PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 1468017

A program for computer-aided analyses of ecological field data.

D Piepenburg·U Piatkowski
View original source ↗Export metadata ↓

Abstract

A program for IBM-compatible microcomputers is introduced which combines several complementary analyses of species-station-tables generated in ecological field investigations. The scope of the program encompasses table editing functions, routines for community delimitation by cluster analysis and procedures for the analysis of properties related both to stations (e.g. diversities) and species (e.g. abundance statistics and association indices). The essential reasoning behind the application of community studies is presented briefly, as well as the multi-step analytical approach implemented in the program.

Explore related subjects

Cluster AnalysisData Interpretation, StatisticalEcologyMathematical ComputingMicrocomputersPhylogenySoftwareJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D Piepenburg, U Piatkowski. 1992. A program for computer-aided analyses of ecological field data.. https://doi.org/10.1093/bioinformatics%2F8.6.587

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

MOTIVATION: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). RESULTS: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. AVAILABILITY AND IMPLEMENTATION: INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Cluster Analysis↗
PubMed2005

Fuzzy species among recombinogenic bacteria.

BACKGROUND: It is a matter of ongoing debate whether a universal species concept is possible for bacteria. Indeed, it is not clear whether closely related isolates of bacteria typically form discrete genotypic clusters that can be assigned as species. The most challenging test of whether species can be clearly delineated is provided by analysis of large populations of closely-related, highly recombinogenic, bacteria that colonise the same body site. We have used concatenated sequences of seven house-keeping loci from 770 strains of 11 named Neisseria species, and phylogenetic trees, to investigate whether genotypic clusters can be resolved among these recombinogenic bacteria and, if so, the extent to which they correspond to named species. RESULTS: Alleles at individual loci were widely distributed among the named species but this distorting effect of recombination was largely buffered by using concatenated sequences, which resolved clusters corresponding to the three species most numerous in the sample, N. meningitidis, N. lactamica and N. gonorrhoeae. A few isolates arose from the branch that separated N. meningitidis from N. lactamica leading us to describe these species as 'fuzzy'. CONCLUSION: A multilocus approach using large samples of closely related isolates delineates species even in the highly recombinogenic human Neisseria where individual loci are inadequate for the task. This approach should be applied by taxonomists to large samples of other groups of closely-related bacteria, and especially to those where species delineation has historically been difficult, to determine whether genotypic clusters can be delineated, and to guide the definition of species.

Cluster Analysis↗
PubMed1992

Subgroup analyses.

Explore the source record for details and available documents.

Cluster Analysis↗