Variance Stabilizing Transformations for Single Cell UMI Data
Clip matrix values to specified range
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Compare gene expression between two groups
Correct data by setting all latent factors to their median values and ...
Correct data by setting all latent factors to their median values and ...
Find differentially expressed genes that are conserved across samples
Non-parametric differential expression test for sparse non-negative da...
Generate data from regularized models.
Extract model formula from model string
Return average variance under negative binomial model
Get median of non zero UMIs from a count matrix
Return variance of residuals of regularized models
Return Pearson or deviance residuals of regularized models
Identify outliers
Convert a given matrix to dgCMatrix
Plot estimated and fitted model parameters
Plot observed UMI counts and model
Prepare regressor data from vst object and cell attributes
Robust scale using median and mad per bin
Robust scale using median and mad
Geometric mean per row
Variance per row
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Smooth data by PCA
Quantile normalization of cell-level data to match typical UMI count d...
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Variance stabilizing transformation for UMI count data
A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija (2019) <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2022) <doi:10.1186/s13059-021-02584-9> for more details.
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