Description Usage Arguments Details Value
View source: R/CLLPDestimate.R
CLLPDestimate
returns the estimated CLL-PD value in the user-specified cohort.
1 2 3 4 5 6 7 | CLLPDestimate(
exprMatrix,
identifier = "ensembl_gene_id",
topVariant = NULL,
normalize = TRUE,
repeats = 20
)
|
exprMatrix |
A numeric matrix that contains the expression level of genes or probes for samples in a cohort. Rownames are gene identifiers and column names are sample identifiers. |
identifier |
A charachater variable that specifies the type of gene identifier. Currently only "ensembl_gene_id" and "gene_symbol" are allowed. |
topVariant |
If specified, it should be a numeric value indicating the number of most variant features (genes or probes) in the user-specificed data. The default value is NULL, which means all rows in the input matrix will be used. |
normalize |
A boolean value indicating wether the user-specified expression matrix should be centered by mean and scaled by standard deviation. The default value is TRUE. |
repeats |
A numeric variable specifying the number of repeats for cross-validation to select prediction model. The default value is 20. |
This function takes an gene expression dataset (RNAseq or microarray) of a external CLL cohort (user-specified), build a regularized linear model using the expression values of overlapped features in the built-in training cohort and use the selected model to esimate CLL-PD in the external cohort.
A list containing three objects 1) estimated_CLLPD: A numeric vector of the estimated CLL-PD values in the user-specified cohort; 2) A dataframe of the features with non-zero coefficients and their coefficients in the selected model (model with highest R2 value); 3) A numeric vector of variance explained (R2) values for CLL-PD of the built-in cohort along the repeated cross-validation runs.
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