Calculate Progeny Scores with Permutations — progenyScatter • progeny
Description: 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.

# Calculate Progeny Scores with Permutations

Source: `R/progenySuppFunc.r`
`progenyScatter.Rd`

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
    )

## Arguments

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.

## Value

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.

## Examples

    # 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)
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