| Title: | Supporting datasets for SBGNview package |
|---|---|
| Description: | This package contains: 1. A microarray gene expression dataset from a human breast cancer study. 2. A RNA-Seq gene expression dataset from a mouse study on IFNG knockout. 3. ID mapping tables between gene IDs and SBGN-ML file glyph IDs. 4. Percent of orthologs detected in other species of the genes in a pathway. Cutoffs of this percentage for defining if a pathway exists in another species. 5. XML text of SBGN-ML files for all pre-collected pathways. |
| Authors: | Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo |
| Maintainer: | Weijun Luo <[email protected]> |
| License: | AGPL-3 |
| Version: | 1.26.0 |
| Built: | 2026-10-10 11:21:01 UTC |
| Source: | https://github.com/bioc/SBGNview.data |
A demo microarray dataset from a cancer study
data(cancer.ds)data(cancer.ds)
A SummarizedExperiment object.
This dataset is constructed using the first three columns of data **gse16873.d** in package **pathview** (i.e. columns "DCIS_1","DCIS_2" and "DCIS_3"). the original values were used without additional processing. It is constructed for showing SBGNview's visualization ability, not for data analysis. Each column in the assay table is a pair of cancer-v.s.-control samples. The value of a gene in a column is the log fold change of this gene in the corresponding pair of cancer-v.s.-control samples.
RNA-Seq result from a mouse IFNG knockout experiment
data(IFNg)data(IFNg)
A SummarizedExperiment object.
This RNA-Seq dataset contains RNA abundance table of two groups: IFNG knockout mice and wild type mice. RNA abundance values were log2 transformed. For demo purpose, data of 4 IFNG knockout mice and 4 wild type mice were included. The experiment and data processing was described in this work: Greer, Renee L., Xiaoxi Dong, et al. "Akkermansia muciniphila mediates negative effects of IFNG on glucose metabolism." Nature communications 7 (2016): 13329.
Mapping table between two types of IDs
data(ENZYME_pathway.id) data(hsa_KO_ENTREZID) data(mmu_KO_ENSEMBL) data(chebi_pathway.id) data(mmu_KO_ENTREZID) data(chebi_compound.name) data(compound.name_pathwayCommons) data(kegg_pathwayCommons) data(hsa_pathwayCommons_ENSEMBL) data(mmu_pathwayCommons_ENTREZID) data(KO_pathway.id) data(KO_pathwayCommons) data(SYMBOL_pathway.id) data(pathwayCommons_SYMBOL) data(chebi_pathwayCommons) data(mmu_pathwayCommons_ENSEMBL) data(hsa_ENTREZID_pathwayCommons) data(chebi_kegg) data(chebi_metacyc.SBGN) data(compound.name_pathway.id) data(compound.name_kegg)data(ENZYME_pathway.id) data(hsa_KO_ENTREZID) data(mmu_KO_ENSEMBL) data(chebi_pathway.id) data(mmu_KO_ENTREZID) data(chebi_compound.name) data(compound.name_pathwayCommons) data(kegg_pathwayCommons) data(hsa_pathwayCommons_ENSEMBL) data(mmu_pathwayCommons_ENTREZID) data(KO_pathway.id) data(KO_pathwayCommons) data(SYMBOL_pathway.id) data(pathwayCommons_SYMBOL) data(chebi_pathwayCommons) data(mmu_pathwayCommons_ENSEMBL) data(hsa_ENTREZID_pathwayCommons) data(chebi_kegg) data(chebi_metacyc.SBGN) data(compound.name_pathway.id) data(compound.name_kegg)
A matrix/data.frame with two columns: the ID mapping between two types of IDs.
An object of class data.frame with 16601 rows and 2 columns.
An object of class data.frame with 10446 rows and 3 columns.
An object of class matrix (inherits from array) with 11016 rows and 2 columns.
An object of class data.frame with 79911 rows and 2 columns.
An object of class data.frame with 11105 rows and 3 columns.
An object of class data.frame with 285207 rows and 2 columns.
An object of class data.frame with 298727 rows and 2 columns.
An object of class data.frame with 6448 rows and 2 columns.
An object of class matrix (inherits from array) with 383983 rows and 2 columns.
An object of class matrix (inherits from array) with 628413 rows and 2 columns.
An object of class data.frame with 47719 rows and 2 columns.
An object of class data.frame with 88148 rows and 2 columns.
An object of class data.frame with 252730 rows and 2 columns.
An object of class data.frame with 406940 rows and 3 columns.
An object of class matrix (inherits from array) with 60425 rows and 2 columns.
An object of class matrix (inherits from array) with 619656 rows and 2 columns.
An object of class matrix (inherits from array) with 384671 rows and 2 columns.
An object of class data.frame with 20692 rows and 2 columns.
An object of class matrix (inherits from array) with 60659 rows and 2 columns.
An object of class data.frame with 571283 rows and 2 columns.
An object of class data.frame with 61143 rows and 2 columns.
Each dataset contains a mapping table. There are several types of ID pairs, such as molecule ID <=> pathway_glyph_ID, molecule ID <=> pathway ID, and molecule ID <=> KEGG ortholog ID. molecule ID <=> pathway_glyph_ID tables are extracted from Biopax files. For example: http://www.pathwaycommons.org/archives/PC3/v10/PathwayCommons10.reactome.BIOPAX.owl.gz. Glyph IDs are extracted from the ID of each XML element "Protein". Its matching molecule ID is extracted from the corresponding XML child element "UnificationXref". See more details and examples in vignette 'SBGNview.data.vignette'
Cutoffs of pathway completeness used for defining existance of pathway in a species
data(pathway.completeness.cutoff.info)data(pathway.completeness.cutoff.info)
A matrix
PathwayCommons only annotated human pathways, we mapped pathwayCommons' genes to other species using KEGG ortholog annotation. As a result, not all of the genes have corresponding genes in another species. We call the percentage of mapped genes the "coverage or completeness" in the species. To determin if a pathway exists in a species, we use a cutoff for this completeness. This cutoff is selected using the following approach: 1. A pathway has different completeness in different species thus form a completeness vector across all species (vector C) . 2. Use a completeness cutoff we can define whether this pathway "exists" in a species, thus form a label vector E (a pathway "Exist" or "not Exist" across all species). 3. Use one way ANOVA to calculate F statistic of completeness between the two groups ("Exist" or "not Exist"), thus one cutoff will have one F statistic. 4. Try different cutoffs(unique completeness values in vector C) and select the one with the largest F statistic, i.e. the cutoff the can maximize the difference between Exist" and "not Exist" groups. This is not a perfect way to define if a pathway exists in a species, but can serve as a filter criteria.
Pathway completeness in a species
data(pathway.species.pct_Mapped)data(pathway.species.pct_Mapped)
A matrix
PathwayCommons only annotated human pathways, we mapped pathwayCommons' genes to other species using KEGG ortholog annotation. As a result, not all of the genes have corresponding genes in another species. We call the percentage of mapped genes the "coverage or completeness" in the species.
XML code of a SBGN-ML file
data(sbgn.xmls)data(sbgn.xmls)
A list of character strings
Each string is the full XML code of a SBGN-ML file. It includes glyphs and arcs of a SBGN map. ***Note: Please note that sbgn.xmls is a large R object and will take a few seconds to load. It is necessary to be loaded into the environment in order to access the pre-generated SBGN-ML files.