GetGLM: Generalized Linear Models
In ConesaLab/MORE: Multi-Omics REgulation (MORE)

GetGLM

R Documentation

Generalized Linear Models

Description

GetGLM fits a regression model for all the genes in the data set to identify the experimental variables and potential regulators that show a significant effect on the expression of each gene.

Usage

GetGLM(
  GeneExpression,
  data.omics,
  associations = NULL,
  omic.type = 0,
  edesign = NULL,
  clinic = NULL,
  clinic.type = NULL,
  center = TRUE,
  scale = TRUE,
  epsilon = 1e-05,
  family = gaussian(),
  elasticnet = NULL,
  interactions.reg = TRUE,
  min.variation = 0,
  col.filter = "cor",
  correlation = 0.7,
  scaletype = "auto"
)

Arguments

`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 `data.omics` will be treated as potential regulators for all genes. In this case, for computational efficiency, it is recommended to use pls2 `method`. Additionally, if the users have prior knowledge for certain omics and want to set other omics to NULL, they can do so.
`edesign`	Data frame describing the experimental design. Rows must be the samples (columns in `GeneExpression`) and columns must be the experimental variables to be included in the model (e.g. treatment, etc.).
`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 `clinic`. The user should code as 0 numeric variables and as 1 categorical or binary variables. By default is set to NULL. In this case, the data type will be predicted automatically. However, the user must verify the prediction and manually input the vector if incorrect.
`center`	By default TRUE. It determines whether centering is applied to `data.omics`.
`scale`	By default TRUE. It determines whether scaling is applied to `data.omics`.
`epsilon`	Convergence threshold for coordinate descent algorithm in elasticnet. Default value, 1e-5.
`family`	Error distribution and link function to be used in the model when `method` glm. By default, gaussian().
`elasticnet`	ElasticNet mixing parameter. There are three options: NULL : The parameter is selected from a grid of values ranging from 0 to 1 with 0.1 increments. The chosen value optimizes the mean cross-validated error when optimizing the lambda values. A number between 0 and 1 : ElasticNet is applied with this number being the combination between Ridge and Lasso penalization (elasticnet=0 is the ridge penalty, elasticnet=1 is the lasso penalty). A vector with the mixing parameters to try. The one that optimizes the mean cross-validated error when optimizing the lambda values will be used. #' By default, NULL.
`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.
`col.filter`	Type of correlation coefficients to use when applying the multicollinearity filter when glm `method` is used. cor: Computes the correlation between omics. Pearson correlation between numeric variables, phi coefficient between numeric and binary and biserial correlation between binary variables. pcor : Computes the partial correlation.
`correlation`	Value to determine the presence of collinearity between two regulators when using the glm `method`. By default, 0.7.
`scaletype`	Type of scaling to be applied. Three options: auto : Applies the autoscaling. pareto : Applies the pareto scaling. `\frac{X_k}{s_k \sqrt[4]{m_b}}` block : Applies the block scaling. `\frac{X_k}{s_k \sqrt{m_b}}` considering m_b the number of variables of the block. By default, auto.
`alfa`	Significance level for variable selection in pls1and pls2 `method`. By default, 0.05.

Value

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.