| Title: | RNA-seq count data employed in the vignette of the tweeDEseq package |
|---|---|
| Description: | RNA-seq count data from Pickrell et al. (2010) employed to illustrate the use of the Poisson-Tweedie family of distributions with the tweeDEseq package. |
| Authors: | Dolors Pelegri-Siso [aut, cre] (ORCID: <https://orcid.org/0000-0002-5993-3003>), Juan R. Gonzalez [aut] (ORCID: <https://orcid.org/0000-0003-3267-2146>), Mikel Esnaola [aut], Robert Castelo [aut] |
| Maintainer: | Dolors Pelegri-Siso <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.50.0 |
| Built: | 2026-10-10 11:04:32 UTC |
| Source: | https://github.com/bioc/tweeDEseqCountData |
RNA-seq tables of counts employed in the vignette of the tweeDEseq package
(Esnaola et al., submitted). These three data sets were downloaded from the
ReCount repository at http://bowtie-bio.sourceforge.net/recount and contain
tables of counts from RNA-seq experiments by Cheung et al. (2010), Montgomery et
al. (2010) and Pickrell et al. (2010). The raw RNA-seq data was pre-processed
according to the procedures described by Frazee et al. (2011). Please check the
individual help pages of each data set for further details.
void
V.G. Cheung, R.R. Nayak, I.X. Wang, S. Elwyn, S.M. Cousins, M. Morley and R.S. Spielman. Plos Biology, 8(9), pii:e1000480, 2010.
A.C. Frazee, B. Langmead and J.T. Leek. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12:449, 2011.
S.B. Montgomery, M. Sammeth, M. Gutierrez-Arcelus, R.P. Lach, C. Ingle, J. Nisbett, R. Guigo and E.T. Dermitzakis. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature, 464:773-777, 2010.
J.K. Pickrell, J.C. Marioni, A.A Pai, J.F. Degner, B.E. Engelhardt, E. Nkadori, J.B Veyrieras, M. Stephens, Y. Gilad, and J.K. Pritchard. Nature, 464:768-772, 2010.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication, submitted.
annotEnsembl63
cheung
montgomery
pickrell
genderGenes
hkGenes
Annotation data for the human genes forming the tables of counts in
pickrell.eset, montgomery.eset and
cheung.eset.
data(annotEnsembl63)data(annotEnsembl63)
Symbol: gene symbol according to the HUGO Gene Nomenclature Committee (HGNC).
Chr: chromosome.
Start: start chromosomal position (Human genome version GRCh37).
End: end chromosomal position (Human genome version GRCh37).
EntrezID: Entrez gene identifier.
Description: Short description of the gene.
Length: Length of the longest cDNA of this gene.
GCcontent: G+C content of the longest cDNA of this gene.
Data for all columns except Length and GCcontent was retrieved
from Ensembl release 63 using the biomaRt package. Data in columns
Length and GCcontent was obtained by downloading the set of
Ensembl Release 63 human cDNAs at ftp://ftp.ensembl.org/pub/release-63/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh37.63.cdna.all.fa.gz and selecting the longest
cDNA for each Ensembl Gene from which length and G+C content was calculated.
annotEnsembl63 dataset
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, and J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication. Submitted.
pickrell
pickrellNorm
montgomery
genderGenes
hkGenes
data(annotEnsembl63) dim(annotEnsembl63) head(annotEnsembl63)data(annotEnsembl63) dim(annotEnsembl63) head(annotEnsembl63)
ExpressionSet object containing RNA-seq count data from lymphoblastoid
cell lines from 41 unrelated Caucasian individuals of European descent. These
count data are employed in the vignette of the package tweeDEseq Esnaola
et al. (submitted). The original experimental data was published by Cheung
et al. (2010) and the table of counts in this ExpressionSet object
corresponds to the one in the ReCount repository available at
http://bowtie-bio.sourceforge.net/recount. Details on the pre-processing
steps to obtain this table of counts from the raw reads of Cheung et al.
(2010) are provided on that website and in the publication by Frazee et al. (2011).
data(cheung)data(cheung)
cheung.eset: ExpressionSet object containing read counts
for 52,580 Ensembl genes for each of the 41 Caucasian
individuals of European descent.
The table of counts is stored in the AssayData slot of an
ExpressionSet object called cheung.eset whose phenotypic
data contains the gender of each individual, among other bits of information.
cheung dataset
V.G. Cheung, R.R. Nayak, I.X. Wang, S. Elwyn, S.M. Cousins, M. Morley and R.S. Spielman. Plos Biology, 8(9), pii:e1000480, 2010.
A.C. Frazee, B. Langmead and J.T. Leek. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12:449, 2011.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication. Submitted.
annotEnsembl63
pickrell
cheung
genderGenes
hkGenes
suppressMessages(library(Biobase)) data(cheung) cheung.eset table(cheung.eset$gender)suppressMessages(library(Biobase)) data(cheung) cheung.eset table(cheung.eset$gender)
ExpressionSet objects containing microarray and RNA-seq count data for
36 matching samples of lymphoblastoid cell lines derived from unrelated Nigerian
HapMap individuals. These microarray and count data are employed in the vignette
of the package tweeDEseq and supporting analysis scripts of the article by
Esnaola et al. (submitted). The original experimental data was published by Huang
et al. (2007) and Pickrell et al. (2010).
The loaded objects can be divided into two classes of data. One corresponding to gene expression data from microarray and RNA-seq with matching samples, and the other where not only samples, but also genes match between the two gene expression data matrices, thus being a subset of the former.
data(commonPickrell1Huang)data(commonPickrell1Huang)
huangArrayRMAnoBatchCommonSamples.eset: ExpressionSet object containing filtered,
normalized and batch-removed microarray expression (RMA) values for 16,323
Ensembl Gene identifiers from 36 unrelated Nigerian individuals.
huangArrayRMAnoBatchCommon.eset: ExpressionSet object containing filtered,
normalized and batch-removed microarray expression (RMA) values for 15,194
Ensembl Gene identifiers from 36 unrelated Nigerian individuals.
pickrell1countsCommonSamples.eset: ExpressionSet object containing filtered
RNA-seq read counts for 27,438 Ensembl Gene
identifiers from 36 unrelated Nigerian individuals. This table of
counts corresponds to RNA-seq data published by Pickrell et al. (2010)
and processed by the pipeline described by Esnaola et al. (submitted).
pickrell1countsCommon.eset: ExpressionSet object containing filtered
RNA-seq read counts for 15,194 Ensembl Gene
identifiers from 36 unrelated Nigerian individuals. This table of
counts corresponds to RNA-seq data published by Pickrell et al. (2010)
and processed by the pipeline described by Esnaola et al. (submitted).
pickrell1countsCQNcommonSamples.eset: ExpressionSet object containing the table of read counts in
pickrell1countsCommonSamples.eset normalized using the package cqn. The
transformation from log CPM values and their offsets, as produced by cqn,
into this table of normalized counts was done with the function normalizeCounts()
of the tweeDEseq package.
pickrell1countsCQNcommon.eset: ExpressionSet object containing the table of read counts in
pickrell1countsCommon.eset normalized using the package cqn. The
transformation from log CPM values and their offsets, as produced by cqn,
into this table of normalized counts was done with the function normalizeCounts()
of the tweeDEseq package.
cqnNormCommonSamples: list object output by the cqn() function from the
cqn package when normalizing the RNA-seq data in pickrell1countsCommonSamples.eset
and used by the function normalizeCounts() from the tweeDEseq package
to obtain the normalized count expression data matrix in the ExpressionSet object
pickrell1countsCQNcommonSamples.eset. This object is necessary when
using DE detection methods, such as edgeR, that employ the offsets
given by cqn and the raw counts in pickrell1countsCommonSamples.eset
instead of the transformed normalized counts in pickrell1countsCQNcommonSamples.eset.
cqnNormCommon: list object output by the cqn() function from the
cqn package when normalizing the RNA-seq data in pickrell1countsCommon.eset
and used by the function normalizeCounts() from the tweeDEseq package
to obtain the normalized count expression data matrix in the ExpressionSet object
pickrell1countsCQNcommon.eset. This object is necessary when
using DE detection methods, such as edgeR, that employ the offsets
given by cqn and the raw counts in pickrell1countsCommon.eset
instead of the transformed normalized counts in pickrell1countsCQNcommon.eset.
The microarray data was processed from the raw CEL files available at
http://www.ncbi.nlm.nih.gov/geo under accession GSE7792. First, only
Yoruba samples were considered. Second, data was processed using the Bioconductor
oligo package. Quality assessment was performed by calculating NUSE and RLE
diagnostics (Bolstad et al., 2005) and discarding those samples that either of the
two reported diagnostics was considered below a minimum quality threshold. Third, using
the RMA algorithm (Irizarry et al., 2003) implemented in the rma() function from
the oligo package with argument target="core", expression values were
background corrected, normalized and summarized into Affymetrix transcript clusters.
Fourth, most samples formed part of family trios and only samples belonging to father
or mother were kept. Fifth, using the getNetAffx() function from the oligo
package, Ensembl Transcript identifiers well obtained for each Affymetrix transcript
cluster identifier. Sixth, using the bioconductor package biomaRt, Ensembl Transcript
identifiers were translated into Ensembl Gene identifiers, resolving multiple assignments
by keeping the Ensembl Gene identifier that had a match in the Ensembl Gene identifiers
forming the table of counts of the Pickrell et al. (2010) RNA-seq data, or choosing one
arbitrarily, otherwise. Seven, duplicated assignments of the same Ensembl Gene identifier
to multiple Affymetrix transcript cluster identifiers were resolved by keeping the
transcript cluster with largest expression variability measured by its interquartile range (IQR).
At this point an expression data matrix of 16,323 Ensembl Genes by 74 samples was obtained
and using the scanning date of each CEL file, samples were grouped into 4 balanced batches
stored in the phenotypic variable Batch within the resulting ExpressionSet.
Batch effect was removed by using the QR-decomposition method implemented in the
removeBatchEffect() function from the package limma while keeping the
sex-specific expression effect by setting the gender sample indicator variable within the
design matrix argument. Finally, samples were further filtered to match those from
the RNA-seq table of counts and this resulted into the gene expression data in
huangArrayRMAnoBatchCommonSamples.eset, while an additional filtering of genes to
match those from the RNA-seq table of counts resulted into the gene expression data in
huangArrayRMAnoBatchCommon.
The RNA-seq data was obtained by the pipeline described in Esnaola et al. (submitted) from
the raw reads deposited at http://eqtl.uchicago.edu/RNA_Seq_data/unmapped_reads.
The resulting table of counts is available in this data package as an ExpressionSet
object under the name pickrell1.eset and consists of 38,415 Ensembl Genes
by 69 samples. This table of counts was first filtered to remove genes with very low
expression levels by keeping only those with a minimum average of 0.1 counts per million.
Second, we further filtered this table of counts in order to match the samples
obtained after processing the LCL microarray data from Huang et al. (2007). At this point
we also generated a second gene expression data set by further filtering genes matching those
from the LCL microarray data from Huang et al. (2007). Third, we normalized both expression
data sets adjusting for gene length and G+C content using the Bioconductor
package cqn (Hansen et al., 2012). The corresponding gene length and G+C content
information was obtained from the data stored in the annotEnsembl63
data.frame object.
The resulting pair of sample-matching expression data matrices have 16,323 (microarray) and 27,438 (RNA-seq) genes by 36 samples, while the resulting pair of sample and gene-matching expression data matrices have 15,194 genes by 36 samples.
commonPickrell1Huang dataset
R.S. Huang, S. Duan, W.K. Bleibel, E.O. Kistner, W. Zhang, T.A. Clark, T.X. Chen, A.C. Schweitzer, J.E. Blume, N.J. Cox and M.E. Dolan, Proc. Natl. Acad. Sci. USA, 104(23):9758-9763, 2007.
J.K. Pickrell, J.C. Marioni, A.A Pai, J.F. Degner, B.E. Engelhardt, E. Nkadori, J.B Veyrieras, M. Stephens, Y. Gilad, and J.K. Pritchard, Nature, 464:768-772, 2010.
B.M. Bolstad, F. Collin, K. Brettschneider, L. Simpson, R.A. Irizarry, and T.P. Speed. Quality assessment of Affymetrix GeneChip data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor, pg. 33–48, Springer, 2005.
K.D. Hansen, R.A. Irizarry and Z. Wu. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics, 2012.
R.A. Irizarry, B. Hobbs, F. Collin, Y.D. Beazer-Barclay, K.J. Antonellis, U. Scherf and T.P. Speed. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2):249–64, 2003.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, J.R. Gonzalez. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments, submitted.
suppressMessages(library(Biobase)) data(commonPickrell1Huang) dim(huangArrayRMAnoBatchCommonSamples.eset) dim(pickrell1countsCQNcommonSamples.eset) table(huangArrayRMAnoBatchCommonSamples.eset$Gender) table(pickrell1countsCQNcommonSamples.eset$Gender) dim(huangArrayRMAnoBatchCommon.eset) dim(pickrell1countsCQNcommon.eset) table(huangArrayRMAnoBatchCommon.eset$Gender) table(pickrell1countsCQNcommon.eset$Gender)suppressMessages(library(Biobase)) data(commonPickrell1Huang) dim(huangArrayRMAnoBatchCommonSamples.eset) dim(pickrell1countsCQNcommonSamples.eset) table(huangArrayRMAnoBatchCommonSamples.eset$Gender) table(pickrell1countsCQNcommonSamples.eset$Gender) dim(huangArrayRMAnoBatchCommon.eset) dim(pickrell1countsCQNcommon.eset) table(huangArrayRMAnoBatchCommon.eset$Gender) table(pickrell1countsCQNcommon.eset$Gender)
Genes with documented sex-specific expression and occurring within the set of
genes that form the tables of counts in pickrell.eset,
montgomery.eset and cheung.eset.
data(genderGenes)data(genderGenes)
msYgenes: Ensembl gene identifiers from genes belonging to the
male-specific region of chromosome Y (Skaletsky et al., 2003).
XiEgenes: Ensembl gene identifiers from genes located in the X chromosome
and which have been reported to escape X-inactivation.
These two lists of genes form a gold-standard set of genes with documented sex-specific
expression which have been employed in the assessment of the method for differential
expression analysis implemented in the tweeDEseq package
(Esnaola et al., submitted). Both gene lists are restricted to genes occuring
within the set of genes that form the tables of counts in pickrell.eset,
montgomery.eset and cheung.eset.
genderGenes dataset
H.S. Skaletsky, T. Kuroda-Kawaguchi, P.J. Minx, H.S. Cordum, L. Hillier, L.G. Brown, S. Repping, T. Pyntikova, J. Ali, T. Bieri, A. Chinwalla, A. Delehaunty, K. Delehaunty, H. Du, G. Fewell, L. Fulton, T. Graves, S.F. Hou, P. Latrielle, S. Leonard, E. Mardis, R. Maupin, J. McPherson, T. Miner, W. Nash, C. Nguyen, P. Ozersky, K. Pepin, S. Rock, T. Rohlfing, K. Scott, B. Schultz, C. Strong, A. Tin-Wollam, S.P. Yang, R.H. Waterston, R.K. Wilson, S. Rozen, and D.C. Page. The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes. Nature, 423:825–837, 2003.
L. Carrel and H.F. Willard. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature, 434:400–404, 2005.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication. Submitted.
annotEnsembl63
pickrell
pickrellNorm
montgomery
hkGenes
data(genderGenes) length(msYgenes) length(XiEgenes)data(genderGenes) length(msYgenes) length(XiEgenes)
Housekeeping genes reported by Eisenberg and Levanon (2003) and occurring within
the set of genes that form the tables of counts in pickrell.eset,
montgomery.eset and cheung.eset in this experimental data
package.
data(hkGenes)data(hkGenes)
hkGenes: Ensembl gene identifiers from the list of housekeeping genes
occuring within the set of genes that form the tables of counts
in pickrell.eset, montgomery.eset
and cheung.eset in this experimental data
package.
This list of genes has been derived from mapping the original list in
http://www.cgen.com/supp_info/Housekeeping_genes.html to Ensemble
Gene identifiers using the org.Hs.eg.db package.
This list of housekeeping genes has been employed to compare count data distributions
from genes with different expression dynamics in (Esnaola et al., submitted)
and is restricted to genes occuring within the set of genes that form the tables
of counts in pickrell.eset, montgomery.eset and
cheung.eset in this experimental data package.
hkGenes dataset
E. Eisenberg, and E.Y. Levanon. Human housekeeping genes are compact. Trends Genet, 19(7):362–365, 2003.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, and J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication. Submitted.
annotEnsembl63
pickrell
pickrellNorm
montgomery
genderGenes
data(hkGenes) length(hkGenes) head(hkGenes)data(hkGenes) length(hkGenes) head(hkGenes)
ExpressionSet object containing RNA-seq count data from lymphoblastoid
cell lines from 60 unrelated Caucasian individuals of European descent. These
count data are employed in the vignette of the package tweeDEseq Esnaola
et al. (submitted). The original experimental data was published by Montgomery
et al. (2010) and the table of counts in this ExpressionSet object
corresponds to the one in the ReCount repository available at
http://bowtie-bio.sourceforge.net/recount. Details on the pre-processing
steps to obtain this table of counts from the raw reads of Montgomery et al.
(2010) are provided on that website and in the publication by Frazee et al. (2011).
data(montgomery)data(montgomery)
montgomery.eset: ExpressionSet object containing read counts
for 52,580 Ensembl genes for each of the 60 Caucasian
individuals of European descent.
The table of counts is stored in the AssayData slot of an
ExpressionSet object called montgomery.eset whose phenotypic
data contains the gender of each individual, among other bits of information.
montgomery dataset
S.B. Montgomery, M. Sammeth, M. Gutierrez-Arcelus, R.P. Lach, C. Ingle, J. Nisbett, R. Guigo and E.T. Dermitzakis. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature, 464:773-777, 2010.
A.C. Frazee, B. Langmead and J.T. Leek. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12:449, 2011.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication. Submitted.
annotEnsembl63
pickrell
cheung
genderGenes
hkGenes
suppressMessages(library(Biobase)) data(montgomery) montgomery.eset table(montgomery.eset$gender)suppressMessages(library(Biobase)) data(montgomery) montgomery.eset table(montgomery.eset$gender)
ExpressionSet objects containing RNA-seq count data from lymphoblastoid
cell lines from 69 unrelated Nigerian individuals. These count data are employed
in the vignette of the package tweeDEseq Esnaola et al. (submitted). The
original experimental data was published by Pickrell et al. (2010). The table
of counts in pickrell.eset corresponds to the one in the
ReCount repository available at http://bowtie-bio.sourceforge.net/recount.
Details on the pre-processing steps to obtain this table of counts from the raw
reads of Pickrell et al. (2010) are provided on that website and in the
publication by Frazee et al. (2011). The other object pickrellNorm.eset
contains the corresponding filtered and normalized table of counts.
The table of counts in pickrell1.eset was obtained by Esnaola et al. (2010)
by the pre-processing steps described on that article and pickrell1Norm.eset
contains the corresponding filtered and normalized table of counts.
data(pickrell)data(pickrell)
pickrell.eset: ExpressionSet object containing read counts
for 52,580 Ensembl genes for each of the 69 Nigerian
individuals.
pickrellNorm.eset: ExpressionSet object containing filtered
and normalized read counts for 10,231 Ensembl
genes for each of the 69 Nigerian individuals.
pickrell1.eset: ExpressionSet object containing read counts
for 38,415 Ensembl genes for each of the 69 Nigerian
individuals.
pickrell1Norm.eset: ExpressionSet object containing filtered
and normalized read counts for 22,060 Ensembl
genes for each of the 69 Nigerian individuals.
These tables of counts are stored in the AssayData slot of the previously
enumerated ExpressionSet objects whose phenotypic data contains the gender
of each individual, among other bits of information. The filtered and normalized
table of counts was obtained from the raw counts in pickrell.eset and
pickrell1.eset by first removing genes with less than 0.5 cpm (counts per
million reads) in all samples but one and then applying the conditional quantile
normalization procedure by Hansen et al. (2011).
pickrell dataset
J.K. Pickrell, J.C. Marioni, A.A Pai, J.F. Degner, B.E. Engelhardt, E. Nkadori, J.B Veyrieras, M. Stephens, Y. Gilad, and J.K. Pritchard Nature, 464:768-772, 2010.
A.C. Frazee, B. Langmead and J.T. Leek. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12:449, 2011.
M. Esnaola, P. Puig, D. Gonzalez, R. Castelo, J.R. Gonzalez. Gene-specific count data distributions are required in RNA-seq experiments with extensive replication. Submitted.
K.D. Hansen, R.A. Irizarry and Z. Wu. Removing technical variability in RNA-seq data using conditional quantile normalization. Johns Hopkins University, Dept. of Biostatistics Working Papers, Paper 227. (http://www.bepress/com/jhubiostat/paper227).
annotEnsembl63
montgomery
cheung
genderGenes
hkGenes
suppressMessages(library(Biobase)) data(pickrell) pickrell.eset table(pickrell.eset$gender) data(pickrellNorm) pickrellNorm.esetsuppressMessages(library(Biobase)) data(pickrell) pickrell.eset table(pickrell.eset$gender) data(pickrellNorm) pickrellNorm.eset