plotProportions {DRIMSeq} | R Documentation |
This plot is available only for a group design, i.e., a design that is equivalent to multiple group fitting.
plotProportions(x, ...) ## S4 method for signature 'dmDSfit' plotProportions(x, gene_id, group_variable, plot_type = "barplot", order_features = TRUE, order_samples = TRUE, plot_fit = TRUE, plot_main = TRUE, group_colors = NULL, feature_colors = NULL) ## S4 method for signature 'dmSQTLfit' plotProportions(x, gene_id, snp_id, plot_type = "boxplot1", order_features = TRUE, order_samples = TRUE, plot_fit = FALSE, plot_main = TRUE, group_colors = NULL, feature_colors = NULL)
x |
|
... |
Other parameters that can be defined by methods using this generic. |
gene_id |
Character indicating a gene ID to be plotted. |
group_variable |
Character indicating the grouping variable which is one
of the columns in the |
plot_type |
Character defining the type of the plot produced. Possible
values |
order_features |
Logical. Whether to plot the features ordered by their expression. |
order_samples |
Logical. Whether to plot the samples ordered by the
group variable. If |
plot_fit |
Logical. Whether to plot the proportions estimated by the full model. |
plot_main |
Logical. Whether to plot a title with the information about the Dirichlet-multinomial estimates. |
group_colors |
Character vector with colors for each group defined by
|
feature_colors |
Character vector with colors for each feature of gene
defined by |
snp_id |
Character indicating the ID of a SNP to be plotted. |
In the QTL analysis, plotting of fitted proportions is deactivated
even when plot_fit = TRUE
. It is due to the fact that neither fitted
values nor regression coefficients are returned by the dmFit
function as they occupy a lot of memory.
For a given gene, plot the observed and estimated with Dirichlet-multinomial model feature proportions in each group. Estimated group proportions are marked with diamond shapes.
Malgorzata Nowicka
plotData
, plotPrecision
,
plotPValues
# -------------------------------------------------------------------------- # Create dmDSdata object # -------------------------------------------------------------------------- ## Get kallisto transcript counts from the 'PasillaTranscriptExpr' package library(PasillaTranscriptExpr) data_dir <- system.file("extdata", package = "PasillaTranscriptExpr") ## Load metadata pasilla_metadata <- read.table(file.path(data_dir, "metadata.txt"), header = TRUE, as.is = TRUE) ## Load counts pasilla_counts <- read.table(file.path(data_dir, "counts.txt"), header = TRUE, as.is = TRUE) ## Create a pasilla_samples data frame pasilla_samples <- data.frame(sample_id = pasilla_metadata$SampleName, group = pasilla_metadata$condition) levels(pasilla_samples$group) ## Create a dmDSdata object d <- dmDSdata(counts = pasilla_counts, samples = pasilla_samples) ## Use a subset of genes, which is defined in the following file gene_id_subset <- readLines(file.path(data_dir, "gene_id_subset.txt")) d <- d[names(d) %in% gene_id_subset, ] # -------------------------------------------------------------------------- # Differential transcript usage analysis - simple two group comparison # -------------------------------------------------------------------------- ## Filtering ## Check what is the minimal number of replicates per condition table(samples(d)$group) d <- dmFilter(d, min_samps_gene_expr = 7, min_samps_feature_expr = 3, min_gene_expr = 10, min_feature_expr = 10) plotData(d) ## Create the design matrix design_full <- model.matrix(~ group, data = samples(d)) ## To make the analysis reproducible set.seed(123) ## Calculate precision d <- dmPrecision(d, design = design_full) plotPrecision(d) head(mean_expression(d)) common_precision(d) head(genewise_precision(d)) ## Fit full model proportions d <- dmFit(d, design = design_full) ## Get fitted proportions head(proportions(d)) ## Get the DM regression coefficients (gene-level) head(coefficients(d)) ## Get the BB regression coefficients (feature-level) head(coefficients(d), level = "feature") ## Fit null model proportions and perform the LR test to detect DTU d <- dmTest(d, coef = "groupKD") ## Plot the gene-level p-values plotPValues(d) ## Get the gene-level results head(results(d)) ## Plot feature proportions for a top DTU gene res <- results(d) res <- res[order(res$pvalue, decreasing = FALSE), ] top_gene_id <- res$gene_id[1] plotProportions(d, gene_id = top_gene_id, group_variable = "group") plotProportions(d, gene_id = top_gene_id, group_variable = "group", plot_type = "lineplot") plotProportions(d, gene_id = top_gene_id, group_variable = "group", plot_type = "ribbonplot")