oxigraph
RDF graph CLI tool and SPARQL HTTP server
Oxigraph CLI is a graph database
implementing the SPARQL standard.
It is packaged as a command-line tool
allowing to manipulate RDF files and query them using SPARQL,
and also allows to spawn a HTTP server on top of the database.
php-matomo-component-network
component providing Network tools
This package contains a component that provide network tools.
Especially it can be used to manipulate IP addresses.
postgresql-17-pgpcre
Perl Compatible Regular Expressions (PCRE) extension for PostgreSQL
This is a module for PostgreSQL that exposes Perl-compatible regular
expressions (PCRE) functionality as functions and operators. It is based on
the popular PCRE library.
postgresql-17-asn1oid
ASN.1 OID data type for PostgreSQL
This plugin provides the necessary support functions to store ASN.1 OIDs in
a PostgreSQL database.
postgresql-17-http
HTTP client for PostgreSQL, retrieve a web page from inside the database
PostgreSQL extension to make HTTP requests from within the database, returning
results for usage in SQL queries.
r-bioc-densvis
density-preserving data visualization via non-linear dimensionality reduction
Implements the density-preserving modification to t-SNE
and UMAP described by Narayan et al. (2020)
<doi:10.1101/2020.05.12.077776>.
The non-linear dimensionality reduction techniques t-SNE and UMAP
enable users to summarise complex high-dimensional sequencing data
such as single cell RNAseq using lower dimensional representations.
These lower dimensional representations enable the visualisation of discrete
transcriptional states, as well as continuous trajectory (for example, in
early development). However, these methods focus on the local neighbourhood
structure of the data. In some cases, this results in
misleading visualisations, where the density of cells in the low-dimensional
embedding does not represent the transcriptional heterogeneity of data in the
original high-dimensional space. den-SNE and densMAP aim to enable more
accurate visual interpretation of high-dimensional datasets by producing
lower-dimensional embeddings that accurately represent the heterogeneity of
the original high-dimensional space, enabling the identification of
homogeneous and heterogeneous cell states.
This accuracy is accomplished by including in the optimisation process a term
which considers the local density of points in the original high-dimensional
space. This can help to create visualisations that are more representative of
heterogeneity in the original high-dimensional space.