A function for ploting the odds of being a case vs control and their effects on adjustments for confounders. It may be useful in cases when it is envisaged that no confounders are expected. It automatically plots the values of Z for the common experimental designs (e.g. 1:1, 1:3 and 1:4). You input alarge number of hypothetical ratios of proportions of cases to controls. It uses another function (ztwo), which computes the Z values.
A functon to plot Density of Z values from a simulation from a multinomial population using the balanced and unbalanced studies and a 3D representaion of the Z values. These plots are useful as visual tools for the confounding effects. The median values are indicated on these plots and these can be used as the consesus values of the effects of covariates in sample size calculations.
This is a class object for the mass spectrometry data sets, which are in the same format as the raw data from the Biomarkers wizard software. It has slots of matrices of raw mass spectrometry and phenotypic data sets, a character variable for the classes of all the covariates in the phenotypic data matrix, a character variable for the covariates of interest, and numeric value for the number of peaks of interest.
betweensampleVariance
(Package: clippda) :
A generic function for computing the biological variance and mean differences
This generic function fits a regression model to the averaged replicate data. The outputs are the between sample variance, and the differences in mean expression between cases and controls, adjusted for confounders.
checkNo.replicates
(Package: clippda) :
A function to detect disparity in the number of replicates across assays
Sometimes in a mass spectrometry experiment, it happens that a few samples have been mislabelled. Mislabelling means that some replicates are in the wrong sample group, and this results in some samples having more (or less) replicates than the number intended by the experimentalist. Apart from disparity in the number of replicates due to mislabelling, a few samples, e.g. the quality control (QC) samples, are often assayed several times. The aim is to analyze data with the same number of technical replicates (in this case, duplicates) for every sample. The function checkNo.replicates identifies samples with a disparate number of replicates. The identified samples are treated as follows:
clippda-package
(Package: clippda) :
A package for clinical proteomics profiling data analysis
This package is still under development but it is intended to provide a range of tools for analysing clinical genomics, methylation and proteomics, data with the non-standard repeated expression measurments arising from technical replicates. Most of these studies are observational case-control by design and the results of analyses must be appropriately adjusted for confounding factors and imbalances in the data. This regression-type problem is different from the regression problem in limma, in which all the covariates are some kind of contrasts and are therefore important. Our method is specifically suitable for analysing single-channel microarrays and proteomics data with repeated probe, or peak measurements, especially in the case where there is no one-to-one correspondence between cases and controls and the data cannot be analysed as log-ratios. In the current version (version 0.1.0), we are more concerned with the problem of sample size calculations for these data sets. But some tools for pre-processing of the repeated peaks data, including tools for checking for the consistency in the number of replicates across samples, the consistency of the peak information between replicate spectra and tools for data formatting and averaging, are included. clippda also implements a routine for evaluating differential-expression between cases and controls, especially for data in which each sample is assayed more than once, and are obtained from studies which are observational, or those for which the data are heterogeneous (e.g. data for cancer studies in which controls are not directly sampled, but are obtained from samples from suspected cases that turn out to be benign disease, after an operation, for example. In this case there could be serious imbalances in demographics between the cases and controls). The test statistics considered are derived from the methods developed by Nyangoma et al. (2009). These new methods for evaluating differential-expression are compared with the empirical Bayes method in the limma package. To limit the number of false positive discoveries, we control the tail probability of the proportion of false positives, (TPPFP). Further details can be found in the package vignette.
A function to compute the Z values when planning an experiment with a binary exposure and a binary confounder. You input the probabilities of 3-cells of the resulting multinomial distribution.
● Data Source:
BioConductor
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● Alias: f
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