| Title: | Gene Ontology analyser for RNA-seq and other length biased data |
|---|---|
| Description: | Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data. |
| Authors: | Matthew Young [aut],
Nadia Davidson [aut],
Federico Marini [ctb, cre] |
| Maintainer: | Federico Marini <[email protected]> |
| License: | LGPL (>= 2) |
| Version: | 1.57.2 |
| Built: | 2024-07-13 05:53:34 UTC |
| Source: | https://github.com/bioc/goseq |
This data set gives the RNA-seq data from an experiment measuring the
effects of androgen stimulation on prostate cancer. Information is given
about all (ENSEMBL) genes for which there was at least one mapping read in
either the treated or untreated RNA-seq experiment. The edgeR
package was used to determine which genes were differentially expressed.
The details of the analysis can be found in the goseq vignette.
A named vector of ENSEMBL genes, with 1 representing differential expression.
Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model, 2008, Li et. al.
Li, H., Lovci, M. T., Kwon, Y. S., Rosenfeld, M. G., Fu, X. D., Yeo, G. W. (2008) Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model Proceedings of the National Academy of Sciences of the United States of America Date: Dec 23 Vol: 105 Issue: 51 Pages: 20179-84
data(genes) head(genes)data(genes) head(genes)
Obtains all gene ontology (GO) categories associated with a set of genes using the relevant organism package.
getgo(genes, genome, id, fetch.cats = c("GO:CC", "GO:BP", "GO:MF"))getgo(genes, genome, id, fetch.cats = c("GO:CC", "GO:BP", "GO:MF"))
genes |
A vector or list of genes to get the associated GO categories. |
genome |
A string identifying the genome that |
id |
A string identifying the gene identifier used by |
fetch.cats |
A vector specifying which categories to fetch the mapping between category names and genes for. See details for valid options. |
This function attempts to make use of the organism packages
(org.<Genome>.<GeneID>.db) to obtain the mapping between gene ID and GO
categories. As with getlength it is preferable that the same
gene identifier system is used for both summarization and retrieving GO
categories.
Valid options for the fetch.cats argument are any combination of
"GO:CC", "GO:BP", "GO:MF" & "KEGG". The three GO terms refer to the
Cellular Component, Biological Process and Molecular Function respectively.
"KEGG" refers to KEGG pathways.
Note that getgo is a convenience function, designed to make
extracting mappings between GO categories and Gene ID easy. For less common
organisms and/or gene ID getgo may fail to return a mapping even when
a legitimate mapping exists in the relevant organism package. If
getgo fails, you should always try to build the mapping yourself from
the organism package (if one exists) before deciding that the information is
unavailable. Further information and examples of this can be found in the
package Vignette.
A list where each entry is named by a gene and contains a vector of
all the associated GO categories. This can be used directly with the
gene2cat option in goseq.
Matthew D. Young [email protected]
supportedGenomes, supportedGeneIDs,
goseq
genes <- c("ENSG00000124208", "ENSG00000182463", "ENSG00000124201", "ENSG00000124205", "ENSG00000124207") getgo(genes,'hg19','ensGene')genes <- c("ENSG00000124208", "ENSG00000182463", "ENSG00000124201", "ENSG00000124205", "ENSG00000124207") getgo(genes,'hg19','ensGene')
Gets the length of each gene in a vector.
getlength(genes, genome, id)getlength(genes, genome, id)
genes |
A vector or list of the genes for which length information is required. |
genome |
A string identifying the genome that |
id |
A string identifying the gene identifier used by |
Length data is obtained from data obtained from the UCSC genome browser for
each combination of genome and id. As fetching this data at
runtime is time consuming, a local copy of the length information for common
genomes and gene ID are included in the geneLenDataBase package. This
function uses this package to fetch the required data.
The length of a gene is taken to be the median length of all its mature, mRNA, transcripts. It is always preferable to obtain length information directly for the gene ID used to summarize your count data, rather than converting IDs and then using the supplied databases. Even when two genes have a one-to-one mapping between different identifier conventions (which is often not the case), they frequently refer to slightly different regions of the genome with different lengths. It is therefore recommended that the user perform the full analysis in terms of only one gene ID, or manually obtain their own length data for the identifier used to bin reads by gene.
Returns a vector of the gene lengths, in the same order as
genes. If length data is unavailable for a particular gene NA is
returned in that position. The returned vector is intended for use with the
bias.data option of the nullp function.
Matthew D. Young [email protected]
supportedGenomes, supportedGeneIDs,
nullp, geneLenDataBase
genes <- c("ENSG00000124208", "ENSG00000182463", "ENSG00000124201", "ENSG00000124205", "ENSG00000124207") getlength(genes,'hg19','ensGene')genes <- c("ENSG00000124208", "ENSG00000182463", "ENSG00000124201", "ENSG00000124205", "ENSG00000124207") getlength(genes,'hg19','ensGene')
Does selection-unbiased testing for category enrichment amongst differentially expressed (DE) genes for RNA-seq data. By default, tests gene ontology (GO) categories, but any categories may be tested.
goseq( pwf, genome, id, gene2cat = NULL, test.cats = c("GO:CC", "GO:BP", "GO:MF"), method = "Wallenius", repcnt = 2000, use_genes_without_cat = FALSE )goseq( pwf, genome, id, gene2cat = NULL, test.cats = c("GO:CC", "GO:BP", "GO:MF"), method = "Wallenius", repcnt = 2000, use_genes_without_cat = FALSE )
pwf |
An object containing gene names, DE calls, the probability
weighting function. Usually generated by |
genome |
A string identifying the genome that |
id |
A string identifying the gene identifier used by |
gene2cat |
A data frame with two columns containing the mapping between
genes and the categories of interest. Alternatively, a list where the names
are genes and each entry is a vector containing GO categories associated
with that gene (this is the output produced by |
test.cats |
A vector specifying which categories to test for over representation amongst DE genes. See details for allowed options. |
method |
The method to use to calculate the unbiased category enrichment scores. Valid options are "Wallenius", "Sampling" & "Hypergeometric". "Hypergeometric" and "Sampling" should almost never be used (see details). |
repcnt |
Number of random samples to be calculated when random sampling
is used. Ignored unless |
use_genes_without_cat |
A boolean to indicate whether genes without a category should still be used. For example, a large number of gene may have no GO term annotated. If this option is set to FALSE, those genes will be ignored in the calculation of p-values (default behaviour). If this option is set to TRUE, then these genes will count towards the total number of genes outside the category being tested (default behaviour prior to version 1.15.2). |
The pwf argument is almost always the output of the function
nullp. This is a data frame with 3 columns, named "DEgenes",
"bias.data" and "pwf" with the rownames set to the gene names. Each row
corresponds to a gene with the DEgenes column specifying if the gene is DE
(1 for DE, 0 for not DE), the bias.data column giving the numeric value of
the DE bias being accounted for (usually the gene length or number of
counts) and the pwf column giving the genes value on the probability
weighting function.
goseq obtains length data from UCSC and GO mappings from the organism
packages (see link{getgo} and getlength for details).
If your data is in an unsupported format you will need to obtain the GO
category mapping and supply them to the goseq function using the
gene2cat argument.
To use your own gene to category mapping with goseq, use the
gene2cat argument. This argument takes a data.frame, with one column
containing gene IDs and the other containing the associated categories. As
the mapping from gene <-> category is in general many to many there will be
multiple rows containing the same gene identifier. Alternatively,
gene2cat can take a list, where the names are the genes and the
entries are the GO categories associated with the genes. This is the format
produced by the getgo function and is more space efficient
than the data.frame representation.
If gene2cat is left as NULL, goseq attempts to use
getgo to fetch GO category to gene identifier mappings.
The PWF is usually calculated using the nullp function to
correct for length bias. However, goseq will work with any vector of
weights. Any bias can be accounted for so long as a weight for each gene is
supplied using this argument. NAs are allowed in the "pwf" and
"bias.data" columns of the PWF data frame (these usually occur as a result
of missing length data for some genes). Any entry which is NA is set
to the weighting of the median gene.
Valid options for the test.cats argument are any combination of
"GO:CC", "GO:BP", "GO:MF" & "KEGG". The three GO terms refer to the
Cellular Component, Biological Process and Molecular Function respectively.
"KEGG" refers to KEGG pathways.
The three methods, "Wallenius", "Sampling" & "Hypergeometric", calculate the p-values as follows.
"Wallenius" approximates the true distribution of numbers of members of a category amongst DE genes by the Wallenius non-central hypergeometric distribution. This distribution assumes that within a category all genes have the same probability of being chosen. Therefore, this approximation works best when the range in probabilities obtained by the probability weighting function is small. "Wallenius" is the recommended method for calculating p-values.
"Sampling" uses random sampling to approximate the true distribution and
uses it to calculate the p-values for over (and under) representation of
categories. In practice, its use quickly becomes computationally prohibitive
because repcnt would need to be set very high for most applications.
CAUTION: "Hypergeometric" should NEVER be used for producing results for biological interpretation. If there is genuinely no bias in power to detect DE in your experiment, the PWF will reflect this and the other methods will produce accurate results.
"Hypergeometric" assumes there is no bias in power to detect differential expression at all and calculates the p-values using a standard hypergeometric distribution. Useful if you wish to test the effect of selection bias on your results.
goseq returns a data frame with several columns. The first column gives the name of the category, the second gives the p-value for the associated category being over represented amongst DE genes. The third column gives the p-value for the associated category being under represented amongst DE genes. The p-values have not been corrected for multiple hypothesis testing. The fourth and fifth columns give the number of differentially expressed genes in the category and total genes in the category respectively. If any of the categories was a GO term, there will be two additional columns for the GO term and its ontology.
Matthew D. Young [email protected]
Young, M. D., Wakefield, M. J., Smyth, G. K., Oshlack, A. (2010) Gene ontology analysis for RNA-seq: accounting for selection bias Genome Biology Date: Feb 2010 Vol: 11 Issue: 2 Pages: R14
data(genes) pwf <- nullp(genes,'hg19','ensGene') pvals <- goseq(pwf,'hg19','ensGene') head(pvals)data(genes) pwf <- nullp(genes,'hg19','ensGene') pvals <- goseq(pwf,'hg19','ensGene') head(pvals)
Fits a monotonic cubic spline to the data provided, using the penalized
constrained least squares method from the mgcv package.
makespline(x, y, newX = NULL, nKnots = 6, lower_bound = 10^-3)makespline(x, y, newX = NULL, nKnots = 6, lower_bound = 10^-3)
x |
The predictor variable. |
y |
The response variable. Must be the same length as |
newX |
The points at which to return the value on the fitted spline.
If not specified |
nKnots |
The number of knots to use in fitting the spline. |
lower_bound |
The spline cannot drop below this value. |
This uses the pcls function from the mgcv package to produce
the fit. The monotonicity constraint is enforced using mono.con from
the same package. The lower_bound argument is only used on the rare
occasions when the fitting function becomes negative or arbitrarily close to
zero. If this does occur lower_bound is added everywhere to ensure
that no one length is given essentially infinite weighting.
Returns a vector of values containing the value of the fit at each
point newX.
Matthew D. Young [email protected].
Package mgcv. In particular this function is a
modification of an example given in the man page for pcls.
y <- c( rbinom(50,p=0.4,size=1), rbinom(50,p=0.6,size=1) ) x <- 1:100 plot(x,y) p <- makespline(x,y) lines(x,p)y <- c( rbinom(50,p=0.4,size=1), rbinom(50,p=0.6,size=1) ) x <- 1:100 plot(x,y) p <- makespline(x,y) lines(x,p)
Calculates a Probability Weighting Function for a set of genes based on a given set of biased data (usually gene length) and each genes status as differentially expressed or not.
nullp(DEgenes, genome, id, bias.data = NULL, plot.fit = TRUE)nullp(DEgenes, genome, id, bias.data = NULL, plot.fit = TRUE)
DEgenes |
A named binary vector where 1 represents DE, 0 not DE and the names are gene IDs. |
genome |
A string identifying the genome that |
id |
A string identifying the gene identifier used by |
bias.data |
A numeric vector containing the data on which the DE may
depend. Usually this is the median transcript length of each gene in bp.
If set to |
plot.fit |
Plot the PWF or not? Calls |
It is essential that the entire analysis pipeline, from summarizing raw
reads through to using goseq be done in just one gene identifier
format. If your data is in a different format you will need to obtain the
gene lengths and supply them to the nullp function using the
bias.data argument. Converting to a supported format from another
format should be avoided whenever possible as this will almost always result
in data loss.
NAs are allowed in the bias.data vector if you do not have
information about a certain gene. Setting a gene to NA is preferable
to removing it from the analysis.
If bias.data is left as NULL, nullp attempts to use
getlength to fetch GO category to gene identifier mappings.
It is recommended you review the fit produced by the nullp function
before proceeding by leaving plot.fit as TRUE.
A data frame with 3 columns, named "DEgenes", "bias.data" and "pwf"
with the rownames set to the gene names. Each row corresponds to a gene
with the DEgenes column specifying if the gene is DE (1 for DE, 0 for not
DE), the bias.data column giving the numeric value of the DE bias being
accounted for (usually the gene length or number of counts) and the pwf
column giving the genes value on the probability weighting function. This
object is usually passed to goseq to calculate enriched categories or
plotPWF for further plotting.
Matthew D. Young [email protected]
Young, M. D., Wakefield, M. J., Smyth, G. K., Oshlack, A. (2010) Gene ontology analysis for RNA-seq: accounting for selection bias Genome Biology Date: Feb 2010 Vol: 11 Issue: 2 Pages: R14
supportedGenomes, supportedGeneIDs,
goseq, getlength
data(genes) pwf <- nullp(genes, 'hg19', 'ensGene')data(genes) pwf <- nullp(genes, 'hg19', 'ensGene')
Plots the Probability Weighting Function created by nullp by
binning together genes.
plotPWF( pwf, binsize = "auto", pwf_col = 3, pwf_lwd = 2, xlab = "Biased Data in <binsize> gene bins.", ylab = "Proportion DE", ... )plotPWF( pwf, binsize = "auto", pwf_col = 3, pwf_lwd = 2, xlab = "Biased Data in <binsize> gene bins.", ylab = "Proportion DE", ... )
pwf |
A data frame with 3 columns named DEgenes, bias.data & pwf and
row names giving the gene names. Usually generated by |
binsize |
Calculate and plot the fraction of genes that are DE in bins of this size. If set to "auto" the best binsize for visualization is attempted to be found automatically. |
pwf_col |
The colour of the probability weighting function |
pwf_lwd |
The width of the probability weighting function |
xlab |
The x-axis label. |
ylab |
The y-axis label. |
... |
Extra arguments that are passed to plot. |
This function is almost always called using the output from the
nullp function. However, it can be used to visualize the
length (or any other type of quantifiable) bias in ability to detect DE in a
data set. The pwf argument needs to be a data frame with 3 columns
each containing numeric entries (although NAs are permitted in the bias.data
and pwf columns), which must be named "DEgenes", "bias.data" and "pwf",
although they can appear in any order. The row names are taken to be the
gene names. The DEgenes column should be 0s or 1s where 1 represents a DE
gene, 0 a gene which is not DE. The bias.data column is a quantification of
the quantity for which there is a bias in detecting DE for the associated
gene, this is usually gene length or the number of counts associated with a
gene. Finally, the pwf column gives the probability weighting to be applied
for a given gene.
Nothing is returned.
Matthew D. Young [email protected]
Young, M. D., Wakefield, M. J., Smyth, G. K., Oshlack, A. (2010) Gene ontology analysis for RNA-seq: accounting for selection bias Genome Biology Date: Feb 2010 Vol: 11 Issue: 2 Pages: R14
data(genes) pwf <- nullp(genes, 'hg19', 'ensGene',plot.fit=FALSE) plotPWF(pwf,binsize=200)data(genes) pwf <- nullp(genes, 'hg19', 'ensGene',plot.fit=FALSE) plotPWF(pwf,binsize=200)
Prints progress through a loop
pp(total, count, i = i)pp(total, count, i = i)
total |
total number of iterations |
count |
current iteration |
i |
index of the loop |
message indicating the progress
Lists which genomes and gene ids are supported for gene lengths and for gene ontology
supportedOrganisms()supportedOrganisms()
Goseq allows a user to provide their own bias data (usually gene lengths)
and/or gene categories (usually gene ontologies), but goseq also provides
this data automatically for many commonly used species. This function lists
which genome and gene ids are automatically supported by goseq. The first to
third columns list the genomes, gene ids, and gene id descriptions
respectively. The fourth column indicates whether this combination of genome
and id are available in the geneLengthDataBase. If a particular combination
is absent, goseq may still automatically fetch the gene lengths from either
a TxDB annotation package (must be installed) or download the data from
UCSC. For example gene lengths for hg38 are not supported in
geneLengthDataBase but may be fetched by these other means. However,
this is not always the case. The final column indicates if GO terms will be
automatically fetched for the genome and id combination. Goseq relies on an
org annotation package (e.g. org.Hs.eg.db) existing for the
organism. In general, if either the lengths or GO terms are not supported,
the user must enter this information manually.
A data.frame containing supported genomes and gene ids
Nadia Davidson [email protected]
supportedOrganisms()supportedOrganisms()