The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. The phenotypic reaction of single-celled organisms such as bacteria, fungi, and animal cell cultures to up to 2,000 environmental challenges can be recorded on sets of 96-well microtiter plates.
The opm package for the free statistical software environment R offers tools for storing the curve kinetics, aggregating the curve parameters, recording associated metadata of organisms and experimental settings as well as methods for analyzing these highly complex data sets graphically and statistically. The package also includes 95% confidence plots, enhanced heatmap graphics and customized multiple comparisons of means procedures for comparing the estimated curve parameters. It is also possible to discretize these parameters and to export them for investigations with other programs and for generating reports for taxonomic journals such as IJSEM. Export and import in the YAML, JSON or CSV format facilitates the data exchange among labs. The CSV files produced by the OmniLog® reader can not only be easily imported but also batch-converted in large numbers.
The opm package for R is a
comprehensive software for analysing phenotype microarray and
growth-curve data. For more information, see the opm
R-Forge site or the main tutorial for
opm.
The package can be used on Windows, Mac and Linux/UNIX
systems. As a prerequisite, one needs to obtain the statistical
computing environment R. We also recommend
a graphical user interface such as RStudio. Both are freely available;
instructions for installation are given on their websites. Maria
del Carmen Montero-Calasanz has compiled a detailed description of
the installation of opm etc. under Windows. The
shown use of graphical user interfaces is similar on other
systems.
There are three ways to install opm and its
dependencies. The first way is to visit the opm
R-Forge site, download the source files or Windows binaries
and install them locally. Alternatively, at the R
prompt, enter:
source("http://www.goeker.org/opm/install_opm.R")
You will then be asked for what exactly to install. In our
experience this works well under Windows, if otherwise please let
us know. (But please first see the troubleshooting section.)
Third, opm and its helper packages can be downloaded
using the links further below on this page and installed manually
(and optionally checked beforehand). Documentation comes with the
packages but is also linked below.
The only somewhat more frequently encountered problems we are
aware of when attempting to install opm and its
dependencies are the following.
R environmentR. Detailed
instructions on how to do this are given elsewhere.R packagesR packages on which opm
or the installation script depend hinder the installation. To
solve this we recommend biocLite as a
convenient tool to install not only Bioconductor
but also core packages, and to update many packages at once. To
use biocLite just copy and paste the code snippet
shown there
into the R prompt.Rtools on WindowsRtools is not needed to install the
opm package (not even under Windows), but we have
observed that old Rtools versions might yield
errors with the opm installation script. If so,
either deinstall or upgrade Rtools before running
the script.In the case of errors that cannot be resolved, please send us the complete output generated when loading the installation file.
Using
opm |
the main tutorial for opm, the best
starting point for new users |
Substrate information in
opm |
availability and use of data on phenotype microarray
substrates in opm |
Growth curves in
opm |
applying opm not to phenotype microarray
data but to growth curves |
| pkgutils | comprehensive online documentation of the latest
pkgutils version |
| opm | comprehensive online documentation of the latest
opm version |
| opmdata | comprehensive online documentation of the latest
opmdata version |
| opmextra | comprehensive online documentation of the latest
opmextra version |
| RStudio | notes on the use of RStudio |
| Fact Sheet | fact sheet summarizing the main features of
opm |
| ISME 2012 | our poster presented at the ISME 14 conference |
| SRI 2013 | our talk at the Conference on Predicting Cell Metabolism and Phenotypes |
| Workshop | our introduction to the opm workshop at the 2015 Phenotype Microarray conference in Florence |
| DSMZ | opm introduction at DSMZ |
| pkgutils | built and checked R source-code archive of
the latest pkgutils version |
| opm | built and checked R source-code archive of
the latest opm version |
| opmdata | built and checked R source-code archive of
the latest opmdata version |
| opmextra | built and checked R source-code archive of
the latest opmextra version |
The last change to the packages or their documentation has been made on Fri Jul 26 03:07:35 CEST 2019.
We thank the authors and maintainers of the R
packages on which pkgutils, opm and
opmdata depend and of those packages used to
generate this documentation. Helpful feedback from
opm users is gratefully acknowledged.
Overview on opm |
Lea A.I. Vaas, J. Sikorski, B. Hofner, N. Buddruhs, A. Fiebig, H.-P. Klenk and M. Göker. "opm: An R package for analysing OmniLog® Phenotype MicroArray Data". Bioinformatics 29 (14): 1823-1824, 2013. |
Parameter comparison and visualization with
opm |
Lea A.I. Vaas, J. Sikorski, V. Michael, M. Göker and H.-P. Klenk. "Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics". PLoS ONE 7 (4): e34846, 2012. |
Advanced usage of opm to
detect differential expressions |
B. Hofner, L. Boccuto and M. Göker. "Controlling false discoveries in high-dimensional situations: Boosting with stability selection". BMC Bioinformatics 16 (6): 144, 2015. |
The pkgutils, opm and
opmdata packages are free software published under
the GPL and
come with absolutely no warranty. This holds even though a lot of
effort was invested into getting the packages free of bugs.
For contact addresses see the R-Forge site.