Brian M. Schilder's repositories
CV
Programmatically generated curriculum vitae.
autoCV
Automatically generate and style your CV from tables.
UCE
UCE is a zero-shot foundation model for single-cell gene expression data
monarchr
R package for easy access, manipulation, and analysis of Monarch KG data
BBreprex
BiocBook
BiocStickers
Stickers for some Bioconductor packages - feel free to contribute and/or modify.
taxize
A taxonomic toolbelt for R
pals
Color Palettes and Palette Evaluation Tools
anndataR
AnnData interoperability in R
bschilder
Personal profile
graphiti
Extract colour palettes from graffiti artworks.
lemur
Latent Embedding Multivariate Regression
signac
R toolkit for the analysis of single-cell chromatin data
BuenColors
R package of colors for the Buenrostro Lab
BiocWorkingGroups
Bioconductor working group guidelines. Also, a list of active, suggested, and inactive working groups for bioconductor for the community to volunteer to be apart of. The community is also welcome to suggest new working groups.
SATURN
SATURN: Uniting Single-cell Gene Expressions with Protein Sequences for Cross-Species Integration
Ilona_thesis
Figures for Ilona's thesis.
ThreeWayTest
Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants
BMSchilder
Professional website for Brian M. Schilder
quick-add-github-issue-browser-extension
Quickly add GitHub issues direct from a browser button
ormr
Work with Python installed at a custom location
gcaer
R package interface to GCAE
MACSr
MACS3 R/BioC wrapper
rphylopic
Get silhouettes of organisms from Phylopic.
EWCE
Expression Weighted Celltype Enrichment. See the package website for up-to-date instructions on usage.
Rbowtie2
Bioconductor package: an R wrapper for Bowtie2 and AdapterRemoval
configs
Config files used to define parameters specific to compute environments at different Institutions
ELeFHAnt
Ensemble Learning for Harmonization and Annotation of Single Cells (ELeFHAnt) provides an easy to use R package for users to annotate clusters of single cells, harmonize labels across single cell datasets to generate a unified atlas and infer relationship among celltypes between two datasets. It provides users with a flexibility of choosing a machine learning based classifiers or let ELeFHAnt automatically use the power of robust classifiers like randomForest and SVM (Support Vector Machines) to make predictions. It has three functions 1) CelltypeAnnotation 2) LabelHarmonization 3) DeduceRelationship.