DAC-class: Differentially Abundant Clusters (DAC) class definition
In tchitchek-lab/SPADEVizR: SPADEVizR: an R package for Visualization, Analysis and Integration of SPADE results
DAC-class R Documentation
Differentially Abundant Clusters (DAC) class definition
Description
The 'DAC' object is a S4 object containing the information related to the differentially abundant clusters between two given biological conditions.
Moreover, this object contains parameters and results used in the statistical analysis.
Details
A cluster is considered as a differentially enriched cluster if its associated p-value and fold-change are below the specific thresholds 'th.pvalue' and 'th.fc'.
The 'print()' and 'show()' can be used to display a summary of this object.
Moreover all information about this object could be saved as a tab separated file using the 'export()' method.
This object is returned by the 'identifyDAC()' function.
Slots
sample.cond1a character specifying the names of the samples of the first biological condition
sample.cond2a character specifying the names of the samples of the second biological condition
cluster.sizea numeric vector containing the number of cells for each cluster
use.percentagesa logical specifying if computation was performed on percentage of cell abundance
methoda character containing the name of the statistical test used to identify the DAC
method.adjusta character containing the name of the multiple correction method used (if any)
method.paireda logical indicating if the statistical test have been performed in a paired manner
th.fca numeric value specifying the fold-change threshold
th.pvaluea numeric value specifying the p-value threshold
resultsa data.frame containing for each cluster (first column): the fold-change (second column) and the standard deviation (third column) for the first biological condition, the fold-change (fourth column) and the standard deviation (fifth column) for the second biological condition, the associated p-value (sixth column) and a logical (seventh column) specifying if the cluster is significantly differentially abundant.
tchitchek-lab/SPADEVizR documentation built on Jan. 27, 2024, 8:58 p.m.
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| DAC-class | R Documentation |
Differentially Abundant Clusters (DAC) class definition
Description
The 'DAC' object is a S4 object containing the information related to the differentially abundant clusters between two given biological conditions. Moreover, this object contains parameters and results used in the statistical analysis.
Details
A cluster is considered as a differentially enriched cluster if its associated p-value and fold-change are below the specific thresholds 'th.pvalue' and 'th.fc'.
The 'print()' and 'show()' can be used to display a summary of this object. Moreover all information about this object could be saved as a tab separated file using the 'export()' method. This object is returned by the 'identifyDAC()' function.
Slots
sample.cond1a character specifying the names of the samples of the first biological condition
sample.cond2a character specifying the names of the samples of the second biological condition
cluster.sizea numeric vector containing the number of cells for each cluster
use.percentagesa logical specifying if computation was performed on percentage of cell abundance
methoda character containing the name of the statistical test used to identify the DAC
method.adjusta character containing the name of the multiple correction method used (if any)
method.paireda logical indicating if the statistical test have been performed in a paired manner
th.fca numeric value specifying the fold-change threshold
th.pvaluea numeric value specifying the p-value threshold
resultsa data.frame containing for each cluster (first column): the fold-change (second column) and the standard deviation (third column) for the first biological condition, the fold-change (fourth column) and the standard deviation (fifth column) for the second biological condition, the associated p-value (sixth column) and a logical (seventh column) specifying if the cluster is significantly differentially abundant.
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