50 Open source projects that are alternatives of or similar to BEER

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

SHARP: Single-cell RNA-seq Hyper-fast and Accurate processing via ensemble Random Projection

An unofficial demultiplexing strategy for SPLiT-seq RNA-Seq data

Deep learning model for single-cell inference of multi-omic profiles from a single input modality.

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

A collection of 50+ trajectory inference methods within a common interface 📥📤

Efficient and precise single-cell reference atlas mapping with Symphony

Comparison of dimensionality reduction methods

Demultiplexing pooled scRNA-seq data with or without genotype reference

Spatial alignment of single cell transcriptomic data.

R package to visualize gene expression data based on weighted kernel density estimation

Single cell type annotation guided by cell atlases, with freedom to be queer

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

<<------ Use SnapATAC!!

A tool for fast and accurate summarizing of variant calling format (VCF) files

Simulating single-cell data using gene regulatory networks 📠

A deep learning-based tool for alignment and integration of single cell genomic data across multiple datasets, species, conditions, batches

Imputation method for scRNA-seq based on low-rank approximation

Discover differential transcript usage from polyA-captured single cell RNA-seq data

Clone identification from single-cell data

Examples using Clustergrammer2 to explore high-dimensional datasets.

Pipeline for initial analysis of droplet-based single-cell RNA-seq data

Spatial Transcriptomics Dashboard

Validation and manipulation of FASTQ files, scRNA-seq barcode pre-processing and UMI quantification.

Reference mapping for single-cell genomics

Papers with code for single cell related papers

Reconstruction of complex single-cell trajectories using CellRouter

Clustering scRNAseq by genotypes

Conversion between scRNA-seq objects

Harmony framework for connecting scRNA-seq data from discrete time points

Single-cell Hierarchical Poisson Factorization

cTPnet Package

A tool for semi-automatic cell type annotation

Uniform Manifold Approximation and Projection

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

create "Karpathy's style" 2d images out of your image embeddings

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

This repository contains R code, with which you can create 3D UMAP and tSNE plots of Seurat analyzed scRNAseq data

A Uniform Manifold Approximation and Projection (UMAP) library for Java, developed by Tag.bio in collaboration with Real Time Genomics.

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment

Reliable, scalable and demultiplexing for single-cell RNA sequencing. Improves genotypes with Expectation-Maximization

Single Cell Analysis Pipelines

Bi-order integration (in silico multi-omics data) of single cell RNA sequencing, single cell ATAC sequencing, spacial transcriptomics and CyTOF data

Community-provided extensions to Seurat

A comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.

🧬 Immunarch by ImmunoMind: R Package for Fast and Painless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires