Selected Software by Dr. S.D. Peddada

The following is a sample of software developed by Dr. Peddada and his team over the years, which are freely available by selecting the links in the following sections. Please check this page regularly for updates as we develop new methods.

Analysis of Compositional Microbiomes (ANCOM)

ANCOM data (available through the Quantitative Insights Into Microbial Ecology [QIIME] 2 website external link)

(Original R-code developed by Dr. Siddhartha Mandal, Norwegian Institute of Public Health)

This package is designed for comparing the abundance of individual taxa in two populations using log-ratios of abundance. This software is based on the ANCOM methodology developed in Mandal et al. This program is available within the software suite QIIME2.

Reference: 

Mandal, S., Van, T. W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015). Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial Ecology in Health and Disease, 26, 1-7.

Order-Restricted Inference for Ordered Gene Expression (ORIOGEN)

ORIOGEN 4.01 allowing Multidimensional Pairwise Comparisons (available through the National Institute of Environmental Health Sciences [NIEHS] website)

This software allows users to select between two options for comparing two or more experimental groups. The first option is used for analyzing ordered experimental conditions (e.g., time, dose, tumor stages, etc.). Under this option, the software can handle an independent sample case, as well as a dependent sample case (e.g., repeated measurements). The residual bootstrap methodology used in this software is robust to any underlying dependence structure. The method controls the false discovery rate (FDR) at the desired level.

The second option is suitable for pairwise comparisons and is not limited to ordered experimental conditions. Thus, for any given design, the second option allows one to make all desired pairwise comparisons among the experimental groups. In addition, it allows one to make directional inferences (e.g., up or down regulated genes, etc.). The method controls for the overall mixed directional FDR.

References:

  • Guo, W., & Peddada, S. D. (2008). Adaptive choice of the number of bootstrap samples in large scale multiple testing. Statistical Applications in Genetics and Molecular Biology, 7(1).
  • Peddada, S. D., Harris, S., Zajd, J., & Harvey, E. (2001). ORIOGEN: order restricted inference for ordered gene expression data. Bioinformatics, 21(20), 3933-3934.
  • Peddada, S. D., Lobenhofer, L., Li, L., Afshari, C., Weinberg, C., & Umbach, D. (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics, 19(7), 834-841.

Constrained Linear Mixed Effects (CLME)

CLME for analyzing mixed and fixed models under inequality constraints (available through the Journal of Statistical Software website external link)

(Programmed by Dr. Casey M. Jelsema, Research Fellow, Biostatistics Branch, NIEHS)

In many applications, such as in dose-response studies or time-course experiments, researchers are interested in testing for specific inequality constraints or patterns among the means of experimental groups. This R package is designed to test for such inequality patterns using a robust residual bootstrap-based methodology that does not require the data to be normally distributed. Furthermore, this software can also handle the presence of covariates and/or random effects. For example, this package can be used in the context of repeated measurement designs with covariates. This package comes with a user-friendly graphical interface that requires no programming to run it. All the user needs to do is to provide the input source of the data and select options from the interface.

Reference:

Jelsema, C. M., & Peddada, S. D. (2016). CLME: an R package for linear mixed effects models under inequality constraints. Journal of Statistical Software, 75. doi: 10.18637/jss.v075.i01. 

Order-Restricted Interference for Oscillatory Systems (ORIOS)

ORIOS for Detecting Rhythmic Signals (available through the NIEHS website)

(R code developed by Yolanda Larriba, University of Valladolid, Valladolid, Spain)

ORIOS is a model-free, order-restricted, inference-based algorithm that detects rhythmic components (e.g., transcripts or genes) participating in oscillatory systems such as the circadian clock. Although this software can be used for any oscillatory data, for simplicity of description, we will use the term “circadian clock data” rather than “oscillatory data” and “genes” in place of “components” of an oscillatory system. The strength of model-free methodology such as the order-restricted inference is that, instead of using a mathematical model to describe the shape or pattern of expression, it uses mathematical inequalities to describe patterns. Thus, it is not limited by any mathematical model that may be rigid and restricted.

ORIOS not only identifies rhythmic genes, it also classifies them into four typical classes of genes, called cyclical, quasi cyclical, non-flat and non-periodic, and flat, according to its signal shape. Cyclical and quasi cyclical genes are declared as rhythmic, while non-flat and non-periodic, and flat are declared as non-rhythmic genes. Compared to some commonly used rhythmicity detection algorithms, ORIOS has substantially higher power to detect true rhythmic genes, while also declaring substantially fewer non-rhythmic genes as rhythmic.

Reference: 

Larriba, Y., Rueda, C., Fernandez, M. A., & Peddada, S. D. (2016). Order restricted inference for oscillatory systems for detecting rhythmic signals. Nucleic Acids Research, 44(22). doi: 10.1093/nar/gkw771.

Temporal Order in an Oscillatory System

R Code for Estimating of Global Relative Order of Peak Expression Satisfied by a Set of Oscillatory Genes (28KB)

(Programmed and available through Ms. Sandra Barragán, University of Valladolid, Spain, miguelaf@eio.uva.es)

For a given collection of oscillatory genes (e.g., cell-cycle genes or circadian clock genes) with phase angles estimated from multiple experiments, in this software we estimate the relative order of peak expression among the genes. It contains 2 functions written in R, called Aggregation of Circular Orders (ACO), which is based on a solution to the traveling salesman problem, and Circular Local Minimization (CLM) algorithm which is used to smooth the solution obtained from ACO. To run these programs the user should first download the companion R package called \emph{isocir} from CRAN http://cran.rproject.org/web/packages/isocir/index.html external link

Reference:

Barragán, S., Rueda, C., Fernández, M. A., & Peddada, S. D. (2015). Determination of temporal order among the components of an oscillatory system. PLoS ONE 10(7): e0124842. https://doi.org/10.1371/journal.pone.0124842 external link

top of pageBACK TO TOP