One of the most utilized features of the software is its clustering capability. By applying algorithms like UPGMA (Unweighted Pair Group Method with Arithmetic Mean) or Neighbor-Joining, users can generate phenograms and dendrograms that visually represent the relatedness between different OTUs (Operational Taxonomic Units). For those requiring more advanced spatial visualization, the software also supports Principal Coordinates Analysis (PCoA) and Principal Component Analysis (PCA). These ordination techniques allow researchers to plot samples in two- or three-dimensional space, making it easier to identify clusters or outliers that might be obscured in a traditional tree diagram.
Although modern software such as R, Python (with SciPy and scikit-learn), and PAST have largely supplanted legacy DOS/Windows programs, NTSYS pc 2.02 remains an important artifact and, for some niche academic and industrial applications, a still-functional tool. This article provides an exhaustive look at the software, its features, historical context, installation, usage, and its lingering relevance today. ntsys pc 2.02 software
Compares two matrices (e.g., for a Mantel test) to check goodness of fit. One of the most utilized features of the
Extracts eigenvectors for Principal Component Analysis (PCA). Compares two matrices (e
→ PLOT → select tree.den . Adjust scaling, then print or export.