GetISGL | R Documentation |
GetISGL
fits a GLM model with Iterative Sparse Group Lasso (ISGL) penalization
for all the genes in the data set to identify the experimental variables and potential
regulators that show a relevant effect on the expression of each gene.GetISGL
fits a GLM model with Iterative Sparse Group Lasso (ISGL) penalization
for all the genes in the data set to identify the experimental variables and potential
regulators that show a relevant effect on the expression of each gene.
GetISGL(
GeneExpression,
data.omics,
associations = NULL,
omic.type = 0,
edesign = NULL,
clinic = NULL,
clinic.type = NULL,
center = TRUE,
scale = TRUE,
interactions.reg = TRUE,
min.variation = 0,
gr.method = "cor",
thres = 0.7
)
GeneExpression |
Data frame containing gene expression data with genes in rows and experimental samples in columns. Row names must be the gene IDs. |
data.omics |
List where each element corresponds to a different omic data type to be considered (miRNAs, transcription factors, methylation, etc.). The names of the list will represent the omics, and each element in the list should be a data matrix with omic regulators in rows and samples in columns. |
associations |
List where each element corresponds to a different omic data type (miRNAs,
transcription factors, methylation, etc.). The names of the list will represent the omics. Each element in
the list should be a data frame with 2 columns (optionally 3), describing the potential interactions between genes
and regulators for that omic. First column must contain the genes (or features in
GeneExpression object), second column must contain the regulators, and an optional third column can
be added to describe the type of interaction (e.g., for methylation, if a CpG site is located in
the promoter region of the gene, in the first exon, etc.). If the user lacks prior knowledge of the potential regulators, they can set the parameter to NULL.
In this case, all regulators in |
edesign |
Data frame describing the experimental design. Rows must be the samples (columns
in |
clinic |
Data.frame with all clinical variables to consider,with samples in rows and variables in columns. |
clinic.type |
Vector which indicates the type of data of variables introduced in |
center |
By default TRUE. It determines whether centering is applied to |
scale |
By default TRUE. It determines whether scaling is applied to |
interactions.reg |
If TRUE, the model includes interactions between regulators and experimental variables. By default, TRUE. |
min.variation |
For numerical regulators, it specifies the minimum change required across conditions to retain the regulator in the regression models. In the case of binary regulators, if the proportion of the most common value is equal to or inferior this value, the regulator is considered to have low variation and will be excluded from the regression models. The user has the option to set a single value to apply the same filter to all omics, provide a vector of the same length as omics if they want to specify different levels for each omics, or use 'NA' when they want to apply a minimum variation filter but are uncertain about the threshold. By default, 0. |
gr.method |
Methodology to apply to create the gorups. By default, cor. |
thres |
Threshold for the correlation when using gr.method ='cor' or threshold for the percentage of variability to explain when gr.method ='pca'. By default, 0.7. |
List containing the following elements:
ResultsPerGene : List with as many elements as genes in GeneExpression
. For each gene, it includes information about gene values, considered variables, estimated coefficients,
detailed information about all regulators, and regulators identified as relevant (in glm scenario) or significant (in pls scenarios).
GlobalSummary : List with information about the fitted models, including model metrics, information about regulators, genes without models, regulators, master regulators and hub genes.
Arguments : List containing all the arguments used to generate the models.
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