# Loading user-provided data

#### Nuno Saraiva-Agostinho

#### 2021-10-26

psichomics is an interactive R package for integrative analyses of alternative splicing and gene expression based on data from multiple sources, including user-provided data.

# Supported file formats

psichomics supports the following data sources. Each source has a link with instructions for data loading.

Source | Sample information | Subject information | Gene expression | Exon-exon junction quantification | Alternative splicing quantification
--- | --- | --- | --- | --- | ---
SRA Run Selector | Yes |  |  |  |
STAR |  |  | Yes | Yes |
VAST-TOOLS |  |  | Yes |  | Yes
TCGA (via FireBrowse) | Yes | Yes | Yes | Yes |
SRA (via recount) | Yes | Yes | Yes | Yes |
GTEx | Yes | Yes | Yes | Yes |
Other sources | Yes | Yes | Yes | Yes | Limited*

* psichomics cannot fully parse alternative splicing events (e.g. it may not identify the cognate gene and coordinates) based on tables from these sources.

## Prepare SRA Run Selector data

The SRA Run Selector contains sample metadata that can be downloaded for all or selected samples from a SRA project. To download sample information, click the Metadata button in the Download columns. The output file is usually named `SraRunTable.txt`.

To proceed loading the data, move the downloaded file to a new folder and follow the instructions in Load user-provided data into psichomics.

## Prepare tables based on RNA-seq data using STAR

The following section goes through the steps required to load data based on RNA-seq data:

1. Retrieve FASTQ files and sample-associated information (optional if you already have the FASTQ files);
2. Map RNA-seq reads from the FASTQ files against a genome of reference using a splice-aware aligner, such as STAR;
3. Merge and prepare its output to be correctly interpreted by psichomics;
4. Load data into psichomics.

### Download FASTQ files (optional)

SRA is a repository of biological sequences that stores data from many published articles with the potential to answer pressing biological questions.

> The latest versions of psichomics support automatic downloading of SRA data from recount, a resource of pre-processed data for thousands of SRA projects (including gene read counts, splice junction quantification and sample metadata). First, check if the project of your interest is available in recount, thus making it quicker to analyse gene expression and alternative splicing for your samples of interest.

Data from SRA can be downloaded using the fasterq-dump command from sra-tools. For instance, to retrieve samples from the SRP126561 project:

    # List SRA samples
    samples=(SRR6368612 SRR6368613 SRR6368614 SRR6368615 SRR6368616 SRR6368617)

    # Download samples
    fasterq-dump --split-3 ${samples}

> `--split-3` allows to output one or two FASTQ files for single-end or paired-end sequencing, respectively (a third FASTQ file may also be returned containing orphaned single-end reads obtained from paired-end sequencing data)

Sample-associated data is also available from the Run Selector page. Click RunInfo Table to download the whole metadata table for all samples (usually downloaded in a file named `SraRunTable.txt`).

### Align RNA-seq data to quantify splice junctions

The quantification of each alternative splicing event is based on the proportion of junction reads that support the inclusion isoform, known as percent spliced-in or PSI (Wang et al., 2008).

To estimate this value for each splicing event, both alternative splicing annotation and quantification of RNA-Seq reads aligning to splice junctions (junction quantification) are required. While alternative splicing annotation is provided by the package, junction quantification will need to be prepared from user-provided data by aligning the RNA-seq reads from FASTQ files to a genome of reference. As junction reads are required to quantify alternative splicing, a splice-aware aligner will be used.

psichomics currently supports STAR output.

#### Index the genome using STAR

Before aligning FASTQ samples against a genome of reference, an index needs to be prepared.

Start by downloading a FASTA file of the whole genome and a GTF file with annotated transcripts. This command makes use of these human FASTA and GTF files (hg19 assembly).

    mkdir hg19_STAR
    STAR --runMode genomeGenerate \
         --genomeDir hg19_STAR \
         --genomeFastaFiles /path/to/hg19.fa \
         --sjdbGTFfile /path/to/hg19.gtf \
         --runThreadN 4

    # Arguments:
    #     --runMode             Generate the genome index
    #     --genomeDir           Path to genome index (output)
    #     --genomeFastaFiles    Path to genome FASTA file(s)
    #     --sjdbGTFfile         Path to junction GTF annotation
    #     --runThreadN 4        Run in parallel using 4 threads

#### Align against genome index using STAR

After the genome index is generated, the sequences in the FASTQ files need to be aligned against the annotated gene and splice junctions from the previously prepared reference. The following commands make STAR output both gene and junction read counts into files ending in `ReadsPerGene.out.tab` and `SJ.out.tab`, respectively.

    align () {
        echo "Aligning ${1} using STAR..."
        STAR --readFilesIn ${1}_1.fastq ${1}_2.fastq \
             --runThreadN 16 \
             --genomeDir hg19_STAR \
             --readFilesCommand zcat \
             --quantMode GeneCounts \
             --outFileNamePrefix ${1}
    }
    # Arguments:
    #     --readFilesIn              FASTQ files to align
    #     --runThreadN 16            Run in parallel using 16 threads
    #     --genomeDir                Path to genome index (input)
    #     --readFilesCommand zcat    Use zcat to extract compressed files
    #     --quantMode                Return gene read counts
    #     --outFileNamePrefix        Prefix for output files

    for each in ${samples}; do
        align "${each}"
    done

### Prepare output for psichomics

To process the resulting data files, type in R:

    # Change working directory to where the STAR output is
    setwd("/path/to/aligned/output/")

    library(psichomics)
    prepareGeneQuant(
        "SRR6368612ReadsPerGene.out.tab", "SRR6368613ReadsPerGene.out.tab",
        "SRR6368614ReadsPerGene.out.tab", "SRR6368615ReadsPerGene.out.tab",
        "SRR6368616ReadsPerGene.out.tab", "SRR6368617ReadsPerGene.out.tab")
    prepareJunctionQuant("SRR6368612SJ.out.tab", "SRR6368613SJ.out.tab",
                         "SRR6368614SJ.out.tab", "SRR6368615SJ.out.tab",
                         "SRR6368616SJ.out.tab", "SRR6368617SJ.out.tab")

To load the data, move the files (including the SRA metadata) to a new folder and follow the instructions in Load user-provided data into psichomics.

## Prepare VAST-TOOLS data

psichomics supports loading inclusion levels and gene expression tables from VAST-TOOLS (the tables available after running `vast-tools combine`). Note:

- If you are interested in analysing gene expression, please run `vast-tools align` with argument `--expr`;
- Gene expression is returned in cRPKMs; to get both cRPKMs and gene read counts, please run `vast-tools combine` with argument `-C` (in case of doubt, always calculate both cRPKMs and gene read counts).

Any sample and/or subject information may also be useful to load. Unless the sample metadata comes from SRA Run Selector, please ensure that the table is recognised by psichomics: read Prepare generic data.

To load the data and move all files to a new folder (VAST-TOOLS alternative splicing quantification and gene expression tables and sample/subject-associated information).

Follow the instructions in Load user-provided data into psichomics to load the files in the visual interface. Otherwise, use function `loadLocalFiles()` with the folder path as an argument:

    library(psichomics)
    data <- loadLocalFiles("/path/to/psichomics/input")
    names(data)
    names(data[[1]])

    junctionQuant  <- data[[1]]$`Junction quantification`
    sampleInfo     <- data[[1]]$`Sample metadata`
    # Both gene read counts and cRPKMs are loaded as separate data frames
    geneReadCounts <- data[[1]]$`Gene expression (read counts)`
    cRPKM          <- data[[1]]$`Gene expression (cRPKM)`

## Prepare FireBrowse data

FireBrowse contains TCGA data for multiple tumour types and can be automatically downloaded and then loaded using psichomics.

Alternatively, manually downloaded files from FireBrowse can be moved to a folder and then loaded in psichomics by following the instructions in Load user-provided data into psichomics.

## Prepare GTEx data

GTEx contains data for multiple normal tissues. GTEx data can be automatically downloaded and then loaded using psichomics.

Alternatively, manually downloaded files from GTEx can be moved to a folder and then loaded in psichomics by following the instructions in Load user-provided data into psichomics.

## Prepare data from any source

psichomics supports importing generic data from any source as long as the tables are prepared as detailed below.

> Please make sure that sample and subject identifiers are exactly the same between all datasets.

### Sample information

> If you are working with sample metadata from SRA Run Selector, see how to prepare SRA Run Selector data.

- Tab-separated values (TSV)
- Sample identifiers (rows) and their attributes (columns)
- The first column must contain sample identifiers and be named `Sample ID`
- Optionally, indicate the subject associated to each sample in a column named `Subject ID` (subject identifiers must be the same as the ones used in subject information)
- Example of a valid sample information dataset:

Sample ID | Type | Tissue | Subject ID
--- | --- | --- | ---
SMP-01 | Tumour | Lung | SUBJ-03
SMP-02 | Normal | Blood | SUBJ-12
SMP-03 | Normal | Blood | SUBJ-25

### Subject information

- Tab-separated values (TSV)
- Subject identifiers (rows) and their attributes (columns)
- The first column must contain subject identifiers and be named `Subject ID`
- Example of a valid subject information dataset:

Subject ID | Age | Gender | Race
--- | --- | --- | ---
SUBJ-01 | 34 | Female | Black
SUBJ-02 | 22 | Male | Black
SUBJ-03 | 58 | Female | Asian

### Gene expression

- Tab-separated values (TSV)
- Read counts of genes (rows) across sample (columns) (sample identifiers must be the same as the ones used in sample information)
- The first column must contain unique gene names (symbols, Ensembl ID, etc.) and be named `Gene ID`
- Example of a valid gene expression dataset:

Gene ID | SMP-18 | SMP-03 | SMP-54
--- | --- | --- | ---
AMP1 | 24 | 10 | 43
BRCA1 | 38 | 46 | 32
BRCA2 | 43 | 65 | 21

### Exon-exon junction quantification

- Tab-separated values (TSV)
- Read counts of exon-exon junctions (rows) across samples (columns) (sample identifiers must be the same as the ones used in sample information)
- The first column must contain junction identifiers and be named `Junction ID`
- Only chromosome number and capital letters X, Y, Z, W, and M are supported, followed by the genomic regions; acceptable junction identifiers include:
  - `10_18748_21822`
  - `chromosome 10 (18748 to 21822)`
  - `chr10:18748-21822`
- Optionally, indicate the strand with `+` or `-` at the end of the junction identifier:
  - `10:3213:9402:+`
  - `chr10:3213-9402 -`
- Junction identifiers whose chromosomes are `alt`, `random` or `Un` (i.e. alternative sequences) are discarded
- Example of a valid exon-exon junction quantification dataset:

Junction ID | SMP-18 | SMP-03
--- | --- | ---
10:6752-7393 | 4 | 0
10:18748-21822 | 8 | 46
10:24257-25325 | 83 | 65

### Alternative splicing quantification (also known as inclusion levels)

> Note that psichomics cannot currently parse alternative splicing events (e.g. identify the cognate gene and coordinates) from generic, user-provided tables.

- Tab-separated values (TSV)
- Quantification values of alternative splicing events (rows) across samples (columns) (sample identifiers must be the same as the ones used in sample information)
- The first column must be named `AS Event ID`
- Values between 0 and 1 or between 0 and 100: if the latter, values are automatically scaled between 0 and 1
- Example of a valid alternative splicing quantification dataset:

AS Event ID | SMP-18 | SMP-03
--- | --- | ---
someASevent001 | 0.71 | 0.30
anotherASevent653 | 0.63 | 0.37
yetAnother097 | 0.38 | 0.62

To load the data, move the files to a new folder and follow the instructions in Load user-provided data into psichomics.

# Load user-provided data into psichomics

## Load using the visual interface

Start psichomics with the following commands in an R console or RStudio:

    library(psichomics)
    psichomics()

Then, click Load user files. Click the Folder input tab and select the appropriate folder. Finally, click Load files to automatically scan and load all supported files from that folder.

## Load using the command-line interface (CLI)

Use function `loadLocalFiles()` with the folder path as an argument:

    library(psichomics)
    data <- loadLocalFiles("/path/to/psichomics/input")
    names(data)
    names(data[[1]])

    geneExpr      <- data[[1]]$`Gene expression`
    junctionQuant <- data[[1]]$`Junction quantification`
    sampleInfo    <- data[[1]]$`Sample metadata`

# Feedback

All feedback on the program, documentation and associated material (including this tutorial) is welcome. Please send any comments and questions to:

> Nuno Saraiva-Agostinho (nunoagostinho@medicina.ulisboa.pt)

> Disease Transcriptomics Lab, Instituto de Medicina Molecular (Portugal)

# References

Wang,E.T. et al. (2008) Alternative isoform regulation in human tissue transcriptomes. Nature, 456, 470–476.