FurtherMNN2018

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Code for further development of the mutual nearest neighbours batch correction method, as implemented in the batchelor package.

FurtherMNN2018 최신버젼 다운로드

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# Further MNN algorithm development

## Overview

This repository contains code for further development of the mutual nearest neighbours (MNN) batch correction method, as implemented in the `mnnCorrect` and `fastMNN` functions in the [_batchelor_](https://github.com/LTLA/batchelor) package.
It is based on the code at https://github.com/MarioniLab/MNN2017, which accompanies the paper **Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors** by [Haghverdi _et al. (2018)_](https://doi.org/10.1038/nbt.4091).

- [Description of the theory behind `fastMNN()`](https://marionilab.github.io/FurtherMNN2018/theory/description.html)
- [Discussion of different normalization strategies](https://marionilab.github.io/FurtherMNN2018/theory/normalization.html)
- [Discussion of known failure points of `fastMNN()`](https://marionilab.github.io/FurtherMNN2018/theory/failure.html)
- [Description of the `clusterMNN()` method](https://marionilab.github.io/FurtherMNN2018/theory/clusters.html)

More details on practical usage can be found in the documentation for each function,
as well as in the [OSCA book](https://osca.bioconductor.org) -
specifically in the [Data Integration chapter](https://osca.bioconductor.org/integrating-datasets.html)
and several workflows like the [merged pancreas workflow](https://osca.bioconductor.org/merged-pancreas.html).

## Simulations

To run the simulations, enter the `simulations/` directory and run:

- `cluster_sim.R`, which simulates a variety of scenarios involving orthogonal batch effects.
- `nonorth_sim.R`, which simulates some pathological non-orthogonal batch effects.

## Real data

Three real data analyses are available - `haematopoiesis`, `pancreas` and `droplet`.
Each subdirectory will usually contain:

- `prepare.Rmd`, to download, pre-process and normalize the data.
- `correct.Rmd`, to perform the batch correction and visualize the result with t-SNE plots.

Data file downloads are performed using the [_BiocFileCache_](https://bioconductor.org/packages/BiocFileCache) package to save time and bandwidth.
This will cache the files locally after the initial download, and reuse the cached versions when the script is re-run.

FurtherMNN2018

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