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https://github.com/yanwu2014/swne
Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
https://github.com/yanwu2014/swne
bioinformatics data-visualization dimensionality-reduction nonnegative-matrix-factorization single-cell-analysis single-cell-atac-seq single-cell-genomics single-cell-rna-seq statistical-methods
Last synced: 5 days ago
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Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
- Host: GitHub
- URL: https://github.com/yanwu2014/swne
- Owner: yanwu2014
- License: bsd-3-clause
- Created: 2018-01-03T21:15:10.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2023-08-02T22:57:11.000Z (over 1 year ago)
- Last Synced: 2024-08-02T16:46:45.917Z (3 months ago)
- Topics: bioinformatics, data-visualization, dimensionality-reduction, nonnegative-matrix-factorization, single-cell-analysis, single-cell-atac-seq, single-cell-genomics, single-cell-rna-seq, statistical-methods
- Language: R
- Size: 211 MB
- Stars: 103
- Watchers: 5
- Forks: 20
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SWNE
Similarity Weighted Nonnegative Embedding (SWNE), is a method for visualizing high dimensional datasets.
SWNE uses Nonnegative Matrix Factorization to decompose datasets into latent factors, projects
those factors onto 2 dimensions, and embeds samples and key features in 2 dimensions relative to the
factors. SWNE can capture both the local and global dataset structure, and allows
relevant features to be embedded directly onto the visualization, helping with biological interpretation.
If you use SWNE in your research, please cite [Wu et al, Cell Systems, 2018](https://doi.org/10.1016/j.cels.2018.10.015)
## Installation
Run the following code to install the package using devtools:
```
if(!require(remotes)){ install.packages("remotes") # If not already installed; }
remotes::install_github("linxihui/NNLM")
remotes::install_github("yanwu2014/swne")
```
If you want to run SWNE on chromatin accessibility data, install [cisTopic](github.com/aertslab/cisTopic) as well.
```
devtools::install_github("aertslab/RcisTarget")
devtools::install_github("aertslab/AUCell")
devtools::install_github("aertslab/cisTopic")
```
## Key Updates
*(10/21/2019): Improve SWNE embeddings by using PAGA graphs to prune the SNN graph. Update factor embedding distance function.
*(09/19/2019): The wrapper function `RunSWNE` now works on integrated Seurat datasets
*(05/15/2019): Updated all code and vignettes for Seurat V3 objects. Removed C1/snDropSeq projection vignette since it's easier to use Seurat data integration (or CONOS)
## Gene Expression Quickstart with Seurat
Download the example [Seurat object](https://bit.ly/2W3pT7k) which contains single cell RNA-seq profiles of 3000 PBMCs
```
## Load object
obj <- readRDS("Data/pbmc3k_final.RObj")
## Extract clusters
clusters <- obj$seurat_clusters
## Select genes to embed
genes.embed <- c("MS4A1", "GNLY", "CD3E", "CD14",
"FCER1A", "FCGR3A", "LYZ", "PPBP", "CD8A")
## Run SWNE
swne.embedding <- RunSWNE(obj, k = 16, genes.embed = genes.embed)
## Plot SWNE
PlotSWNE(swne.embedding, alpha.plot = 0.4, sample.groups = clusters,
do.label = T, label.size = 3.5, pt.size = 1.5, show.legend = F,
seed = 42)
```
## Chromatin Accessibility Quickstart with cisTopic
SWNE's chromatin accessibility visualizations currently only work with [cisTopic]((https://github.com/aertslab/cisTopic)), a great method by [Gonazalez-Blas et al](https://www.nature.com/articles/s41592-019-0367-1) that uses LDA to decompose scATAC or scTHS datasets. Download the example [cisTopic object](https://bit.ly/2HnClXK) which contains single cell THS-seq profiles of 14,535 human brain cells from [Lake, Sos, Chen et al, NBT, 2017](https://www.nature.com/articles/nbt.4038).
```
library(cisTopic)
library(swne)
library(org.Hs.eg.db)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
## Load data
cisTopicObject <- readRDS("Data/adult-vCTX_cisTopic.RObj")
## Pull out clusters
clusters <- cisTopicObject@other$clusters
## Annotate regions
cisTopicObject <- getRegionsScores(cisTopicObject)
cisTopicObject <- annotateRegions(cisTopicObject, txdb = TxDb.Hsapiens.UCSC.hg38.knownGene,
annoDb = "org.Hs.eg.db")
## Run SWNE embedding
swne.emb <- RunSWNE(cisTopicObject, alpha.exp = 1.25, snn.exp = 1, snn.k = 30)
## Embed genes based on promoter accessibility
marker.genes <- c("CUX2", "RORB", "FOXP2", "FLT1", "GAD1", "SST", "SLC1A2", "MOBP", "P2RY12")
swne.emb <- EmbedPromoters(swne.embedding, cisTopicObject, genes.embed = marker.genes,
peaks.use = NULL, alpha.exp = 1, n_pull = 3)
PlotSWNE(swne.emb, sample.groups = clusters, pt.size = 0.5, alpha.plot = 0.5, do.label = T,
seed = 123)
```
## scRNA-seq Walkthroughs and examples
Since SWNE is primarily meant for visualization and interpretation of the data, we typically use either [Seurat](http://satijalab.org/seurat/) or [Pagoda2](https://github.com/hms-dbmi/pagoda2) as a primary scRNA-seq pipeline. All the R markdown files used to generate the walkthroughs can be found under the Examples/ directory.
1. A basic [walkthrough](https://yanwu2014.github.io/swne/Examples/pbmc3k_swne_seurat.html) using SWNE to visualize 3k PBMC cells starting from a pre-computed Seurat object
2. A basic [walkthrough](https://yanwu2014.github.io/swne/Examples/pbmc3k_swne_pagoda2.html) using SWNE to visualize 3k PBMC cells starting from a pre-computed Pagoda2 object
3. A basic [walkthrough](https://yanwu2014.github.io/swne/Examples/pbmc3k_swne_matrix.html) using SWNE to visualize 3k PBMC cells starting from a counts matrix
4. A [walkthrough](https://yanwu2014.github.io/swne/Examples/Han_hemato_swne.html) benchmarking SWNE against UMAP and SWNE using a large mouse hematopoiesis data from the [Mouse Cell Atlas](http://bis.zju.edu.cn/MCA/). The walkthrough was based of an analysis done by the UMAP authors in Figure 2 of their UMAP paper [Becht, McInnes et al, NBT, 2019](https://www.nature.com/articles/nbt.4314).
5. A [walkthrough](https://yanwu2014.github.io/swne/Examples/hemato_swne.html) demonstrating SWNE on a hematopoiesis dataset and comparing SWNE against other embeddings including t-SNE and UMAP (recreating Figure 2 from our Cell Systems paper).
6. A [walkthrough](https://yanwu2014.github.io/swne/Examples/zika_swne.html) demonstrating SWNE on a single-cell [study](https://elifesciences.org/articles/32942) looking at Zika replication across time in a hepatoma cell line.
7. A [walkthrough](https://yanwu2014.github.io/swne/Examples/multiple_pancreas_alignment_swne.html) using SWNE to visualize four pancreas datasets that have undergone alignment with Seurat V3's [data integration](https://doi.org/10.1016/j.cell.2019.05.031).
## scATAC/THS-seq Walkthroughs and examples
Since SWNE is primarily meant for visualization and interpretation of the data, we typically use either [cisTopic](http://satijalab.org/seurat/) as a primary pipeline
1. A [walkthrough](https://yanwu2014.github.io/swne/Examples/scATAC_hemato_swne.html) using SWNE to visualize a hematopoiesis trajectory that illustrates how to embed both the promoter accessibility of genes, and the binding site accessibility of transcription factors.
## Recreating Figures
To recreate the figures from our preprint, see the `Scripts/` directory.
To generate the simulated discrete and trajectory datasets, use `splatter_generate.R`. The simulated datasets we generated can be found [here](https://bit.ly/2JQDDNc)
To generate the visualizations and embedding evaluations, run `splatter_discrete_swne.R` and `splatter_trajectory_swne.R` for the discrete and trajectory simulations, respectively. To benchmark SWNE runtimes, use `splatter_runtime_analysis.R`.
The data needed to run `hemato_swne.R` can be found [here](https://bit.ly/2MFiByO) and the raw data for the hematopoietic cells can be found, courtesy of the monocle2 developers, [here](http://www.gs.washington.edu/~xqiu/proj2/RGE_analysis_data.tar.gz). The `hemato_swne.R` script is also available as a [SWNE walkthrough](https://yanwu2014.github.io/swne/Examples/hemato_swne.html).
The data needed to run `snDropSeq_swne.R` on the cerebellar and visual cortex data can be found [here](https://bit.ly/2I6R5XL) and the raw data can be found at the GEO accession GSE97930.
The raw PBMC dataset can be found at the 10X genomics [website](https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k).