https://github.com/bioconductor/tenxbrainanalysis
R scripts for analyzing the 1.3 million brain cell data set from 10X Genomics
https://github.com/bioconductor/tenxbrainanalysis
Last synced: 10 months ago
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R scripts for analyzing the 1.3 million brain cell data set from 10X Genomics
- Host: GitHub
- URL: https://github.com/bioconductor/tenxbrainanalysis
- Owner: Bioconductor
- Created: 2017-12-04T21:19:32.000Z (over 8 years ago)
- Default Branch: devel
- Last Pushed: 2023-03-15T21:02:16.000Z (about 3 years ago)
- Last Synced: 2025-04-28T05:35:05.461Z (about 1 year ago)
- Language: R
- Size: 37.1 KB
- Stars: 12
- Watchers: 6
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Scripts for analyzing the 10X 1.3 million brain cell data
The scripts should be executed in the following order:
- `intro.Rmd`: An introduction, duh.
- `preprocess.Rmd`: Downloading the data and quality control
- `cycle.Rmd`: Cell cycle phase assignment
- `normalize.Rmd`: Calculation of cell-specific size factors
- `variance.Rmd`: Identification of highly variable genes
- `dimred.Rmd`: Dimensionality reduction with randomized PCA
Various output objects will be saved to `objects/`.
A few of these objects are currently hosted at https://drive.google.com/open?id=1_0WbmJ2BriLKlyKEf1Bbb8K0_NwD9rw-.
Note that `sce.rds` does not contain the actual counts or normalized expression values, and requires something like this:
```r
library(TENxBrainData)
tenx <- TENxBrainData()
sce <- readRDS("sce.rds")
tenx <- tenx[,colnames(sce)] # drop 19,672 cells from the raw TENxBrainData
counts(sce) <- counts(tenx) # overwrite inbuilt absolute path
library(scater)
sce <- normalize(sce) # generate normalized expression values
```
The `pics/make_pics.R` scripts will generate the figures used in the paper.