ReginaldoAllves
Postdoctoral fellow at USC and Caltech. PhD in Cancer Genetics. MBA in Data Science. PostGrad Certificate in Machine Learning Engineering
University of Southern California (USC)United States
Pinned Repositories
ReginaldoAllves's Repositories
ReginaldoAllves/awesome-deep-learning-single-cell-papers
ReginaldoAllves/awesome-single-cell
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
ReginaldoAllves/azimuth
A Shiny web app for mapping datasets using Seurat v4
ReginaldoAllves/CellAnnotationTutorial
Accompanying code for the tutorial: Annotating single cell transcriptomic maps using automated and manual methods
ReginaldoAllves/CellChat
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
ReginaldoAllves/cellsnake
Cellsnake tool main repo
ReginaldoAllves/celltypist
A tool for semi-automatic cell type classification
ReginaldoAllves/fgsea
Fast Gene Set Enrichment Analysis
ReginaldoAllves/harmony
Fast, sensitive and accurate integration of single-cell data with Harmony
ReginaldoAllves/knowledgebase
recipes that save time
ReginaldoAllves/Metastasis.MMR.status
ReginaldoAllves/MultiK
MultiK is a data-driven tool that objectively assesses the optimal number(s) of clusters based on the concept of consensus clustering via a multi-resolution perspective.
ReginaldoAllves/nichenetr
NicheNet: predict active ligand-target links between interacting cells
ReginaldoAllves/OmnipathR
R client for the OmniPath web service
ReginaldoAllves/sc-type
ReginaldoAllves/scCATCH
Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
ReginaldoAllves/scImmuCC
Hierarchical annotation of immune cells n scRNA-Seq data based on ssGSEA algorithm.
ReginaldoAllves/SCISSORS
SCISSORS builds upon the Louvain graph-based clustering in Seurat by optimizing parameter selection when reclustering cell groups, with an eye towards identifying rare cell types.
ReginaldoAllves/scMiko
R package developed for single-cell RNA-seq analysis. It was designed using the Seurat framework, and offers existing and novel single-cell analytic work flows.
ReginaldoAllves/scPipeline
Single-cell analytic toolbox that offers modular workflows for multi-level cellular annotation and user-friendly analysis reports
ReginaldoAllves/scrna-workflow
scRNA workflow of Cellsnake
ReginaldoAllves/scTab
ReginaldoAllves/seurat
R toolkit for single cell genomics
ReginaldoAllves/seurat-wrappers
Community-provided extensions to Seurat
ReginaldoAllves/SingleCell_RNASeq_Jan24
Repo for January 2024 iteration of course
ReginaldoAllves/singleCellNet
SingleCellNet: classify single cells across species and platforms
ReginaldoAllves/TCC-MBA-DSA-USP-ESALQ
ReginaldoAllves/TCR-BCR-seq-analysis
T/B cell receptor sequencing analysis notes
ReginaldoAllves/TOSICA
Transformer for One-Stop Interpretable Cell-type Annotation
ReginaldoAllves/UpSetR
An R implementation of the UpSet set visualization technique published by Lex, Gehlenborg, et al..