Package 'brainflowprobes' reference manual

Title:	Plots and annotation for choosing BrainFlow target probe sequence
Description:	Use these functions to characterize genomic regions for BrainFlow target probe design.
Authors:	Amanda Price [aut, cre] , Leonardo Collado-Torres [ctb]
Maintainer:	Amanda Price <[email protected]>
License:	Artistic-2.0
Version:	1.19.0
Built:	2024-07-16 05:33:05 UTC
Source:	https://github.com/bioc/brainflowprobes

Extract coverage data for a set of regions

Description

This function extracts the data from the BigWig coverage files that is then used by four_panels. This function can take a while to run depending on your internet connection. Furthermore, this function relies on functionality in the rtracklayer package for reading BigWig files which does not work in Windows machines. The data extracted by this function is also used by plot_coverage.

Usage

brainflowprobes_cov(REGION, PD = brainflowprobes::pd, VERBOSE = TRUE)
brainflowprobes_cov(REGION, PD = brainflowprobes::pd, VERBOSE = TRUE)

Arguments

`REGION`	Either a single hg19 genomic sequence including the chromosome, start, end, and optionally strand separated by colons (e.g., 'chr20:10199446-10288068:+'), or a string of sequences. Must be character. Chromosome must be proceeded by 'chr'.
`PD`	A list of data.frames with the `sumMapped` and `files` columns. Defaults to the data included in this package.
`VERBOSE`	A logical value indicating whether to print updates from the process of loading the data from the BigWig files.

Value

A list of region coverage coverage data.frame lists used by four_panels and plot_coverage. That is, a list with one element per dataset in pd (so four: Sep, Deg, Cell, Sort). Each element of the output list is a list with one data.frame per input region. In the case of four_panels_example_cov there was only one input region hence each region coverage data.frame list has one element. A region coverage data.frame has one column per sample and one row per genome base-pair for the given region and dataset.

Author(s)

Leonardo Collado-Torres

Examples


## This function loads data from BigWig files using the rtracklayer package.
## This functionality is not supported on Windows OS machines!
if (.Platform$OS.type != "windows") {
    ## How long this takes to run will depend on your internet connection.
    example_cov <- brainflowprobes_cov("chr20:10286777-10288069:+",
        PD = lapply(brainflowprobes::pd, head, n = 2)
    )

    ## Output examination:
    # A list with one element per element in brainflowprobes::pd
    stopifnot(is.list(example_cov))
    stopifnot(identical(
        names(example_cov),
        names(brainflowprobes::pd)
    ))

    # For each dataset, brainflowprobes_cov() returns a list of region
    # coverage data.frames. In this example, there was a single input region.
    stopifnot(all(
        sapply(example_cov, length) ==
            length(
                GenomicRanges::GRanges("chr20:10286777-10288069:+")
            )
    ))

    # Then each data.frame itself has 1 row per genome base-pair in the region
    stopifnot(
        all(
            sapply(example_cov, function(x) {
                nrow(x[[1]])
            }) ==
                GenomicRanges::width(
                    GenomicRanges::GRanges("chr20:10286777-10288069:+")
                )
        )
    )

    # and one column per sample in the dataset unless you subsetted the data
    # like we did earlier when creating "example_cov".
    stopifnot(identical(
        sapply(four_panels_example_cov, function(x) {
            ncol(x[[1]])
        }),
        sapply(pd, nrow)
    ))
}

## This is how the example data included in the package was made:
## Not run: 
## This can take about 10 minutes to run!
four_panels_example_cov <- brainflowprobes_cov("chr20:10286777-10288069:+")

## End(Not run)


## If you are interested, you could download all the BigWig files
## in the \code{brainflowprobes::pd} list of data.frames from the
## \code{files} column to your disk. Doing so will greatly increase the
## speed for \code{brainflowprobes_cov} and the functions that depend on
## this data. Then edit \code{brainflowprobes::pd} \code{files} to point to
## your local files.

## Web location of BigWig files
lapply(brainflowprobes::pd, function(x) head(x$files))
## This function loads data from BigWig files using the rtracklayer package.
## This functionality is not supported on Windows OS machines!
if (.Platform$OS.type != "windows") {
    ## How long this takes to run will depend on your internet connection.
    example_cov <- brainflowprobes_cov("chr20:10286777-10288069:+",
        PD = lapply(brainflowprobes::pd, head, n = 2)
    )

    ## Output examination:
    # A list with one element per element in brainflowprobes::pd
    stopifnot(is.list(example_cov))
    stopifnot(identical(
        names(example_cov),
        names(brainflowprobes::pd)
    ))

    # For each dataset, brainflowprobes_cov() returns a list of region
    # coverage data.frames. In this example, there was a single input region.
    stopifnot(all(
        sapply(example_cov, length) ==
            length(
                GenomicRanges::GRanges("chr20:10286777-10288069:+")
            )
    ))

    # Then each data.frame itself has 1 row per genome base-pair in the region
    stopifnot(
        all(
            sapply(example_cov, function(x) {
                nrow(x[[1]])
            }) ==
                GenomicRanges::width(
                    GenomicRanges::GRanges("chr20:10286777-10288069:+")
                )
        )
    )

    # and one column per sample in the dataset unless you subsetted the data
    # like we did earlier when creating "example_cov".
    stopifnot(identical(
        sapply(four_panels_example_cov, function(x) {
            ncol(x[[1]])
        }),
        sapply(pd, nrow)
    ))
}

## This is how the example data included in the package was made:
## Not run: 
## This can take about 10 minutes to run!
four_panels_example_cov <- brainflowprobes_cov("chr20:10286777-10288069:+")

## End(Not run)


## If you are interested, you could download all the BigWig files
## in the \code{brainflowprobes::pd} list of data.frames from the
## \code{files} column to your disk. Doing so will greatly increase the
## speed for \code{brainflowprobes_cov} and the functions that depend on
## this data. Then edit \code{brainflowprobes::pd} \code{files} to point to
## your local files.

## Web location of BigWig files
lapply(brainflowprobes::pd, function(x) head(x$files))

Check the PDF file

Description

This function checks that the PDF file does not exist to avoid overwriting a plot the user has previously made.

Usage

check_pdf(PDF = "four_panels.pdf", OUTDIR = tempdir())
check_pdf(PDF = "four_panels.pdf", OUTDIR = tempdir())

Arguments

`PDF`	The name of the PDF file. Defaults to `four_panels.pdf`.
`OUTDIR`	The default directory where `PDF` will be saved to.

Details

This is an utility function used by four_panels and plot_coverage.

Value

The path to the PDF file if the file doesn't exist. It appends the pdf file extension if it was absent from PDF.

Author(s)

Leonardo Collado-Torres

Examples


## Choose a random PDF file
PDF <- paste0("test_", stats::runif(1, max = 1e10))

## Initially this works because the output PDF does not exist.
PDF <- check_pdf(PDF)
## It also adds the PDF extension if the user didn't supply it.
PDF

## Create a dummy PDF file
pdf(file = PDF)
plot(1, 1)
dev.off()

## Now it doesn't work since the PDF file already exists.
testthat::expect_error(
    check_pdf(basename(PDF)),
    "already exists! Rename or erase it"
)
## Choose a random PDF file
PDF <- paste0("test_", stats::runif(1, max = 1e10))

## Initially this works because the output PDF does not exist.
PDF <- check_pdf(PDF)
## It also adds the PDF extension if the user didn't supply it.
PDF

## Create a dummy PDF file
pdf(file = PDF)
plot(1, 1)
dev.off()

## Now it doesn't work since the PDF file already exists.
testthat::expect_error(
    check_pdf(basename(PDF)),
    "already exists! Rename or erase it"
)

Plot expression coverage in four datasets for candidate probe sequences.

Description

four_panels creates four plots for each candidate probe sequence. The first plot (Separation) shows the adjusted read coverage in cytosolic and nuclear RNA from human postmortem cortex. The second plot (Degradation) shows coverage in human cortical samples exposed to room temperature for 0-60 minutes. The third plot (Sorted) shows RNA coverage in nuclei that had been sorted based on reactivity to NeuN-antibody, a neuronal marker. NeuN+ samples are enriched for neurons, and NeuN- samples are enriched for non-neurons. The fourth plot (Single Cells) shows the expression coverage in single cells isolated from human temporal lobe.

Usage

four_panels(
  REGION,
  PDF = "four_panels.pdf",
  OUTDIR = tempdir(),
  JUNCTIONS = FALSE,
  COVERAGE = NULL,
  CODING_ONLY = FALSE,
  VERBOSE = TRUE
)
four_panels(
  REGION,
  PDF = "four_panels.pdf",
  OUTDIR = tempdir(),
  JUNCTIONS = FALSE,
  COVERAGE = NULL,
  CODING_ONLY = FALSE,
  VERBOSE = TRUE
)

Arguments

`REGION`	Either a single hg19 genomic sequence including the chromosome, start, end, and optionally strand separated by colons (e.g., 'chr20:10199446-10288068:+'), or a string of sequences. Must be character. Chromosome must be proceeded by 'chr'.
`PDF`	The name of the PDF file. Defaults to `four_panels.pdf`.
`OUTDIR`	The default directory where `PDF` will be saved to.
`JUNCTIONS`	A logical value indicating if the candidate probe sequence spans splice junctions (Default=FALSE).
`COVERAGE`	The output of brainflowprobes_cov for the input `REGION`. Defaults to `NULL` but it can be pre-computed and saved separately since this is the step that takes the longest to run. Also, this is the only step that depends on rtracklayer's functionality for reading BigWig files which does not run on Windows OS. So it could be run on a non-Windows machine, saved, and then shared with Windows users.
`CODING_ONLY`	A logical vector of length 1 specifying whether to subset the Annotated Genes to only the coding genes. That is, whether to subset the genes by whether they have a non-NA `CSS` value. The Annotated Genes are downloaded with `GenomicState::GenomicStateHub()`.
`VERBOSE`	A logical value indicating whether to print updates from the process of loading the data from the BigWig files.

Value

four_panels() first annotates the input candidate probe sequence(s) in REGION using bumphunter::matchGenes(), and then cuts the expression coverage for each sequence from each sample in four different datasets (see the BrainFlow publication for references) using derfinder::getRegionCoverage(). The coverage is normalized to the total mapped reads per sample and kilobase width of each probe region before log2 transformation. The four plots are labeled by the dataset and the plots are topped by the sequence coordinates, sequence width, and the name of the nearest gene.

A good candidate probe sequence will have several characteristics. In the Separation data, the sequence should be relatively highly expressed in nuclear RNA, at least in your age of interest. The sequence should also show stable expression over the 60 minutes of room temperature exposure in the Degradation data. The sequence should also be expressed in the appropriate NeuN fraction (depending on cell type specificity) in the Sorted dataset, and also be expressed in the right cell type in the Single Cell dataset.

four_panels() saves the results as four_panels.pdf in a temporary directory unless otherwise specified with OUTDIR.

⁠if(JUNCTIONS)⁠, this means that the candidate probe sequence spans splice junctions. In this case, the character vector of regions should represent the coordinates of each exon spanned in the sequence. ⁠if(JUNCTIONS)⁠, four_panels() will sum the coverage of each exon and plot that value for each dataset instead of creating an independent set of plots for each exon. This is a way to avoid deflating coverage by including lowly-expressed intron coverage in the plots.

Author(s)

Amanda J Price

Examples


## Here we use the pre-saved example coverage data such that this example
## will run fast!
four_panels("chr20:10286777-10288069:+",
    COVERAGE = four_panels_example_cov
)
## Not run: 
## Without using COVERAGE, this function reads BigWig files from the web
## using rtracklayer and this functionality is not supported on Windows
## machines.
if (.Platform$OS.type != "windows") {
    ## This example takes 10 minutes to run!
    four_panels("chr20:10286777-10288069:+")
}

## These examples will take several minutes to run depending on your
## internet connection
four_panels(c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
))

PENK_exons <- c(
    "chr8:57353587-57354496:-",
    "chr8:57358375-57358515:-",
    "chr8:57358985-57359040:-",
    "chr8:57359128-57359292:-"
)

## General syntax
four_panels(PENK_exons,
    JUNCTIONS = TRUE,
    PDF = "PDF_file.pdf", OUTDIR = "/path/to/directory/"
)

four_panels("chr20:10286777-10288069:+",
    PDF = "PDF_file.pdf", OUTDIR = "/path/to/directory/"
)


## Explore the effect of changing CODING_ONLY
## Check how gene name changes in the title of the plot
## (everything else stays the same)
cov <- brainflowprobes_cov("chr10:135379301-135379311:+")
four_panels("chr10:135379301-135379311:+", COVERAGE = cov)
four_panels("chr10:135379301-135379311:+",
    COVERAGE = cov,
    PDF = "coding_only_four_panels", CODING_ONLY = TRUE
)

## End(Not run)
## Here we use the pre-saved example coverage data such that this example
## will run fast!
four_panels("chr20:10286777-10288069:+",
    COVERAGE = four_panels_example_cov
)
## Not run: 
## Without using COVERAGE, this function reads BigWig files from the web
## using rtracklayer and this functionality is not supported on Windows
## machines.
if (.Platform$OS.type != "windows") {
    ## This example takes 10 minutes to run!
    four_panels("chr20:10286777-10288069:+")
}

## These examples will take several minutes to run depending on your
## internet connection
four_panels(c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
))

PENK_exons <- c(
    "chr8:57353587-57354496:-",
    "chr8:57358375-57358515:-",
    "chr8:57358985-57359040:-",
    "chr8:57359128-57359292:-"
)

## General syntax
four_panels(PENK_exons,
    JUNCTIONS = TRUE,
    PDF = "PDF_file.pdf", OUTDIR = "/path/to/directory/"
)

four_panels("chr20:10286777-10288069:+",
    PDF = "PDF_file.pdf", OUTDIR = "/path/to/directory/"
)


## Explore the effect of changing CODING_ONLY
## Check how gene name changes in the title of the plot
## (everything else stays the same)
cov <- brainflowprobes_cov("chr10:135379301-135379311:+")
four_panels("chr10:135379301-135379311:+", COVERAGE = cov)
four_panels("chr10:135379301-135379311:+",
    COVERAGE = cov,
    PDF = "coding_only_four_panels", CODING_ONLY = TRUE
)

## End(Not run)

Example base-pair region coverage data

Description

A list of base-pair region coverage data.frame lists used for exemplifying the package functionality. This is the data extracted for the example region ⁠chr20:10286777-10288069:+⁠ by looping through the phenotype tables stored in pd and using the getRegionCoverage function. This can be reproduced using the brainflowprobes_cov function in this package.

Format

A list with the base-pair coverage output from brainflowprobes_cov with the example region used throughout the package documentation ('chr20:10286777-10288069:+').

Sep: base-pair coverage region data.frame list for pd$Sep,
Deg: base-pair coverage region data.frame list for pd$Deg,
Cell: base-pair coverage region data.frame list for pd$Cell,
Sort: base-pair coverage region data.frame list for pd$Sort.

Examples

if (FALSE) {
    ## Takes about 10 minutes to run!
    four_panels_example_cov <- brainflowprobes_cov(
        "chr20:10286777-10288069:+"
    )
}
if (FALSE) {
    ## Takes about 10 minutes to run!
    four_panels_example_cov <- brainflowprobes_cov(
        "chr20:10286777-10288069:+"
    )
}

Compute the nearest annotation to the annoated genes in brainflowprobes

Description

For a given set of genomic regions, this function computes the nearest annotation information using the Annotated Genes required by this package. The Annotated Genes are actually provided by GenomicState::GenomicStateHub().

Usage

get_nearest_annotation(gr, CODING_ONLY = FALSE)
get_nearest_annotation(gr, CODING_ONLY = FALSE)

Arguments

`gr`	A GenomicRanges::GRanges() object.
`CODING_ONLY`	A logical vector of length 1 specifying whether to subset the Annotated Genes to only the coding genes. That is, whether to subset the genes by whether they have a non-NA `CSS` value. The Annotated Genes are downloaded with `GenomicState::GenomicStateHub()`.

Details

This is an utility function used by region_info, four_panels and plot_coverage.

Value

The bumphunter::matchGenes() output for the annotation information using the Annotated Genes for Gencode version 31 on hg19 coordinates (subset to only the coding elements if CODING_ONLY was set to TRUE).

Author(s)

Leonardo Collado-Torres

Examples


gr <- GenomicRanges::GRanges("chr10:135379301-135379311:+")

get_nearest_annotation(gr)
get_nearest_annotation(gr, CODING_ONLY = TRUE)
gr <- GenomicRanges::GRanges("chr10:135379301-135379311:+")

get_nearest_annotation(gr)
get_nearest_annotation(gr, CODING_ONLY = TRUE)

Check or compute the region coverage from the datasets in brainflowprobes

Description

This utility function checks the user-provided region data.frame coverage list (COVERAGE) or computes a new one using brainflowprobes_cov. This is used by four_panels and plot_coverage.

Usage

get_region_cov(
  REGION,
  COVERAGE = NULL,
  VERBOSE = TRUE,
  PD = brainflowprobes::pd
)
get_region_cov(
  REGION,
  COVERAGE = NULL,
  VERBOSE = TRUE,
  PD = brainflowprobes::pd
)

Arguments

`REGION`	Either a single hg19 genomic sequence including the chromosome, start, end, and optionally strand separated by colons (e.g., 'chr20:10199446-10288068:+'), or a string of sequences. Must be character. Chromosome must be proceeded by 'chr'.
`COVERAGE`	The output of brainflowprobes_cov for the input `REGION`. Defaults to `NULL` but it can be pre-computed and saved separately since this is the step that takes the longest to run. Also, this is the only step that depends on rtracklayer's functionality for reading BigWig files which does not run on Windows OS. So it could be run on a non-Windows machine, saved, and then shared with Windows users.
`VERBOSE`	A logical value indicating whether to print updates from the process of loading the data from the BigWig files.
`PD`	A list of data.frames with the `sumMapped` and `files` columns. Defaults to the data included in this package.

Value

If COVERAGE is provided and all checks pass, then this function returns COVERAGE. Otherwise, it computes a new region coverage data.frame list using brainflowprobes_cov.

Author(s)

Leonardo Collado-Torres

Examples


## If all checks pass, then it returns the COVERAGE
stopifnot(identical(
    get_region_cov(COVERAGE = four_panels_example_cov),
    four_panels_example_cov
))
## If all checks pass, then it returns the COVERAGE
stopifnot(identical(
    get_region_cov(COVERAGE = four_panels_example_cov),
    four_panels_example_cov
))

A list of phenotype data.frames

Description

A list of four phenotype data frames used throughput the package.

Format

A list of four data.frames:

Sep: phenotype information for samples from https://www.biorxiv.org/content/10.1101/567966v1; a data.frame with 23 rows and 15 columns. Column descriptions: SampleID is the sample name, Zone is the RNA fraction of the sample ("Nucleus" or "Cytosol"), Age, Sex, and Race list these demographic characteristics, Fetal categorizes each sample age as "Fetal" or "Adult", Library categorizes the RNAseq library preparation method as polyA selection ("polyA") or rRNA depletion ("RiboZero"), RIN_fraction is the RNA Integrity Number for each sample, sumMapped is the number of mapped reads, Shortlabels, Label, col, and LabelFrac are columns of information for plotting, BigWig is the name of the BigWig file for each sample, and files lists the URL for the BigWig online.
Deg: phenotype information for samples from https://www.pnas.org/content/114/27/7130; a data.frame with 40 rows and 16 columns. Column descriptions: DegradationTime is the number of minutes the brain tissue for each sample was left on the benchtop at room temperature before RNA was extracted, AgeDeath is the age of the donor at time of death, Dx lists whether the donor was diagnosed as schizophrenic or was a neurotypical control, Sex and Race list these demographic characteristics for each sample, pH lists the pH of the brain at collection, PMI is the postmortem interval between death and brain harvesting, BrNum is the donor number, RIN is the RNA Integrity Number for the RNA sample, LibraryProtocol categorizes the RNAseq library preparation method as polyA selection ("polyA") or rRNA depletion ("Ribo"), SampleID is the ID for the sample, totalAssignedGene is the proportion of reads mapping to a gene body, sumMapped is the number of mapped reads, BigWig is the name of the BigWig file, SampleID_library is the Sample ID and library column values together, and files lists the URL for the BigWig online.
Cell: phenotype information for samples from https://www.pnas.org/content/112/23/7285; a data.frame with 466 rows and 11 columns. Column descriptions: geo_accession is the accession number for each sample in the Gene Expression Omnibus, Age is the numeric age of each sample, AgeGroup categorizes each sample age as "prenatal" or "postnatal", sub_tissue lists whether the sample was from cortex or hippocampus, Cluster_color is the color for each sample for plotting, Cell_type is the cell identity assigned to each sample, RunName is the run name assigned to each sample in the Sequence Read Archive (SRA), SubjectID is the subject label, sumMapped is the number of mapped reads, BigWig is the name of the BigWig file, and files lists the URL for the BigWig online.
Sort: phenotype information for samples from https://www.biorxiv.org/content/10.1101/428391v2; a data.frame with 12 rows and 12 columns. Column descriptions: Description categorizes the RNAseq library preparation method as polyA selection ("PolyA") or rRNA depletion ("Ribo"), SubjectID is the subject number, CellType lists whether the sample was labeled by NeuN antibody (neuronal, "NeuN_Plus") or not (non-neuronal, "NeuN_Minus"), SampleID combines the sample number, cell type and library of each sample in one column, BrNum is the donor ID, RIN is the RNA Integrity Number for each RNA sample, Age is the numeric age at death, sumMapped is the number of mapped reads, Label and col provide information for plotting, BigWig is the name of each BigWig file, and files lists the URL for the BigWig online.

Examples

##  pd <- list(Sep = pdSep, Deg = pdDeg, Cell = pdCell, Sort = pdSort)
##  pd <- list(Sep = pdSep, Deg = pdDeg, Cell = pdCell, Sort = pdSort)

Plot coverage in nuclear and cytoplasmic RNA for candidate probe sequences.

Description

plot_coverage plots the coverage of nuclear and cytoplasmic RNA from adult and prenatal human prefrontal cortex across a candidate or series of candidate probe sequences for BrainFlow. If the sequence spans splice junctions, the plot will include the introns. A good candidate sequence will be highly and evenly expressed in nuclear RNA.

Usage

plot_coverage(
  REGION,
  PDF = "regionCoverage_fractionedData.pdf",
  OUTDIR = tempdir(),
  COVERAGE = NULL,
  CODING_ONLY = FALSE,
  VERBOSE = TRUE
)
plot_coverage(
  REGION,
  PDF = "regionCoverage_fractionedData.pdf",
  OUTDIR = tempdir(),
  COVERAGE = NULL,
  CODING_ONLY = FALSE,
  VERBOSE = TRUE
)

Arguments

`REGION`	Either a single hg19 genomic sequence including the chromosome, start, end, and optionally strand separated by colons (e.g., 'chr20:10199446-10288068:+'), or a string of sequences. Must be character. Chromosome must be proceeded by 'chr'.
`PDF`	The name of the PDF file. Defaults to `regionCoverage_fractionedData.pdf`.
`OUTDIR`	The default directory where `PDF` will be saved to.
`COVERAGE`	The output of brainflowprobes_cov for the input `REGION`. Defaults to `NULL` but it can be pre-computed and saved separately since this is the step that takes the longest to run. Also, this is the only step that depends on rtracklayer's functionality for reading BigWig files which does not run on Windows OS. So it could be run on a non-Windows machine, saved, and then shared with Windows users.
`CODING_ONLY`	A logical vector of length 1 specifying whether to subset the Annotated Genes to only the coding genes. That is, whether to subset the genes by whether they have a non-NA `CSS` value. The Annotated Genes are downloaded with `GenomicState::GenomicStateHub()`.
`VERBOSE`	A logical value indicating whether to print updates from the process of loading the data from the BigWig files.

Value

plot_coverage plots all input sequences using derfinderPlot::plotRegionCoverage(). It returns a plot for each input candidate sequence listed in REGION. Each plot includes coverage of the sequence(s) in nuclear (N) and cytoplasmic (C) RNA isolated from adult (A) and fetal (F) prefrontal cortex, sequenced using two different library preparation methods. PolyA+ libraries (P) were generated using selection for polyadenylated transcripts, and RiboZero (R) libraries were generated using a ribosomal RNA depletion protocol.

Each plot also shows the overlapping genes beneath the coverage, and the genomic location. The title lists the nearest gene, the position of the sequence relative to the gene's canonical transcriptional start site (TSS), and further annotation information as described in the 'region' column from bumphunter::matchGenes().

plot_coverage saves the results as regionCoverage_fractionedData.pdf in a temporary directory unless otherwise specified with OUTDIR.

Author(s)

Amanda J Price

Examples


## Here we use the pre-saved example coverage data such that this example
## will run fast!
plot_coverage("chr20:10286777-10288069:+",
    COVERAGE = four_panels_example_cov
)

## Without using COVERAGE, this function reads BigWig files from the web
## using rtracklayer and this functionality is not supported on Windows
## machines.
if (.Platform$OS.type != "windows") {
    plot_coverage("chr20:10286777-10288069:+",
        PDF = "regionCoverage_fractionedData_fromScratch.pdf"
    )
}
## Not run: 

## These examples will take a few minutes to run!
plot_coverage(c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
))

candidates <- c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
)

## General syntax:
plot_coverage(candidates,
    PDF = "PDF_file.pdf",
    OUTDIR = "/path/to/directory/"
)

plot_coverage("chr20:10286777-10288069:+",
    PDF = "PDF_file.pdf", OUTDIR = "/path/to/directory/"
)

## Explore the effect of changing CODING_ONLY
## Check how gene name and distance to TSS changes in the title of the plot
## (everything else stays the same)
cov <- brainflowprobes_cov("chr10:135379301-135379311:+")
plot_coverage("chr10:135379301-135379311:+", COVERAGE = cov)
plot_coverage("chr10:135379301-135379311:+",
    COVERAGE = cov,
    PDF = "coding_only_plot_coverage", CODING_ONLY = TRUE
)

## End(Not run)

## Here we use the pre-saved example coverage data such that this example
## will run fast!
plot_coverage("chr20:10286777-10288069:+",
    COVERAGE = four_panels_example_cov
)

## Without using COVERAGE, this function reads BigWig files from the web
## using rtracklayer and this functionality is not supported on Windows
## machines.
if (.Platform$OS.type != "windows") {
    plot_coverage("chr20:10286777-10288069:+",
        PDF = "regionCoverage_fractionedData_fromScratch.pdf"
    )
}
## Not run: 

## These examples will take a few minutes to run!
plot_coverage(c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
))

candidates <- c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
)

## General syntax:
plot_coverage(candidates,
    PDF = "PDF_file.pdf",
    OUTDIR = "/path/to/directory/"
)

plot_coverage("chr20:10286777-10288069:+",
    PDF = "PDF_file.pdf", OUTDIR = "/path/to/directory/"
)

## Explore the effect of changing CODING_ONLY
## Check how gene name and distance to TSS changes in the title of the plot
## (everything else stays the same)
cov <- brainflowprobes_cov("chr10:135379301-135379311:+")
plot_coverage("chr10:135379301-135379311:+", COVERAGE = cov)
plot_coverage("chr10:135379301-135379311:+",
    COVERAGE = cov,
    PDF = "coding_only_plot_coverage", CODING_ONLY = TRUE
)

## End(Not run)

Print relevant info about candidate probe sequence.

Description

region_info returns annotation of a single potential probe sequence or list of sequences and, if specified, prints the resuts in a .csv file.

Usage

region_info(
  REGION,
  CSV = TRUE,
  SEQ = TRUE,
  OUTDIR = tempdir(),
  CODING_ONLY = FALSE
)
region_info(
  REGION,
  CSV = TRUE,
  SEQ = TRUE,
  OUTDIR = tempdir(),
  CODING_ONLY = FALSE
)

Arguments

`REGION`	Either a single hg19 genomic sequence including the chromosome, start, end, and optionally strand separated by colons (e.g., `'chr20:10199446-10288068:+'`), or a string of sequences to be annotated. Must be character. Chromosome must be proceeded by 'chr'.
`CSV`	A `logical(1)` value indicating if the results should be exported in a .csv file.
`SEQ`	A 'logical(1)“ value indicating if the base sequence should be returned.
`OUTDIR`	If a .csv file is to be exported, this parameter indicates the path where the file should be saved. By default the file will be saved in a temporary directory.
`CODING_ONLY`	A logical vector of length 1 specifying whether to subset the Annotated Genes to only the coding genes. That is, whether to subset the genes by whether they have a non-NA `CSS` value. The Annotated Genes are downloaded with `GenomicState::GenomicStateHub()`.

Value

This function annotates all input sequences using bumphunter::matchGenes(). It returns a data frame where each row is a genomic sequence specified in REGION. The columns c('seqnames', 'start', 'end', 'width', 'strand') list the chromosome, range, sequence length, and strand of the REGION. The columns c('name', 'annotation', 'description', 'region', 'distance', 'subregion', 'insideDistance', 'exonnumber', 'nexons', 'UTR', 'geneL', 'codingL', 'Geneid', 'subjectHits') are described in bumphunter::matchGenes() documentation.

If SEQ=TRUE, a column 'Sequence' will be included. This is recommended for sending the probe sequence to be synthesized.

If CSV=TRUE, a .csv file called region_info.csv will be saved to a temporary directory unless otherwise specified in OUTDIR.

Author(s)

Amanda J Price

Examples

x <- region_info("chr20:10286777-10288069:+", CSV = FALSE)
head(x)

## You can easily transform this data.frame to a GRanges object
GenomicRanges::GRanges(x)

y <- region_info(
    c(
        "chr20:10286777-10288069:+",
        "chr18:74690788-74692427:-",
        "chr19:49932861-49933829:-"
    ),
    CSV = FALSE, SEQ = FALSE
)
head(y)

candidates <- c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
)
region_info(candidates, CSV = FALSE)

## Explore the effect of changing CODING_ONLY
## Check how the "distance", "name", "Geneid" among other values change
region_info("chr10:135379301-135379311:+", CSV = FALSE)
region_info("chr10:135379301-135379311:+", CSV = FALSE, CODING_ONLY = TRUE)
## Not run: 
region_info(candidates, OUTDIR = "/path/to/directory/")

region_info("chr20:10286777-10288069:+", OUTDIR = "/path/to/directory")

## End(Not run)
x <- region_info("chr20:10286777-10288069:+", CSV = FALSE)
head(x)

## You can easily transform this data.frame to a GRanges object
GenomicRanges::GRanges(x)

y <- region_info(
    c(
        "chr20:10286777-10288069:+",
        "chr18:74690788-74692427:-",
        "chr19:49932861-49933829:-"
    ),
    CSV = FALSE, SEQ = FALSE
)
head(y)

candidates <- c(
    "chr20:10286777-10288069:+",
    "chr18:74690788-74692427:-",
    "chr19:49932861-49933829:-"
)
region_info(candidates, CSV = FALSE)

## Explore the effect of changing CODING_ONLY
## Check how the "distance", "name", "Geneid" among other values change
region_info("chr10:135379301-135379311:+", CSV = FALSE)
region_info("chr10:135379301-135379311:+", CSV = FALSE, CODING_ONLY = TRUE)
## Not run: 
region_info(candidates, OUTDIR = "/path/to/directory/")

region_info("chr20:10286777-10288069:+", OUTDIR = "/path/to/directory")

## End(Not run)

Package 'brainflowprobes'

Help Index

Extract coverage data for a set of regions

Description

Usage

Arguments

Value

Author(s)

Examples

Check the PDF file

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Plot expression coverage in four datasets for candidate probe sequences.

Description

Usage

Arguments

Value

Author(s)

Examples

Example base-pair region coverage data

Description

Format

See Also

Examples

Compute the nearest annotation to the annoated genes in brainflowprobes

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Check or compute the region coverage from the datasets in brainflowprobes

Description

Usage

Arguments

Value

Author(s)

Examples

A list of phenotype data.frames

Description

Format

See Also

Examples

Plot coverage in nuclear and cytoplasmic RNA for candidate probe sequences.

Description

Usage

Arguments

Value

Author(s)

Examples

Print relevant info about candidate probe sequence.

Description

Usage

Arguments

Value

Author(s)

Examples