70 Open source projects that are alternatives of or similar to scHPF

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

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

Spatial alignment of single cell transcriptomic data.

Clustering with variational Bayes and population Monte Carlo

Minimum-distortion embedding with PyTorch

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

Online feature-extraction and classification algorithm that learns representations of input patterns.

BEER: Batch EffEct Remover for single-cell data

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

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

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

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

Federated Principal Component Analysis Revisited!

Clone identification from single-cell data

A tool for semi-automatic cell type annotation

Uniform Manifold Approximation and Projection

A Python Library for Similarity-based Dimensionality Reduction

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

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.

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.

Estimate dynamic high-order correlations in multivariate timeseries data

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

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

Scalable sparse Bayesian learning for large CS recovery problems

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

Spatial Transcriptomics Dashboard

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

🕝 Time-warped principal components analysis (twPCA)

Ensemble topic modelling with pLSA

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

Efficient and precise single-cell reference atlas mapping with Symphony

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

cTPnet Package

A curated list of community detection research papers with implementations.

Comparison of dimensionality reduction methods

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)

tGPLVM: A Nonparametric, Generative Model for Manifold Learning with scRNA-seq experimental data

Demultiplexing pooled scRNA-seq data with or without genotype reference

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.

Julia package for automatic Bayesian inference on a factor graph with reactive message passing

R package for Distance Metric Learning

A fast and flexible framework for data reduction in R

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

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).

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

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

ADENINE: A Data ExploratioN PipelINE

Local Fisher Discriminant Analysis in R

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

Easy genetic ancestry predictions in Python

Examples using Clustergrammer2 to explore high-dimensional datasets.

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.

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

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

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

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

Uncertainty-aware principal component analysis.

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