abhijeetrpatil Goto Github PK
Name: ABHIJEET R PATIL
Type: User
Name: ABHIJEET R PATIL
Type: User
My personal website
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
HiC-Pro: An optimized and flexible pipeline for Hi-C data processing
A (continuously updated) collection of references to Hi-C data. Predominantly human/mouse Hi-C data, with replicates.
A collection of tools for Hi-C data analysis
R package that allows easily performing a complete Hi-C data analysis through a Graphical User Interface
Repository for HPAP-scRNA-seq data analysis
A unified interface to immune deconvolution methods (CIBERSORT, EPIC, quanTIseq, TIMER, xCell, MCPcounter) and mouse deconvolution methods
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Code for CRM- ML Isets manuscript
Supplementary code for paper
Cluster-specific pseudo-bulk analysis of 10X single-cell RNA-seq data by connecting Seurat to the VBC RNA-seq pipeline.
R package: {rfca} Random forest-based cell annotation methods for scRNAseq analysis. {rfca} contains methods which identifies cell types using machine learning trained on a diversity of cell types, without the need for a labelled training dataset. It also allows you to train your own cell prediction models with your own labels (cell type, subtype, cell state, cluster number etc). This package is best suited for researchers who want to annotate their datasets in a quick and unbiased way, phenotype their datasets based on cell identity proportions, and discover common cell states across different datasets and disease models.
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
SalmonTE is an ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances
Code to reproduce results in Ravindra NG* & Sehanobish A* et al. 2020, "Disease State Prediction From Single-Cell Data Using Graph Attention Networks" to appear in ACM Proceedings. <https://arxiv.org/abs/2002.07128>
A list of scRNA-seq analysis tools
My single-cell visualization tool
Batch effects and the effective design of single-cell gene expression studies
nuclear compartments, subcompartments, nuclear organization, Hi-C, autoencoder
Software for Quantifying Interspersed Repeat Expression
TADbit is a complete Python library to deal with all steps to analyze, model and explore 3C-based data. With TADbit the user can map FASTQ files to obtain raw interaction binned matrices (Hi-C like matrices), normalize and correct interaction matrices, identify and compare the so-called Topologically Associating Domains (TADs), build 3D models from the interaction matrices, and finally, extract structural properties from the models. TADbit is complemented by TADkit for visualizing 3D models
Cluster single cells and analyze cell clade relationships with colorful visualizations.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.