Martin-Fahrenberger / GTestimate

scRNA-seq normalization method, based on the Simple Good-Turing estimator

GTestimate

GTestimate is a scRNA-seq normalization method. In contrast to other methods it uses the Simple Good-Turing estimator for the per cell relative gene expression estimation. Thereby GTestimate can account for the unobserved genes and avoid overestimation of the observed genes. At default settings it serves as a drop-in replacement for Seurat's NormalizeData().

Installation

You can install the development version of GTestimate like so:

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
if (!requireNamespace("sparseMatrixStats", quietly = TRUE))
  devtools::install_github("const-ae/sparseMatrixStats")

devtools::install_github("Martin-Fahrenberger/GTestimate")

Seurat Example

This is a basic example of how to use GTestimate to normalize scRNA-seq data in a Seurat workflow.

library(GTestimate)
library(Seurat)
data('pbmc_small')

pbmc_small <- GTestimate(pbmc_small) # Instead of NormalizeData(pbmc_small)
pbmc_small <- FindVariableFeatures(pbmc_small)
pbmc_small <- ScaleData(pbmc_small)
pbmc_small <- RunPCA(pbmc_small)
# and so on

SingleCellExperiment Example

This is a basic example of how to use GTestimate to normalize scRNA-seq data in a SingleCellExperiment workflow using size-factors.

library(GTestimate)
library(Seurat)
library(scran)
data('pbmc_small')

pbmc_sce <- as.SingleCellExperiment(pbmc_small)

pbmc_sce <- computeSumFactors(pbmc_sce)
pbmc_sce <- GTestimate(pbmc_sce, size.factors = sizeFactors(pbmc_sce)) # Instead of logNormCounts(sce)
# and so on

Credit

The core implementation of the Simple Good-Turing estimator in C++ has been adapted from Aaron Lun's implementation for the edgeR R-package. His implementation was in turn based on Geoffrey Sampson's C code acessible at https://www.grsampson.net/D_SGT.c