| Title: | Data sets for FABIA (Factor Analysis for Bicluster Acquisition) |
|---|---|
| Description: | Supplying gene expression data sets for the demos of the biclustering method "Factor Analysis for Bicluster Acquisition" (FABIA). The following three data sets are provided: A) breast cancer (van't Veer, Nature, 2002), B) multiple tissues (Su, PNAS, 2002), and C) diffuse large-B-cell lymphoma (Rosenwald, N Engl J Med, 2002). |
| Authors: | Sepp Hochreiter <[email protected]> |
| Maintainer: | Sepp Hochreiter <[email protected]> |
| License: | LGPL (>= 2.1) |
| Version: | 1.50.0 |
| Built: | 2026-10-15 11:32:21 UTC |
| Source: | https://github.com/bioc/fabiaData |
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by van't Veer et al. 2002. Array S54 was removed because it is an outlier.
Goal was to find a gene signature to predict the outcome of a cancer therapy that is to predict whether metastasis will occur. A 70 gene signature has been discovered.
Here we want to find subclasses in the data set.
Hoshida et al. 2007 found 3 subclasses and verified that 50/61 cases from class 1 and 2 were ER positive and only in 3/36 from class 3.
bA is the data set with
97 samples and 1213 genes,
bAc1 are the
three subclasses from Hoshida et al. 2007.
Breast_ABreast_A
Matrix XBreast: 97 samples and 1213 probe sets,
Vector CBreast:
three subclasses from Hoshida
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al. ‘Gene expression profiling predicts clinical outcome of breast cancer’, Nature 415:530-536, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by van't Veer et al. 2002. Array S54 was removed because it is an outlier.
Goal was to find a gene signature to predict the outcome of a cancer therapy that is to predict whether metastasis will occur. A 70 gene signature has been discovered.
Here we want to find subclasses in the data set.
Hoshida et al. 2007 found 3 subclasses and verified that 50/61 cases from class 1 and 2 were ER positive and only in 3/36 from class 3.
XBreast is the data set with
97 samples and 1213 genes,
CBreast are the
three subclasses from Hoshida et al. 2007.
CBreastCBreast
Vector CBreast of 97 samples giving the
three subclasses from Hoshida.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al. ‘Gene expression profiling predicts clinical outcome of breast cancer’, Nature 415:530-536, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by Rosenwald et al. 2002.
Goal was to predict the survival after chemotherapy
Hoshida divided the data set into three classes: “OxPhos” (oxidative phosphorylation), “BCR” (B-cell response), and “HR” (host response).
We want to identify these subclasses.
CDLBCLCDLBCL
Vector CDLBCL of 180 samples giving the
three subclasses according to Hoshida et al. 2007.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, et al. ‘The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma’, New Engl. J. Med. 346: 1937-1947, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by Su et al. 2002.
Gene expression from human and mouse samples across a diverse array of tissues, organs, and cell lines have been profiled. The goal was to have a reference for the normal mammalian transcriptome.
Here we want to identify the subclasses which correspond to the tissue types.
CMultiCMulti
Vector CMulti of 102 samples giving the
four classes of tissue types.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, et al. ‘Large-scale analysis of the human and mouse transcriptomes’, Proc. Natl. Acad. Sci. USA 99:4465-4470, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by Rosenwald et al. 2002.
Goal was to predict the survival after chemotherapy.
Hoshida divided the data set into three classes: “OxPhos” (oxidative phosphorylation), “BCR” (B-cell response), and “HR” (host response).
We want to identify these subclasses.
DLBCL_BDLBCL_B
Matrix XDLBCL: 180 samples and 661 probe sets,
Vector CDLBCL:
three subclasses according to Hoshida et al. 2007.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, et al. ‘The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma’, New Engl. J. Med. 346: 1937-1947, 2002.
fabiaData available data sets.
fabiaData()fabiaData()
Sepp Hochreiter
fabiaData()fabiaData()
fabiaDataVersion displays version information about the
package.
fabiaDataVersion()fabiaDataVersion()
Sepp Hochreiter
fabiaDataVersion()fabiaDataVersion()
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by Su et al. 2002.
Gene expression from human and mouse samples across a diverse array of tissues, organs, and cell lines have been profiled. The goal was to have a reference for the normal mammalian transcriptome.
Here we want to identify the subclasses which correspond to the tissue types.
Multi_AMulti_A
Matrix XMulti: 102 samples and 5565 probe sets,
Vector CMulti:
four classes of tissue types.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, et al. ‘Large-scale analysis of the human and mouse transcriptomes’, Proc. Natl. Acad. Sci. USA 99:4465-4470, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by van't Veer et al. 2002. Array S54 was removed because it is an outlier.
Goal was to find a gene signature to predict the outcome of a cancer therapy that is to predict whether metastasis will occur. A 70 gene signature has been discovered.
Here we want to find subclasses in the data set.
Hoshida et al. 2007 found 3 subclasses and verified that 50/61 cases from class 1 and 2 were ER positive and only in 3/36 from class 3.
XBreast is the data set with
97 samples and 1213 genes,
CBreast are the
three subclasses from Hoshida et al. 2007.
XBreastXBreast
Matrix XBreast: 97 samples and 1213 probe sets.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al. ‘Gene expression profiling predicts clinical outcome of breast cancer’, Nature 415:530-536, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by Rosenwald et al. 2002.
Goal was to predict the survival after chemotherapy
Hoshida divided the data set into three classes: “OxPhos” (oxidative phosphorylation), “BCR” (B-cell response), and “HR” (host response).
We want to identify these subclasses.
XDLBCLXDLBCL
Matrix XDLBCL: 180 samples and 661 probe sets.
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, et al. ‘The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma’, New Engl. J. Med. 346: 1937-1947, 2002.
Microarray data from Broad Institute “Cancer Program Data Sets” which was produced by Su et al. 2002.
Gene expression from human and mouse samples across a diverse array of tissues, organs, and cell lines have been profiled. The goal was to have a reference for the normal mammalian transcriptome.
Here we want to identify the subclasses which correspond to the tissue types.
XMultiXMulti
Matrix XMulti: 102 samples and 5565 probe sets
Broad Institute “Cancer Program Data Sets”: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
Hoshida Y, Brunet J-P, Tamayo P, Golub TR, Mesirov JP, ‘Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets’, PLoS ONE 2(11): e1195, 2007.
Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, et al. ‘Large-scale analysis of the human and mouse transcriptomes’, Proc. Natl. Acad. Sci. USA 99:4465-4470, 2002.