,
Christian H. Holland [ctb] ,
Igor Bulanov [ctb],
Aurélien Dugourd [cre, ctb]| Title: | Pathway RespOnsive GENes for activity inference from gene expression |
|---|---|
| Description: | PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. |
| Authors: | Michael Schubert [aut],
Alberto Valdeolivas [ctb] ,
Christian H. Holland [ctb] ,
Igor Bulanov [ctb],
Aurélien Dugourd [cre, ctb] |
| Maintainer: | Aurélien Dugourd <[email protected]> |
| License: | Apache License (== 2.0) | file LICENSE |
| Version: | 1.29.0 |
| Built: | 2024-11-08 06:01:49 UTC |
| Source: | https://github.com/bioc/progeny |
This function is designed for getting a model matrix with top significant genes for each pathway
getModel(organism = "Human", top = 100, decoupleR = F)getModel(organism = "Human", top = 100, decoupleR = F)
organism |
"Human" or "Mouse" taken from the main function's argument. Default to "Human" |
top |
Desired top number of genes for each pathway according to their significance(p.value). Default to 100 |
decoupleR |
if TRUE, the model matrix is goign to be generated with a format makes it directlz compatible with the decoupleR package |
This function returns model matrix according to the top n significant
#getting a model matrix according to the desired top n significant model <- getModel("Human", top=100)#getting a model matrix according to the desired top n significant model <- getModel("Human", top=100)
HGNC gene symbols in rows, pathways in columns. Pathway activity inference works by matrix multiplication of gene expression with the model.
The full human model contains 22479 genes, associated pathways, weight and the p-value.
gene names in HGNC symbols
names of PROGENy pathways
z-scores for a given gene
significance of gene in pathway
https://www.nature.com/articles/s41467-017-02391-6
get("model_human_full", envir = .GlobalEnv)get("model_human_full", envir = .GlobalEnv)
MGI gene symbols in rows, pathways in columns. Pathway activity inference works by matrix multiplication of gene expression with the model.
The full mouse model contains 17426 genes, associated pathways, weight and the p-value.
gene names in HGNC symbols
names of PROGENy pathways
z-scores for a given gene
significance of gene in a pathway
https://www.ncbi.nlm.nih.gov/pubmed/31525460
get("model_mouse_full", envir = .GlobalEnv)get("model_mouse_full", envir = .GlobalEnv)
This function uses the linear model of pathway-responsive genes underlying the PROGENy method. It transforms a gene expression matrix with HGNC/MGI gene symbols in rows and sample names in columns into a pathway score matrix with samples in rows and pathways in columns.
progeny( expr, scale = TRUE, organism = "Human", top = 100, perm = 1, verbose = FALSE, z_scores = FALSE, get_nulldist = FALSE, assay_name = "RNA", return_assay = FALSE, ... )progeny( expr, scale = TRUE, organism = "Human", top = 100, perm = 1, verbose = FALSE, z_scores = FALSE, get_nulldist = FALSE, assay_name = "RNA", return_assay = FALSE, ... )
expr |
A gene expression object with HGNC/MGI symbols in rows and samples in columns. In order to run PROGENy in single-cell RNAseq data, it also accepts Seurat and SingleCellExperiment object, taking the normalized counts for the computation. |
scale |
A logical value indicating whether to scale the scores of each pathway to have a mean of zero and a standard deviation of one. It does not apply if we use permutations. |
organism |
The model organism - "Human" or "Mouse" |
top |
The top n genes for generating the model matrix according to significance (p-value) |
perm |
An interger detailing the number of permutations. No permutations by default (1). When Permutations larger than 1, we compute progeny pathway scores and assesses their significance using a gene sampling-based permutation strategy, for a series of experimental samples/contrasts. |
verbose |
A logical value indicating whether to display a message about the number of genes used per pathway to compute progeny scores (i.e. number of genes present in the progeny model and in the expression dataset) |
z_scores |
Only applies if the number of permutations is greater than 1. A logical value. TRUE: the z-scores will be returned for the pathway activity estimations. FALSE: the function returns a normalized z-score value between -1 and 1. |
get_nulldist |
Only applies if the number of permutations is greater than 1. A logical value. TRUE: the null distributions generated to assess the signifance of the pathways scores is also returned. |
assay_name |
Only applies if the input is a Seurat object. It selects the name of the assay on which Progeny will be run. Default to: RNA, i.e. normalized expression values. |
return_assay |
Only applies if the input is a Seurat object. A logical value indicating whether to return progeny results as a new assay called Progeny in the Seurat object used as input. Default to FALSE. |
... |
Additional arguments to be passed to the functions. |
The publication of the method is available at: https://www.nature.com/articles/s41467-017-02391-6
The supplied expression object has to contain HGNC/MGI 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 human and mouse model matrices consists of 14 pathways and large set of genes with an associated p-value (p-value per gene and pathway) that accounts for the importance of each gene on each pathway upon perturbation. Its coefficients are non-zero if the gene-pathway pair corresponds to the top N genes (100 by default) 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 rows and pathways in columns. In case we run the method with permutations and the option get_nulldist to TRUE, we will get a list with two elements. The first element is the matrix with the pathway activity as before. The second elements is the null distributions that we generate to assess the signifance of the pathways scores.
# use example gene expression matrix here, this is just for illustration gene_expression <- as.matrix(read.csv(system.file("extdata", "human_input.csv", package = "progeny"), row.names = 1)) # calculate pathway activities pathways <- progeny(gene_expression, scale=TRUE, organism="Human", top = 100, perm = 1)# use example gene expression matrix here, this is just for illustration gene_expression <- as.matrix(read.csv(system.file("extdata", "human_input.csv", package = "progeny"), row.names = 1)) # calculate pathway activities pathways <- progeny(gene_expression, scale=TRUE, organism="Human", top = 100, perm = 1)
Compute progeny pathway scores and assesses significance based on permutations
progenyPerm( df, weight_matrix, k = 10000, z_scores = TRUE, get_nulldist = FALSE )progenyPerm( df, weight_matrix, k = 10000, z_scores = TRUE, get_nulldist = FALSE )
df |
A data.frame of n*m+1 dimension, where n is the number of omic features to be considered and m is the number of samples/contrasts. The first column should be the identifiers of the omic features. These identifiers must be coherent with the identifiers of the weight matrix. |
weight_matrix |
A progeny coefficient matrix. the first column should be the identifiers of the omic features and should be coherent with the identifiers provided in df. |
k |
The number of permutations to be performed to generate the null-distribution used to estimate the significance of progeny scores. The default value is 10000. |
z_scores |
if true, the z-scores will be returned for the pathway activity estimations. Else, the function returns a normalized z-score value between -1 and 1. |
get_nulldist |
if true, the null score distribution used for normalization will be returned along with the actual normalized score data frame. |
This function returns a list of two elements. The first element is a data frame of p*m+1 dimensions, where p is the number of progeny pathways, and m is the number of samples/contrasts. Each cell represents the significance of a progeny pathway score for one sample/contrast. The significance ranges between -1 and 1. The significance is equal to x*2-1, x being the quantile of the progeny pathway score with respect to the null distribution. Thus, this significance can be interpreted as the equivalent of 1-p.value two-sided test over an empirical distribution) with the sign indicating the direction of the regulation. The second element is the null distribution list (a null distribution is generated for each sample/contrast).
# use example gene expression matrix gene_expression <- as.matrix(read.csv(system.file("extdata", "human_input.csv", package = "progeny"), row.names = 1)) # calculate pathway activities progeny(gene_expression, scale=TRUE, organism="Human", top=100, perm=10000)# use example gene expression matrix gene_expression <- as.matrix(read.csv(system.file("extdata", "human_input.csv", package = "progeny"), row.names = 1)) # calculate pathway activities progeny(gene_expression, scale=TRUE, organism="Human", top=100, perm=10000)
This function generate a series of scatter plot with marginal distribution (in the form of an arrangeGrob object), for each progeny pathway and sample/contrast. Each scatter plot has progeny weights as x-axis and the gene level stat used to compute progeny score as the y-axis. The marginal distribution of the gene level stats is displayed on the right of the plot to give visual support of the significance of each gene contributing to the progeny pathway score. The green and red colors represent the positive and negative contribution of genes to the progeny pathway, respectively. For each gene contribution, 4 cases are possible, as the combinations of the sign of the gene level stat and the sign of the gene level weight. Positive weight will lead to a positive(green)/negative(red) gene contribution if the gene level stat is positive/negative. Negative weight will lead to a negative(red)/positive(green) gene contribution if the gene level stat is positive/negative.
progenyScatter( df, weight_matrix, dfID = 1, weightID = 1, statName = "gene stats", verbose = FALSE )progenyScatter( df, weight_matrix, dfID = 1, weightID = 1, statName = "gene stats", verbose = FALSE )
df |
an n*m data frame, where n is the number of omic features (genes). m isn't really important, as long as at least one column corresponds to a sample or contrast statistic. One of the columns should correspond to the gene symbols. |
weight_matrix |
A progeny coefficient matrix. the first column should be the identifiers of the omic features, and should be coherent with the identifiers provided in df. |
dfID |
an integer corresponding to the column number of the gene identifiers of df. |
weightID |
an integer corresponding to the column number of the gene identifiers of the weight matrix. |
statName |
The name of the stat used, to be displayed on the plot |
verbose |
Logical indicating whether we want to have the messages indicating the different computed weights. |
The function returns a list of list of arrangeGrob objects. The first level list elements correspond to samples/contrasts. The second level correspond to pathways. The plots can be saved in a pdf format using the saveProgenyPlots function.
# use example gene expression matrix gene_expression <- read.csv(system.file("extdata", "human_input.csv", package = "progeny")) # getting a model matrix with 100 top significant genes and converting to df weight_matrix <- getModel("Human", top=100) weight_matrix <- data.frame(names = row.names(weight_matrix), row.names = NULL, weight_matrix) #use progenyScatter function plots <- progenyScatter(gene_expression, weight_matrix)# use example gene expression matrix gene_expression <- read.csv(system.file("extdata", "human_input.csv", package = "progeny")) # getting a model matrix with 100 top significant genes and converting to df weight_matrix <- getModel("Human", top=100) weight_matrix <- data.frame(names = row.names(weight_matrix), row.names = NULL, weight_matrix) #use progenyScatter function plots <- progenyScatter(gene_expression, weight_matrix)
This function is designed to save the plots (in pdf format) of a nested (2 level) list of arrangeGrob objects, such as the one returned by the progenyScatter function.
saveProgenyPlots(plots, contrast_names, dirpath)saveProgenyPlots(plots, contrast_names, dirpath)
plots |
a list of list of arrangeGrob object (such as the one returned by the progenyScatter function.).The first level list elements correspond to samples/contrasts. The second level corresponds to pathways. The plots can be saved in a pdf format using the saveProgenyPlots function. |
contrast_names |
a vector of the same length as the first level of the plot list corresponding to the names of each sample/contrast |
dirpath |
the path to the directory where the plots should be saved |
This function produces the pdf files of plots taken from the progenyScatter function
#create plots using progneyScatter function gene_expression <- read.csv(system.file("extdata", "human_input.csv", package = "progeny")) # getting a weight_matrix weight_matrix <- getModel("Human", top=100) weight_matrix <- data.frame(names = row.names(weight_matrix), row.names = NULL, weight_matrix) plots <- progenyScatter(gene_expression, weight_matrix) #create a list with contrast names contrast_names <- names(gene_expression[2:ncol(gene_expression)]) #assign a path to store your plots dirpath <- "./progeny_plots/" # save it # saveProgenyPlots(plots, contrast_names, dirpath)#create plots using progneyScatter function gene_expression <- read.csv(system.file("extdata", "human_input.csv", package = "progeny")) # getting a weight_matrix weight_matrix <- getModel("Human", top=100) weight_matrix <- data.frame(names = row.names(weight_matrix), row.names = NULL, weight_matrix) plots <- progenyScatter(gene_expression, weight_matrix) #create a list with contrast names contrast_names <- names(gene_expression[2:ncol(gene_expression)]) #assign a path to store your plots dirpath <- "./progeny_plots/" # save it # saveProgenyPlots(plots, contrast_names, dirpath)
List with three elements: the gene counts, the experimental design and the result of limma differential analysis
List with three elements: the gene counts, the experimental design and the result of limma differential analysis
gene counts
experiemental design
differential analysis result using limma
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119931
data("vignette_data")data("vignette_data")