plot_impulse: Plot impulse model fits
In ImpulseDE: Detection of DE genes in time series data using impulse models

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/Impulse_DE_fin.R

Plots impulse model fits for the specified gene IDs. In the case of two time courses, the fits for the combined, case and control data are plotted.

plot_impulse(gene_IDs, data_table, data_annotation, imp_fit_genes,
  control_timecourse = FALSE, control_name = NULL, case_name = NULL,
  file_name_part = "", title_line = "", sub_line = "",
  new_device = TRUE)

`gene_IDs`	character vector of gene names to be plotted; must be part of the `rownames` of `data_table`
`data_table`	numeric matrix of expression values; genes should be in rows, samples in columns. Data should be properly normalized and log2-transformed as well as filtered for present or variable genes.
`data_annotation`	table providing co-variables for the samples including condition and time points. Time points must be numeric numbers.
`imp_fit_genes`	list of fitted impulse model values and parameters as produced by `impulse_DE` (list element `impulse_fit_results`).
`control_timecourse`	logical indicating whether a control time timecourse is part of the data set (`TRUE`) or not (`FALSE`). Default is `FALSE`.
`control_name`	character string specifying the name of the control condition in `annotation_table`.
`case_name`	character string specifying the name of the case condition in `annotation_table`. Should be set if more than two conditions are present in `data_annotation`.
`file_name_part`	character string to be used as file extention.
`title_line`	character string to be used as title for each plot.
`sub_line`	character string to be used as subtitle for each plot.
`new_device`	logical indicating whether each plot should be plotted into a new device (`TRUE`) or not (`FALSE`). Default is `TRUE`.

Plots of the impulse model fits for the specified gene IDs.

Jil Sander

Chechik, G. and Koller, D. (2009) Timing of Gene Expression Responses to Envi-ronmental Changes. J. Comput. Biol., 16, 279-290.

impulse_DE, calc_impulse.

#' Install package longitudinal and load it
library(longitudinal)
#' Attach datasets
data(tcell)
#' check dimension of data matrix of interest
dim(tcell.10)
#' generate a proper annotation table
annot <- as.data.frame(cbind("Time" =
     sort(rep(get.time.repeats(tcell.10)$time,10)),
     "Condition" = "activated"), stringsAsFactors = FALSE)
#' Time columns must be numeric
annot$Time <- as.numeric(annot$Time)
#' rownames of annotation table must appear in data table
rownames(annot) = rownames(tcell.10)
#' since genes must be in rows, transpose data matrix using t()
#' consider 6 genes for now only
genes <- c("SIVA","CD69","ZNFN1A1","IL4R","MAP2K4","JUND")
tcell.10.filtered <- t(tcell.10[,genes])
#' generate a list object having the form of the output of impulse_DE
#' first the parameter fits and SSEs
impulse_parameters_case <- matrix(
    c(0.6,	18.6,	17.2,	17.4,	5.1,	40.2,	3.5,
      0.3,	-464.9,	18.3,	17.3,	-17.2,	35.3,	17.5,
      23.2,	18,	18.8,	18.5,	3,	37,	13.2,
      NA,	NA,	NA,	NA,	NA,	NA,	3.1,
      NA,	NA,	NA,	NA,	NA,	NA,	9.6,
      9.5,	17.5,	18.7,	17.5,	8,	48,	46.7),length(genes),7, byrow = TRUE)
rownames(impulse_parameters_case) <- genes
colnames(impulse_parameters_case) <- c("beta", "h0", "h1", "h2", "t1", "t2", "SSE")
#' then the fitted values for the time points
impulse_fits_case <- matrix(c(
    18.55,	18.43,	18.15,	17.73,	17.43,	17.24,	17.24,	17.24,	17.38,	17.38,
    16.22,	17.18,	17.7,	17.97,	18.12,	18.27,	18.26,	18.03,	17.3,	17.28,
    18,	18,	18.82,	18.82,	18.82,	18.82,	18.82,	18.82,	18.48,	18.48,
    15.93,	15.93,	15.93,	15.93,	15.93,	15.93,	15.93,	15.93,	15.93,	15.93,
    17.62,	17.62,	17.62,	17.62,	17.62,	17.62,	17.62,	17.62,	17.62,	17.62,
    17.5,	17.5,	17.5,	17.5,	18.18,	18.67,	18.67,	18.67,	17.98,	17.53)
    ,length(genes),length(unique(annot$Time)), byrow = TRUE)
rownames(impulse_fits_case) <- genes
colnames(impulse_fits_case) <- unique(annot$Time)
#' finalize list object
impulse_fit_genes <- list("impulse_parameters_case" = impulse_parameters_case,
                          "impulse_fits_case" = impulse_fits_case)
#' Plot expression values
plot_impulse(genes, tcell.10.filtered, annot, impulse_fit_genes)