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

Science Score: 23.0%

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bioconductor-package human-cell-atlas core-package dimension-reduction feature-extraction gene-expression shiny single-cell transcriptomics bioconductor
Last synced: 11 months ago · JSON representation

Repository

R scripts for analyzing the 1.3 million brain cell data set from 10X Genomics

Basic Info
  • Host: GitHub
  • Owner: Bioconductor
  • Language: R
  • Default Branch: devel
  • Size: 37.1 KB
Statistics
  • Stars: 12
  • Watchers: 6
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Created over 8 years ago · Last pushed over 3 years ago
Metadata Files
Readme

README.md

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=10WbmJ2BriLKlyKEf1Bbb8K0NwD9rw-. 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.

Owner

  • Name: Bioconductor
  • Login: Bioconductor
  • Kind: organization

Software for the analysis and comprehension of high-throughput genomic data

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