Nikolay Oskolkov

tSNE_vs_UMAP_GlobalStructure. Here we address the global structure preservation by tSNE and UMAP

HowUMAPWorks. Here I explain the math behind UMAP and show how to program it from scratch in Python

Physalia_MLOmicsIntegration_2025. Physalia course Machine Learning for Multi-Omics Integration

DeepLearningSingleCellBiology. Here I show how to use Deep Autoencoders for single cell RNA sequencing data analysis

DeepLearningAncientDNA. Here I show how to use Convolutional Neural Networks (CNNs) for Ancient DNA analysis

NormalizeSingleCell. Comparison of single cell normalization strategies

UMAPDataIntegration. Graph based data integration with UMAP

DeepLearningDataIntegration. Here I show how to use Deep Learning for biological and biomedical Data Integration.

ClusteringHighDimensions. Here I demonstrate how to automatically detect the number of clusters in scRNAseq data

DeepLearningMicrobiome. HTML

LSTMNeanderthalDNA. Implementation of LSTM for detecting regions of Neanderthal introgression in modern human genomes

SupervisedOMICsIntegration. Supervised intehration of CLL data with PLS-DA from DIABLO mixOmics

Physalia_AI_Genomics. This is a repository with the course material for Physalia AI for Genomics course

DimReductSingleCell. Here I cover linear and non-linear dimension reduction techniques for single cell genomics

Physalia_EnvMetagenomics_2025. R

Physalia_AncientMetagenomics_2025. R

DeepLearningClinicalDiagnostics. Here I show how to utilize Bayesian Deep Learning using PyMC3 for making more accurate and safer predictions for biomedical applications

MCWorkflow. Nextflow

Physalia_MLOmics_Barcelona_2025. This repository contains machine learning multiOmics material for the Physalia course in Barcelona on December 15-17 2025

UnivariteVsMultivariteModels. Here we compare a few multivarite and univarite feature selection models

IntegrativeOmicsWorkflow. Here we provide a primer-workflow for biological data integration analysis.

DeepLearningNeanderthalIntrogression. Here I deposite input files and Jupyter notebooks on detecting Neanderthal introgression analysis

GenomicsNewClothes. Here I discuss common pitfalls in Genetics research due to the high-dimensional nature of genetic variation data that suffers from the Curse of Dimensionality

aMeta. R

LMMFromScratch. Deriving and coding Linear Mixed Model (LMM) from scratch

WhyPCALooksTriangular. Here I provide some insights on the peculiar triangular shape of PCA plots that can often be found in Life Science projects

Xgboost-for-scRNAseq. A workflow for applying tree-based machine learning algorithms such as Random forest and Xgboost to scRNAseq data

HowToBatchCorrectSingleCell. Here I explain batch-effects correction techniques for scRNAseq experiments

DeepLearningMicroscopyImaging. Here I demonstrate how to use Faster-RCNN and Mask-RCNN for cell detection using Human Protein Atlas (HPA) digital image data

OsloBioinfoWeek2022. HTML

AdvancedPythonCourse. Material for advanced Python course 2019

Physalia_EnvMetagenomics_2024.

HowToInitializeUMAPtSNE. Checking how tSNE and UMAP depend on different initialization scenarios

aeMeta. Ancient environmental metagenomic workflow

UnsupervisedOMICsIntegration. Multi-OMICs Factor Analysis on scNMT data set

tSNELargePerplexityLimit. Here we investigate the degradation of tSNE to PCA / MDS at large perplexity values

SBW2022. This is a teaching material for scRNAseq workshop within SBW2022

RNAseq_Forensics. A computational method for detecting unwanted tissue signals in RNAseq samples

ML_Computational_Biology. This repository contains the course material for Machine Learning for Computational Biology course

UMAP_VarianceExplained. Here I show a simple way to estimate data variance explained by UMAP and tSNE components

MCManuscript. Microbial Contamination Manuscript

HowLinearMixedModelWorks. HTML

REML. Deriving and coding Linear Mixed Model in Restricted Maximum Likelihood (REML) approach

FeatureSelectionIntegrOMICs. How to us univariate and multivariate feature selection for OMICs integration

COVID19. Corona infection related computations

Is-UMAP-accurate-. Here I provide scripts for reproducing the plots from the Medium blog post "Is UMAP accurate?"

Physalia_MLOmicsIntegration_2026. This is the repository cantaining machine learning multiOmics integration material for the Physalia course

R_course_TARGETWISE_2026. This repository contains the R course material at LIOS within TARGETWISE project

AncientMetagenomics. This repository contains the material for the course in ancient metagenomics