stemcells: Human Stem Cells Data
In mixOmicsTeam/mixOmics: Omics Data Integration Project
stemcells R Documentation
Human Stem Cells Data
Description
This data set contains the expression of a random subset of 400 genes in 125
samples from 4 independent studies and 3 cell types.
Usage
data(stemcells)
Format
A list containing the following components:
- list("gene")
data matrix with 125 rows and 400 columns. Each row
represents an experimental sample, and each column a single gene.
- list("celltype")
a factor indicating the cell type of each sample.
- list("study")
a factor indicating the study from which the sample was
extracted.
Details
This data set contains the expression of a random subset of 400 genes in 125
samples from 4 independent studies and 3 cell types. Those studies can be
combined and analysed using the MINT procedure.
Value
none
References
Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017).
MINT: A multivariate integrative approach to identify a reproducible
biomarker signature across multiple experiments and platforms. BMC
Bioinformatics 18:128.
mixOmicsTeam/mixOmics documentation built on Dec. 3, 2024, 11:15 p.m.
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| stemcells | R Documentation |
Human Stem Cells Data
Description
This data set contains the expression of a random subset of 400 genes in 125 samples from 4 independent studies and 3 cell types.
Usage
data(stemcells)
Format
A list containing the following components:
- list("gene")
data matrix with 125 rows and 400 columns. Each row represents an experimental sample, and each column a single gene.
- list("celltype")
a factor indicating the cell type of each sample.
- list("study")
a factor indicating the study from which the sample was extracted.
Details
This data set contains the expression of a random subset of 400 genes in 125 samples from 4 independent studies and 3 cell types. Those studies can be combined and analysed using the MINT procedure.
Value
none
References
Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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