progeny {progeny} | R Documentation |
This function uses the linear model of pathway-responsive genes underlying the PROGENy method. It transforms a gene expression matrix with HGNC gene symbols in rows and sample names in columns into a pathway score matrix with samples and in rows and pathways in columns.
This function uses the linear model of pathway-responsive genes underlying the PROGENy method. It transforms a gene expression matrix with HGNC gene symbols in rows and sample names in columns into a pathway score matrix with samples and in rows and pathways in columns.
progeny(expr, scale = TRUE)
expr |
A gene expression object with HGNC symbols in rows and samples in columns |
scale |
Logical value indicating whether to scale the scores of each pathway to have a mean of zero and standard deviation of one |
The publication of the method is available at: https://www.biorxiv.org/content/early/2016/08/28/065672
The supplied expression object has to contain HGNC symbols in rows. This will, in most cases (and how we originally used it), be either normalized gene expression of a microarray experiment or log-transformed (and possible variance-stabilized) counts from an RNA-seq experiment.
The model matrix itself consists of 11 pathways and 1059 genes. Its coefficients are non-zero if the gene-pathway pair corresponds to the top 100 genes that were up-regulated upon stimulation of the pathway in a wide range of experiments. The value corresponds to the fitted z-score across experiments in our model fit. Only rows with at least one non-zero coefficient were included, as the rest is not used to infer pathway activity.
A matrix with samples in columns and pathways in rows
# use your gene expression matrix here, this is just for illustration gene_expression = matrix(rep(1, nrow(model)), dimnames=list(rownames(model), "sample")) # calculate pathway activities pathways = progeny(gene_expression)