newBASiCS_D_Data

Matrix of dimensions <code>q</code> times <code>n_test</code> whose elements corresponds to the raw expression counts in the test sample. 
First <code>q.bio</code> rows correspond to biological genes. Last <code>q-q.bio</code> rows correspond to technical spike-in genes.

CountsTest

Matrix of dimensions <code>q</code> times <code>n_ref</code> whose elements corresponds to the raw expression counts in the reference sample. 
First <code>q.bio</code> rows correspond to biological genes. Last <code>q-q.bio</code> rows correspond to technical spike-in genes.

CountsRef

Logical vector of length <code>q</code>. If <code>Tech = F</code> the gene is biological; otherwise the gene is spike-in.

Tech

Vector of length <code>q-q.bio</code> whose elements indicate the input number of molecules for the spike-in genes in the test sample (amount per cell).

SpikeInputTest

Vector of length <code>q-q.bio</code> whose elements indicate the input number of molecules for the spike-in genes in the reference sample (amount per cell).

SpikeInputRef

Vector of length <code>n_test</code> indicating experimental batches for test group.

BatchInfoTest

Vector of length <code>n_ref</code> indicating experimental batches for test group.

BatchInfoRef

GeneNames

<code>newBASiCS_D_Data</code> creates a <code><a rd-options="BASiCS" href="/link/BASiCS_D_Data-class?package=BASiCS&version=0.7.30&to=BASiCS" data-mini-rdoc="BASiCS::BASiCS_D_Data-class">BASiCS_D_Data-class</a></code> object from 
expression counts matrices and experimental information about spike-in genes.

Single-cell mRNA sequencing can uncover novel cell-to-cell
heterogeneity in gene expression levels in seemingly homogeneous
populations of cells. However, these experiments are prone to high levels
of unexplained technical noise, creating new challenges for identifying
genes that show genuine heterogeneous expression within the population of
cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing
data) is an integrated Bayesian hierarchical model where: (ii)
cell-specific normalization constants are estimated as part of the model
parameters, (ii) technical variability is quantified based on spike-in
genes that are artificially introduced to each analysed cells lysate and
(iii) the total variability of the expression counts is decomposed into
technical and biological components. BASiCS also provides an intuitive
detection criterion for highly (or lowly) variable genes within the
population of cells under study. This is formalized by means of tail
posterior probabilities associated to high (or low) biological cell-to-cell
variance contributions, quantities that can be easily interpreted by
applied users. Additionally, BASiCS has now been extended to compare gene
expression patterns between pre-specified populations of cells. Unlike traditional
differential expression tools, BASiCS quantifies changes in expression that lie
beyond comparisons of means, also allowing the study of changes in cell-to-cell
heterogeneity. The latter are quantified via a biological over-dispersion
parameter that measures residual over-dispersion (with respect to Poisson sampling)
after normalisation and the removal of technical variation.

Catalina Vallejos

BASiCS

Bayesian Analysis of Single-Cell Sequencing data

newBASiCS_D_Data function

<code>newBASiCS_D_Data</code> creates a <code><a rd-options='BASiCS' href='BASiCS_D_Data-class'>BASiCS_D_Data-class</a></code> object from 
expression counts matrices and experimental information about spike-in genes.

newBASiCS_D_Data: Creates a BASiCS_D_Data object from expression counts matrices and experimental information about spike-in genes

Description

Usage

Arguments

Value

See Also

Examples