PoisUMINormalizeData: Normalize Data

In tallulandrews/PoissonUMIs: Poisson Modelling of UMI-tagged scRNASeq

Description

Calculates weights based on information contained in each expression value (i.e. difference from null expectation).

Usage

PoisUMI_Normalize_Data(expr_mat, fit=NA)

Arguments

expr_mat	a numeric matrix of raw UMI counts, columns = samples, rows = genes.
fit	output from PoisUMI_Fit_Full_Poisson.

Details

The Poisson model considers each observation to be drawn from a Poisson distribution with parameter:

lambda_ij = m_i * m_j * T * alpha

where

m_i

and

m_j

are the proportion of all molecules detected that are of gene i or in cell j respectively, and T is the total molecules detected across all genes and cells.

alpha

approximates the extent of over-counting of molecules.

This function normalizes expression data as the number of standard deviations from the mean each observation is using the observation-specfic Poisson distribution. Resulting data will contain positive, higher than expected, and negative, lower than expected, values.

If "fit" is not provided it will be automatically calculated using PoisUMI_Fit_Full_Poisson.

Value

A matrix of normalized expression values.

Examples

  library(M3DExampleData)
  fit <- PoisUMI_Fit_Full_Poisson(Mmus_example_list$data)
  norm <- PoisUMI_Normalize_Data(Mmus_example_list$data, fit)

tallulandrews/PoissonUMIs documentation built on May 31, 2019, 2:56 a.m.