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beeline

BEELINE: evaluation of algorithms for gene regulatory network inference

bioinfor_researchers_atlas

building-mathematics-model-and-machine-learning-algorithm

This lesson is For SCAU-2020 Team to study how to use apply mathematics model in their biological system to perform simulation and test or even drive their ideas for understream design

cellharmony-align

Single-Cell RNA-Seq community alignment function for matching query to reference cells.

cellr

CellR: single-cell RNA-Seq guided deconvolution of cellular composition from bulk-tissue RNA-Seq

cellrank

CellRank for directed single-cell fate mapping

cider

R package CIDER: Meta-Clustering for Single-Cell Data Integration and Evaluation

compbio

computational biology and machine learning

enigma

A fast and accurate deconvolution algorithm based on regularized matrix completion algorithm (ENIGMA)

enigma_0.1

This repo is for testing the new version of ENIGMA

enigma_analysis

enigma_docs

enigma_documentation

enigma_documentation_1

enigma_test

test ENIGMA installation

enigma_test2

test the newest package of ENIGMA

graphlayouts

new layout algorithms for network visualizations in R

icanet

Independent Component Analysis decipher functional modules for better cell clustering and annotation

icanet_external_data

imputation_grn_inference

Project to study the effect of imputation methods on GRN reconstruction in single cell transcriptome data

inductive-matrix-completion

we proposed a type of inductive matrix completion with trace norm regularizer, and presented two practical method for solving trace norm based IMC optimization problem. Using two different tasks and four different benchmark tasks, We showed the max-norm can often be superior to established trace-norm regularization.

liger

R package for integrating and analyzing multiple single-cell datasets

lowpassinghm

Improve your single cell gene expression heatmap with one function

lsi_for_batch_effect_correction

mathmatics-modeling-lecture

motifmatchr

Fast motif matching in R

mousegerm

mousegerm_ddx43

All datasets include analyzed scRNA-seq, peak files called from eCLIP-seq datasets, and spliced/unspliced readouts of single cell. And codes for reproducing main results of study

netid

The pipeline for benchmarking NetID with different imputation methods on any UMI-based scRNA-seq datasets

netid_package

A scalable method to infer fate specific gene regulatory network from single cell gene expression data