| Title: | Data employed in the vignette of the GSVA package |
|---|---|
| Description: | This package stores the data employed in the vignette of the GSVA package. These data belong to the following publications: Cahoy et al. J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021. |
| Authors: | Robert Castelo <[email protected]> |
| Maintainer: | Robert Castelo <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.49.2 |
| Built: | 2026-10-10 10:30:44 UTC |
| Source: | https://github.com/bioc/GSVAdata |
This package contains data employed in the vignette of the GSVA package.
geneprotExpCostaEtAl2021 Bulk RNA-seq data by Costa et al. (2021).
c2BroadSets C2 canonical pathways from the MSigDB 3.0
database of gene sets at the Broad Institute (Subramanian et al. 2005;
Liberzon et al., 2011).
gbm_VerhaakEtAl TCGA Glioblastoma Multiforme microarray
expression data from Verhaak et al. (2010).
brainTxDbSets Gene sets signatures specific to four
different brain cell types derived from murine models (Cahoy et al., 2008).
commonPickrellHuang Matching microarray and RNA-seq data
from human lymphoblastoid cell lines (Huang et al., 2007; Pickrell et al., 2010).
annotEntrez220212 Annotation data on gene length and G+C
content from NCBI at http://www.ncbi.nlm.nih.gov.
genderGenesEntrez Entrez genes with documented sex-specific
expression (Skaletsky et al., 2003; Carrel and Willard, 2005).
S. Haenzelmann, J. Guinney and R. Castelo
J.D. Cahoy, B. Emery, A. Kaushal, L.C. Foo, J.L. Zamanian, et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci, 28:264-278, 2008.
L. Carrel and H.F. Willard. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature, 434:400–404, 2005.
D. Costa, N. Bonet, A. Sole, J.M. Gonzalez de Aledo-Castillo, E. Sabido, F. Casals, C. Rovira, A. Nadal, J.L. Marin, T. Cobo and R. Castelo. Genome-wide postnatal changes in immunity following fetal inflammatory response. FEBS Journal, 288:2311-2331, 2021. https://doi.org/10.1111/febs.15578.
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.
A. Liberzon, A. Subramanian, R. Pinchback, H. Thorvaldsdottir, P. Tamayo, J.P. Mesirov. Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27(12):1739-40, 2011.
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.
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.
A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, J.P. Mesirov. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102(43):15545-50, 2005.
R.G.W. Verhaak, K.A. Hoadley, E. Purdom, V. Wang, Y. Qi, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 17:98-110, 2010.
Annotation data for human genes defined by Entrez identifiers and downloaded on 22/02/2012.
data(annotEntrez220212)data(annotEntrez220212)
Length: Length of the longest cDNA of this gene.
GCcontent: G+C content of the longest cDNA of this gene.
All human mRNAs were downloaded from NCBI on 22/02/12 by going first to the
taxonomy browser, select "Homo Sapiens", then select mRNAs, then follow the
link on "manage filters" and made sure that only mRNAs is checked. Then, we selected
FASTA format and sent it to a file. We also downloaded the NCBI Entrez Gene ID
to NCBI Accession mapping from
ftp://ftp.ncbi.nih.gov/gene/DATA/gene2accession.gz which we used to group
mRNA transcripts by Entrez Gene identifier. Finally, for each Entrez Gene identifier
we picked the longest mRNA and stored its length and G+C content in a data frame
called annotEntrez220212 whose row names indicate the corresponding Entrez
Gene identifier.
NCBI: http://www.ncbi.nlm.nih.gov
data(annotEntrez220212) dim(annotEntrez220212) head(annotEntrez220212)data(annotEntrez220212) dim(annotEntrez220212) head(annotEntrez220212)
Gene sets signatures specific to four different brain cell types (astrocytes, oligodendrocytes, neurons and cultured astroglial cells) derived from murine models (Cahoy et al. 2008).
data(brainTxDbSets)data(brainTxDbSets)
The data is contained in an list object called brainTxDbSets
obtained from the Brain Transcriptome Database (Cahoy et al., 2008).
Cahoy, J.D., Emery, B., Kaushal, A., Foo, L.C., Zamanian, J.L. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci, 28:264-278, 2008.
data(brainTxDbSets) head(lapply(brainTxDbSets, head))data(brainTxDbSets) head(lapply(brainTxDbSets, head))
C2 Broad Sets.
data(c2BroadSets)data(c2BroadSets)
The data is contained in an GeneSetCollection object called
c2BroadSets obtained by parsing the file c2.all.v3.0.entrez.gmt,
downloaded from http://www.broadinstitute.org/gsea, using the
getGmt() function from the GSEABase package.
Subramanian, Tamayo, et al. PNAS, 102:15545-15550, 2005.
Mootha, Lindgren, et al. Nat Genet, 34:267-273, 2003.
data(c2BroadSets) c2BroadSetsdata(c2BroadSets) c2BroadSets
ExpressionSet objects containing microarray and RNA-seq count data for
11,508 matching Entrez genes from 36 samples of lymphoblastoid cell lines derived
from unrelated Nigerian individuals. These microarray and count data are employed
in the vignette of the package GSVA Hanzelmann et al. (submitted). The
original experimental data was published by Huang et al. (2007) and Pickrell et al.
(2010).
data(commonPickrellHuang)data(commonPickrellHuang)
huangArrayRMAnoBatchCommon_eset: ExpressionSet object containing filtered,
normalized and batch-removed microarray expression values for 11,508 Entrez
genes from 36 unrelated Nigerian individuals.
pickrellCountsArgonneCQNcommon_eset: ExpressionSet object containing filtered
and normalized RNA-seq read counts for 11,508 Entrez
genes from 36 unrelated Nigerian individuals. This table of
counts corresponds to RNA-seq data produced at the Argonne
sequencing center (see Pickrell et al., 2010).
pickrellCountsYaleCQNcommon_eset: ExpressionSet object containing filtered
and normalized RNA-seq read counts for 11,508 Entrez
genes from 36 unrelated Nigerian individuals. This table of
counts corresponds to RNA-seq data produced at the Yale
sequencing center (see Pickrell et al., 2010).
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, most
samples formed part of family trios and only samples belonging to father or mother
were kept. Fourth, 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. Fifth, using the getNetAffx function from the oligo
package, Affymetrix transcript cluster identifiers were translated into Entrez Gene
identifiers resolving duplicated assignments by keeping the transcript cluster with
largest expression variability measured by its interquartile range (IQR).
At this point an expression data matrix of 17,324 Entrez
genes by 59 samples was obtained and using the scanning date of each CEL file samples
were grouped into 5 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 and genes were further filtered to match those from the RNA-seq tables of counts.
The RNA-seq data was obtained by directly downloading the tables of counts processed
by Pickrell et al. (2010) from http://eqtl.uchicago.edu/RNA_Seq_data/results,
which initially consisted of 41,466 Ensembl genes by 80 and 81 samples corresponding to
the RNA-seq data obtained at the Argonne and Yale sequencing centers, respectively.
Some of the samples (11 from Argonne and 12 from Yale) were prepared and sequenced
twice within each sequencing center. In these cases we kept the sample of deeper
coverage obtaining a final number of 69 samples on each table. We further filtered
genes with low expression by discarding those with a mean of less than 0.5 counts
per million calculated in log2 scale resulting in tables of counts with 17,607 genes
(Argonne) and 17,843 genes (Yale) by 69 samples and we kept those genes common to
both tables (17,324). Next, we normalized these two tables of counts 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 extracted from data
deposited at the same site from were the tables of counts were downloaded. We further
filtered these two normalized tables of counts in order to match the genes and samples
obtained after processing the LCL microarray data from Huang et al. (2007). This step
required first to translate Ensembl gene identifiers into Entrez gene identifiers and
second to match gene and sample identifiers between microarray and RNA-seq data. After
these two steps we obtained the two final tables of counts of 11,508 Entrez genes by
36 samples included in this package.
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.
S. Haenzelmann, J. Guinney and R. Castelo. GSVA: Gene Set Variation Analysis for microarray and RNA-Seq data, submitted.
suppressMessages(library(Biobase)) data(commonPickrellHuang) dim(huangArrayRMAnoBatchCommon_eset) dim(pickrellCountsArgonneCQNcommon_eset) dim(pickrellCountsYaleCQNcommon_eset) table(huangArrayRMAnoBatchCommon_eset$Gender) table(pickrellCountsArgonneCQNcommon_eset$Gender) table(pickrellCountsYaleCQNcommon_eset$Gender) stopifnot(identical(featureNames(huangArrayRMAnoBatchCommon_eset), featureNames(pickrellCountsArgonneCQNcommon_eset))) stopifnot(identical(sampleNames(huangArrayRMAnoBatchCommon_eset), sampleNames(pickrellCountsArgonneCQNcommon_eset)))suppressMessages(library(Biobase)) data(commonPickrellHuang) dim(huangArrayRMAnoBatchCommon_eset) dim(pickrellCountsArgonneCQNcommon_eset) dim(pickrellCountsYaleCQNcommon_eset) table(huangArrayRMAnoBatchCommon_eset$Gender) table(pickrellCountsArgonneCQNcommon_eset$Gender) table(pickrellCountsYaleCQNcommon_eset$Gender) stopifnot(identical(featureNames(huangArrayRMAnoBatchCommon_eset), featureNames(pickrellCountsArgonneCQNcommon_eset))) stopifnot(identical(sampleNames(huangArrayRMAnoBatchCommon_eset), sampleNames(pickrellCountsArgonneCQNcommon_eset)))
Microarray data from Glioblastoma multiforme (GBM) downloaded from
the TCGA website (http://cancergenome.nih.gov). The data
is provided as an ExpressionSet object containing RMA-processed
expression values.
data(gbm_VerhaakEtAl)data(gbm_VerhaakEtAl)
The data is contained in an ExpressionSet object called
gbm_eset and was obtained using RMA (Irizarry et al. 2003).
Verhaak, R.G.W., Hoadley, K.A., Purdom, E., Wang, V., Qi, Y., et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 17:98-110, 2010.
Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U., and Speed, T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2):249–64, 2003.
data(gbm_VerhaakEtAl) gbm_eset head(pData(gbm_eset)) table(gbm_eset$subtype)data(gbm_VerhaakEtAl) gbm_eset head(pData(gbm_eset)) table(gbm_eset$subtype)
Entrez genes with documented sex-specific expression.
data(genderGenesEntrez)data(genderGenesEntrez)
msYgenesEntrez: Entrez gene identifiers from genes belonging to the
male-specific region of chromosome Y (Skaletsky et al., 2003).
XiEgenesEntrez: Entrez gene identifiers from genes located in the X chromosome
and which have been reported to escape X-inactivation
(Carrel and Willard, 2005).
These are two vectors of Entrez gene identifiers corresponding to genes with sex-specific expression documented by Skaletsky et al. (2003) and Carrel and Willard (2005).
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.
data(genderGenesEntrez) length(msYgenesEntrez) length(XiEgenesEntrez)data(genderGenesEntrez) length(msYgenesEntrez) length(XiEgenesEntrez)
Gene and protein expression data published by Costa et al. (2021). The gene expression data consists of normalized log-CPM units of expression derived from bulk RNA sequencing (RNA-seq) of total RNA from archived neonatal dried blood spots (DBS) specimens from 21 extremely low gestational age newborns (ELGANs, < 28 weeks of gestation). The DBS specimens were obtained from 8 females and 13 males, among which 10 where exposed to a fetal inflammatory response (FIR) before birth, and 11 were not exposed. The protein expresion data was obtained from the same specimens, except for a missing one, using a mass-spectrometry assay, and the included data corresponds to the VSN normalized data before and after imputation.
data(geneprotExpCostaEtAl2021)data(geneprotExpCostaEtAl2021)
The RNA-seq data is contained in a SummarizedExperiment object
called geneExpCostaEtAl2021 obtained as follows:
Raw 2x75nt paired-end reads in FASTQ files were aligned to the
GRCh38 version of the reference human genome, without alternate
locus scaffolds (GCA_000001405.15) and including human decoy
sequences from hs38d1 (GCA_000786075.2), using STAR version 2.6.0c
(Dobin et al., 2013) with default parameters, except for
--peOverlapNbasesMin 10 and --sjdbOverhang 74.
Aligned reads in BAM files were reduced to a table of counts of
25,221 genes by 84 samples using gene annotations from Gencode v24
and the R/Bioconductor package GenomeAlignments version 1.16.0, and
its function summarizeOverlaps. We used specific arguments in
the call to this function to restrict the count of genic reads to
only those that fell entirely within the exonic regions and aligned
to a unique site on the genome, to reflect library preparation
protocols, and to avoid counting reads without a matching pair or
overlapping multiple features.
Lowly-expressed genes were filtered out by discarding those that did not show a minimum reliable level of expression of 10 counts per million reads of the smallest library size, in at least 6 samples. This filtering step led to a final table of counts of 11,279 genes by 21 samples.
Normalized log-CPM units of expression were obtained by using the
edgeR package (Robinson et al., 2010) as follows. First, we
calculated library factors using the TMM algorithm implemented in
the function calcNormFactors(), and second we used the
function cpm() with default parameters, except for
log=TRUE.
The protein expression data is contained in a SummarizedExperiment object
called protExpCostaEtAl2021. You may consult the publication for further
details on how these data were pre-processed.
D. Costa, N. Bonet, A. Sole, J.M. Gonzalez de Aledo-Castillo, E. Sabido, F. Casals, C. Rovira, A. Nadal, J.L. Marin, T. Cobo and R. Castelo. Genome-wide postnatal changes in immunity following fetal inflammatory response. FEBS Journal, 288:2311-2331, 2021. https://doi.org/10.1111/febs.15578.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, and Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29:15–21, 2013.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26:139–140, 2010.
data(geneprotExpCostaEtAl2021) geneExpCostaEtAl2021 protExpCostaEtAl2021data(geneprotExpCostaEtAl2021) geneExpCostaEtAl2021 protExpCostaEtAl2021
A subset of the Visium spatial 10X Genomics spatial gene expression data for the human cerebellum, after quality control filtering and normalization. The original data was downloaded using the function ['HumanCerebellum'][TENxVisiumData::HumanCerebellum] from the Bioconductor package ['TENxVisiumData'][TENxVisiumData::TENxVisiumData]. The script 'inst/scripts/prepare_HumanCerebellumSubset.R' was used to perform the quality control filtering and normalization, and to save the resulting subsetted data in different files in the 'inst/extdata' directory.
HumanCerebellumNormSubset()HumanCerebellumNormSubset()
A 'SpatialExperiment' object containing the subsetted normalized human cerebellum spatial transcriptomics data.
spe <- HumanCerebellumNormSubset() spespe <- HumanCerebellumNormSubset() spe