sCVdata-class: Analysis results for the scClustViz app

In BaderLab/scClustViz: Differential Expression-based scRNAseq Cluster Assessment and Viewing

Analysis results for the scClustViz app

Description

An S4 class to store analysis results for the scClustViz app. sCVdata objects should be generated using the function CalcSCV, which at minimum populates the slots Clusters, ClustGeneStats, and params. The remaining slots can be optionally populated - see slot entries for details. For efficiency, multiple sCVdata objects can be generated for multiple cluster solutions using the single command CalcAllSCV.

Usage

## S4 method for signature 'sCVdata'
Clusters(sCVd)

## S4 replacement method for signature 'sCVdata'
Clusters(sCVd) <- value

## S4 method for signature 'sCVdata'
ClusterNames(sCVd)

## S4 replacement method for signature 'sCVdata'
ClusterNames(sCVd) <- value

## S4 method for signature 'sCVdata'
ClusterColours(sCVd)

## S4 replacement method for signature 'sCVdata'
ClusterColours(sCVd) <- value

## S4 method for signature 'sCVdata'
ClustGeneStats(sCVd)

## S4 replacement method for signature 'sCVdata'
ClustGeneStats(sCVd) <- value

## S4 method for signature 'sCVdata'
DEvsRest(sCVd)

## S4 replacement method for signature 'sCVdata'
DEvsRest(sCVd) <- value

## S4 method for signature 'sCVdata'
DEcombn(sCVd)

## S4 replacement method for signature 'sCVdata'
DEcombn(sCVd) <- value

## S4 method for signature 'sCVdata'
Silhouette(sCVd)

## S4 replacement method for signature 'sCVdata'
Silhouette(sCVd) <- value

## S4 method for signature 'sCVdata'
Param(sCVd, param)

Methods (by generic)

• Clusters: Access Clusters slot

• Clusters<-: Assign Clusters slot

• ClusterNames: Access Clusters slot

• ClusterNames<-: Assign Clusters slot

• ClusterColours: Access Clusters slot

• ClusterColours<-: Assign Clusters slot

• ClustGeneStats: Access ClustGeneStats slot

• ClustGeneStats<-: Assign ClustGeneStats slot

• DEvsRest: Access DEvsRest slot

• DEvsRest<-: Assign DEvsRest slot - see slot details

• DEcombn: Access DEcombn slot

• DEcombn<-: Assign DEcombn slot - see slot details

• Silhouette: Access Silhouette slot

• Silhouette<-: Assign Silhouette slot

• Param: Access Param slot (see sCVparams)

Slots

Clusters

A named factor representing cluster assignments for every cell. Accessed with Clusters. Length should be equal to number of cells in input data, and names should match colnames of gene expression matrix (generally cell barcodes). Levels can be cluster numbers or cluster names.

ClustGeneStats

A named list of data frames, one entry for each level in Clusters (with corresponding name). Accessed with ClustGeneStats. Each entry is data frame containing gene summary statistics for the cluster. Each data frame has the same number of rows as the input gene expression matrix, where each row represents the results for that gene (and shares its name). The three variables in the data frame are summary statistics. Detection Rate (DR) refers to the proprotion of cells in the cluster in which gene cDNA was detected (a gene expression value > 0). Mean Detected Gene Expression (MDGE) refers to the mean normalized gene expression in cells from the cluster in which the gene was detected (i.e. non-zero mean). Mean Gene Expression (MGE) is the mean normalized gene expression of the gene in the cluster.

DEvsRest

A named list of data frames, one entry for each level in Clusters (with corresponding name). Accessed with DEvsRest. Each entry is data frame containing gene differential expression stats when comparing the cells of that cluster to all other cells in the input data, as calculated by CalcDEvsRest. Rows represent genes, and variables must include logGER (an effect size measure: gene expression ratio in log space, often referred to as logFC) and FDR (significance measure: false discovery rate). This slot can be populated using the function CalcDEvsRest, which can either calculate differential expression for all clusters, or take an appropriately formatted list of precomputed values. See CalcDEvsRest documentation for details.

DEcombn

A named list of data frames, one entry for each combination of pairs of levels in Clusters (named as ClusterA-ClusterB). Accessed with DEcombn. Each entry is a data frame containing gene differential expression stats when comparing the two clusters, as calculated by CalcDEvsCombn. Rows represent genes, and variables must include logGER (an effect size measure: gene expression ratio in log space, often referred to as logFC) and FDR (significance measure: false discovery rate). This slot can be populated using the function CalcDEvsCombn, which can either calculate differential expression for all combinations of clusters, or take an appropriately formatted list of precomputed values. See CalcDEvsCombn documentation for details.

Silhouette

An object of class Silhouette defining a cluster cohesion/separation statistic (silhouette width) for every cell. Accessed with Silhouette. This slot can be populated using the function CalcSilhouette, which is a wrapper to silhouette with distance calcuated using the same reduced dimensional cell embedding as was used for clustering (see Param(sCVdata,"DRforClust")).

params

An object of class sCVparams containing the parameters relevant to the data in this object. Accessed with Param. Slots are accessed using Param(sCVdata,slotName).

BaderLab/scClustViz documentation built on Sept. 10, 2023, 11:51 p.m.