we can show
how to load a JSON file below.

**Note:** Running this code will clear any information about
*linkedTxomes*. Don't run this unless you really want to clear this
table!

```{r}
library(BiocFileCache)
if (interactive()) {
  bfcloc <- getTximetaBFC()
} else {
  bfcloc <- tempdir()
}
bfc <- BiocFileCache(bfcloc)
bfcinfo(bfc)
bfcremove(bfc, bfcquery(bfc, "linkedTxomeTbl")$rid)
bfcinfo(bfc)
```

# Loading *linkedTxome* JSON files

If a collaborator or the Suppmentary Files for a publication shares a
`linkedTxome` JSON file, we can likewise use `tximeta` to
automatically assemble the relevant metadata and transcript
ranges. This implies that the other person has used `tximeta` with the
function `makeLinkedTxome` demonstrated above, pointing to their
*Salmon* index and to the FASTA and GTF source(s).

We point to the JSON file and use `loadLinkedTxome` and then the
relevant metadata is saved for persistent usage. In this case, we
saved the JSON file in a temporary directory.

```{r}
jsonFile <- file.path(tmp, paste0(basename(indexDir), ".json"))
loadLinkedTxome(jsonFile)
```

Again, using `tximeta` figures out whether it needs to access the
remote GTF or not, and assembles the appropriate object on the user's
behalf.

```{r}
se <- tximeta(coldata)
```

# Clear *linkedTxomes* again

Finally, we clear the *linkedTxomes* table again so that the above
examples will work. This is just for the vignette code and not part of
a typical workflow.

**Note:** Running this code will clear any information about
*linkedTxomes*. Don't run this unless you really want to clear this
table!

```{r}
if (interactive()) {
  bfcloc <- getTximetaBFC()
} else {
  bfcloc <- tempdir()
}
bfc <- BiocFileCache(bfcloc)
bfcinfo(bfc)
bfcremove(bfc, bfcquery(bfc, "linkedTxomeTbl")$rid)
bfcinfo(bfc)
```

# Other quantifiers

`tximeta` can import the output from any quantifiers that are
supported by `tximport`, and if these are not *Salmon*, *alevin*, or
*Sailfish* output, it will simply return a non-ranged
*SummarizedExperiment* by default.

An alternative solution is to wrap other quantifiers in workflows
that include metadata information JSON files along with each
quantification file. One can place these files in
`aux_info/meta_info.json` or any relative location specified by
`customMetaInfo`, for example `customMetaInfo="meta_info.json"`. This
JSON file is located relative to the quantification file and should
contain a tag `index_seq_hash` with an associated value of the SHA-256
hash of the reference transcripts. For computing the hash value of the
reference transcripts, see the
[FastaDigest](https://github.com/COMBINE-lab/FastaDigest) python
package. The hash value used by *Salmon* is the SHA-256 hash value
of the reference sequences stripped of the header lines, and
concatenated together with the empty string (so only cDNA sequences
combined without any new line characters). *FastaDigest* can be
installed with `pip install fasta_digest`.

# Automated analysis with ARMOR

This vignette described the use of `tximeta` to import quantification
data into R/Bioconductor with automatic detection and addition of
metadata. The *SummarizedExperiment* produced by `tximeta` can then be
provided to downstream statistical analysis packages as described
above. The *tximeta* package does not contain any functionality for
automated differential analysis. 

The [ARMOR](https://github.com/csoneson/ARMOR) workflow does automate 
a variety of differential analyses, and make use of `tximeta` for
creation of a *SummarizedExperiment* with attached annotation
metadata. ARMOR stands for 
``An Automated Reproducible MOdular Workflow for Preprocessing
and Differential Analysis of RNA-seq Data'' and is described in more
detail in the article by @Orjuelag2019.

# Default BiocFileCahce Caching Location Update

*tximeta* makes use of the default BiocFileCache location, unless
otherwise specified by the user. As of BiocFileCache version > 1.15.1,
the default caching location used by BiocFileCache has changed. In
order to continue to use the same cache (without re-downloading
files), please follow the steps in the BiocFileCache vignette, under
the heading **Default Caching Location Update**.

# Acknowledgments

The development of *tximeta* has benefited from suggestions from these
and other individuals in the community:

* Vincent Carey
* Lori Shepherd
* Martin Morgan
* Koen Van den Berge
* Johannes Rainer
* James Ashmore
* Ben Johnson
* Tim Triche
* Kristoffer Vitting-Seerup

# Session info

```{r}
library(devtools)
session_info()
```

# References
