NikolayOskolkov/FeatureSelectionIntegrOMICs

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NikolayOskolkov/FeatureSelectionIntegrOMICs

NikolayOskolkov

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How to us univariate and multivariate feature selection for OMICs integration

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

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

ClusteringHighDimensions

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

DeepLearningDataIntegration

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

UMAPDataIntegration

Graph based data integration with UMAP

NormalizeSingleCell

Comparison of single cell normalization strategies

DeepLearningMicrobiome

LSTMNeanderthalDNA

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

DimReductSingleCell

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

DeepLearningClinicalDiagnostics

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

SupervisedOMICsIntegration

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

UnivariteVsMultivariteModels

Here we compare a few multivarite and univarite feature selection models

DeepLearningMicroscopyImaging

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

LMMFromScratch

Deriving and coding Linear Mixed Model (LMM) from scratch

AdvancedPythonCourse

Material for advanced Python course 2019

Jupyter Notebook

aMeta

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

tSNELargePerplexityLimit

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

OsloBioinfoWeek2022

UMAP_VarianceExplained

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

HowToBatchCorrectSingleCell

Here I explain batch-effects correction techniques for scRNAseq experiments

SBW2022

This is a teaching material for scRNAseq workshop within SBW2022

HowToInitializeUMAPtSNE

Checking how tSNE and UMAP depend on different initialization scenarios

WhyPCALooksTriangular

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

UnsupervisedOMICsIntegration

Multi-OMICs Factor Analysis on scNMT data set

IntegrativeOmicsWorkflow

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

COVID19

Corona infection related computations

Jupyter Notebook

REML

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

HowLinearMixedModelWorks