The present package is a rewritten version of blotIt2 by Daniel Kaschek. The aim of this toolbox is to scale biological replicate data to a common scale, making the quantitative data of different gels comparable.

Please note that blotIt3 and blotIt2 can be used in parallel. All functions have different names, so they can not only be installed but also loaded and used simultaneously (great for double checking).

blotIt3 requires the R packages utils, MASS, data.table, ggplot2, rootSolve and trust. Additionally, the package devtools is needed to install blotIt3 from github. If not already done, the required packages can be installed by executing

install.packages(c("utils", "MASS", "data.table", "ggplot2", "rootSolve", "trust", "devtools"))

blotIt3 then is installed via devtools:

devtools::install_github("SeverinBang/blotIt3")

First, the package is imported

library(blotIt3)

A .csv file is imported and is formatted by the function read_wide. An example data file is supplied. It can be accessed by

example_data_path <- system.file(
                "extdata", "sim_data_wide.csv",
                package = "blotIt3"
            )

This reads out the provided example file, transfers it to a temporary location and stores the path to this temporary location in example_data_path. The example file is structured as follows

The first three columns contain description data: time points, measurement conditions and IDs (e.g. the IDs of the different gels). All following columns contain the measurements of different targets, with the first row containing the names and the following the measurement values corresponding to the time, condition and ID stated in the first columns.

The information which columns contain descriptions has to be passed to read_wide:

imported_data <- read_wide(
    file = example_data_path, # path to the example file
    description = seq(1,3), # Indices of columns containing the information
    sep = ",", # sign seperating the colums
    dec = "." # decimal sign
)

The result is then a long table of the form

While the first (nameless) columns just contains (unique) row names. New are the columns name and value. While the column names of the original file are pasted in the former, the latter contains the respective values. The data.frame imported_data can now be passed to the main function.

The full function call is

scaled_data <- align_me(
  data = imported_data,
  model = "yi / sj",
  error_model = "value * sigmaR",
  biological = yi ~ name + time + condition,
  scaling = sj ~ name + ID,
  error = sigmaR ~ name + 1,
  parameter_fit_scale = "log",
  normalize = TRUE,
  average_techn_rep = FALSE,
  verbose = FALSE,
  normalize_input = TRUE
)

We will go now through the parameters individually:

• data A long table, usually the output of read_wide
• model A formula like describing the model used for aligning. The present one yi / sj means that the measured values Y_i are the real values yi scaled by scaling factors sj. The model therefore is the real value divided by the corresponding scaling factor.
• error_model A description of which errors affect the data. Here, only a relative error is present, where the parameter sigmaR is scaled by the respective value
• biological Description of which parameter (left hand side of the tilde) represented by which columns (right hand side of the tilde) contain the "biological effects". In the present example, the model states that the real value is represented by yi -- which is the left hand side of the present biological entry. The present right hand side is "name", "time" and "condition". In short: we state that the entries "name", "time" and "condition" contain real, biological differences.
• scaling Same as above, but here is defined which columns contain identificators of different scaling. Here it is "name" and "ID", meaning that measurements with differ in this effects, (but have the same biological effects) are scaled upon another.
• error Describes how the error affects the values individually. The present formulation means, that the error parameter is not individually adjusted.
• parameter_fit_scale Describes the scale on which the parameter are fitted. align_me() accepts "linear", "log", "log2" and "log10". The default is "Linear".
• average_techn_rep A logical parameter that indicates, if technical replicates should be averaged before the scaling.
• verbose If set to TRUE additional information will be printed in the console.
• normalize_input If set to TRUE, the data will be scaled before the actual scaling. This means that the raw input will be scaled to a common order of magnitude before the scaling parameters will be calculated. This is only a computational aid, to eliminate a rare fail of convergence when the different values differ by many orders of magnitude. Setting this to TRUE makes only sense (and is only supported) for parameter_fit_scale = "linear".

The result of align_me() is a list with the entries

• aligned A data.frame with the columns containing the biological effects as well as the columns value containing the "estimated true values" and sigma containing the uncertainty of the fits. Both are on common
• scaled The original data but with the values scaled to common scale and errors from the evaluation of the error model, also scaled to common scale (obeying Gaussian error propagation).
• prediction The scales and sigma are from the evaluation of the respective models (on original scale).
• original Just the original parameters
• original_with_parameters As above but with additional columns for the estimated parameters.
• biological Names of the columns defined to contain the biological effects.
• scaling Names of the columns defined to contain the scaling effects.

blotIt3 provides one plotting function plot_align_me() which data set will be plotted can be specified per parameter

plot_align_me(
    out_list = scaled_data,
    plot_points = "aligned",
    plot_line = "aligned",
    spline = FALSE,
    scales = "free",
    align_zeros = TRUE,
    plot_caption = TRUE,
    ncol = NULL,
    my_colors = NULL,
    duplicate_zero_points = FALSE,
    my_order = NULL
)

The parameters again are:

• out_list the result of align_me()
• plot_points It can separately specified which data sets should be plotted as dots and as line. Here the data set for the dots is defined. It can be either of original, scaled, prediction or aligned.
• plot_line Same above but for the line.
• spline Logical parameter, if set to TRUE, the line plotted will be not straight lines connecting points but a smooth spline.
• scales String passed as scales argument to facet_wrap.
• align_zeros Logical parameter, if set to TRUE the zero ticks will be aligned throughout all the sub plots, although the axis can have different scales.
• plot_caption Logical parameter, indicating if a caption describing which data is plotted should be added to the plot.
• ncol Numerical passed as ncol argument to facet_wrap.
• my_colors list of custom color values as taken by the values argument in the scale_color_manual method for ggplot objects, if not set the default ggplot color scheme is used.
• duplicate_zero_points Logical, if set TRUE all zero time points are assumed to belong to the first condition. E.g. when the different conditions consist of treatments added at time zero. Default is FALSE.
• my_order Optional list of target names in the custom order that will be used for faceting
• ... Logical expression used for subsetting the data frames, e.g. name == "pAKT" & time < 60

GPL-3