abhijeetrpatil Goto Github PK

abhijeetrpatil.github.io

My personal website

adaptive-lasso-with-weights

awesome-single-cell

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

de-analysis

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

examples

hic-pro

HiC-Pro: An optimized and flexible pipeline for Hi-C data processing

hic_data

A (continuously updated) collection of references to Hi-C data. Predominantly human/mouse Hi-C data, with replicates.

hic_tools

A collection of tools for Hi-C data analysis

hiceekr

R package that allows easily performing a complete Hi-C data analysis through a Graphical User Interface

hpap-scrna-seq-workflow-2022

Repository for HPAP-scRNA-seq data analysis

immunedeconv

A unified interface to immune deconvolution methods (CIBERSORT, EPIC, quanTIseq, TIMER, xCell, MCPcounter) and mouse deconvolution methods

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

ml_islets

Code for CRM- ML Isets manuscript

multiomics-single-cell-t1d

Supplementary code for paper

pseudobulk

Cluster-specific pseudo-bulk analysis of 10X single-cell RNA-seq data by connecting Seurat to the VBC RNA-seq pipeline.

rfca

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.

rnaseq

RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

salmonte

SalmonTE is an ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances

scgat

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>

scrna-seq_notes

A list of scRNA-seq analysis tools

scrubletr

scte

scviewer

My single-cell visualization tool

singlecellseq

Batch effects and the effective design of single-cell gene expression studies

sniper

nuclear compartments, subcompartments, nuclear organization, Hi-C, autoencoder

squire

Software for Quantifying Interspersed Repeat Expression

tadbit

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

too-many-cells

Cluster single cells and analyze cell clade relationships with colorful visualizations.

xtea

Comprehensive TE insertion identification with WGS/WES data from multiple sequencing technics