68 Open source projects that are alternatives of or similar to tGPLVM

Processing of single cell RNAseq data for the recovery of TCRs in python

A repository of resources for understanding the concepts of machine learning/deep learning.

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

Single-cell Orientation Tracing

All-in-one analysis pipeline

Discrete Distributional Differential Expression

Uncertainty-aware principal component analysis.

Expression Weighted Celltype Enrichment. See the package website for up-to-date instructions on usage.

A R framework for pipeline benchmarking, with application to single-cell RNAseq

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

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

🕝 Time-warped principal components analysis (twPCA)

In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.

Code for the empty droplet and cell detection project from the HCA Hackathon.

R package for Distance Metric Learning

Streaming, Memory-Limited, r-truncated SVD Revisited!

Minimum-distortion embedding with PyTorch

Federated Principal Component Analysis Revisited!

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

A Python Library for Similarity-based Dimensionality Reduction

Single-cell Hierarchical Poisson Factorization

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

🐟 🔬🦀 alevin-fry is an efficient and flexible tool for processing single-cell sequencing data, currently focused on single-cell transcriptomics and feature barcoding.

Wasserstein Generative Adversarial Network for analysing scRNAseq data

TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses

Local Fisher Discriminant Analysis in R

A Bayesian model for compositional single-cell data analysis

A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

Ensemble topic modelling with pLSA

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

Differential expression analysis for single-cell RNA-seq data.

Estimate dynamic high-order correlations in multivariate timeseries data

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).

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

Spatial Single Cell Analysis in Python

ADENINE: A Data ExploratioN PipelINE

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.

Manuscript for functional prediction of transcriptomic “dark matter” across species

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

Metacell - Single-cell mRNA Analysis

Scripts to run and benchmark scRNA-seq cell cluster labeling methods

CellO: Gene expression-based hierarchical cell type classification using the Cell Ontology

Comparison of dimensionality reduction methods

An online companion to the OSCA manuscript demonstrating Bioconductor resources and workflows for single-cell RNA-seq analysis.

A fast and flexible framework for data reduction in R

Uniform Manifold Approximation and Projection (UMAP) implementation in Julia

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

scLearn：Learning for single cell assignment

Deep Learning based cell composition analysis with Scaden.

Stacked Denoising AutoEncoder based on TensorFlow

Easy genetic ancestry predictions in Python

Single-cell exploratory data analysis for RNA-Seq

CellRank for directed single-cell fate mapping

An R implementation of the SINCERA pipeline for single cell RNA-seq profiling analysis

scisorseqr is an R-package for processing of single-cell long read data and analyzing differential isoform expression across any two conditions

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

No description or website provided.

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.

Parallel Barnes-Hut t-SNE implementation written in Rust.

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.