Packaged: 2018-06-28 07:29:01 UTC; christopher

Packaged: 2018-08-28 11:31:31 UTC; christopher

Packaged: 2018-09-12 06:59:13 UTC; christopher

Packaged: 2018-10-05 11:47:24 UTC; christopher

Description: Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created 'clusterlab'. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers.
Packaged: 2019-01-22 10:59:45 UTC; christopher

Packaged: 2019-08-02 12:59:33 UTC; christopher

Packaged: 2019-09-05 07:33:13 UTC; christopher

Title: Unbiased Cell Type Identification of Single-Cell Transcriptomic
Description: A typical computational pipeline to process single-cell RNA sequencing (scRNA-seq) data  involves clustering of cells. Assignment of cell type labels to those clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging if you are not familiar with all the captured subpopulations or have unexpected contaminants. 'clustermole' provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them.
Imports: dplyr, GSVA (>= 1.26.0), magrittr, methods, rlang (>= 0.1.2),
Suggests: covr, roxygen2, testthat (>= 2.1.0), knitr, rmarkdown