| Title: | Tool for Evaluation of Cell Identity from Transcription Profiles |
|---|---|
| Description: | The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. |
| Authors: | Nancy Mah [aut, cre], Katerina Taskova [aut], Justin Marsh [aut] |
| Maintainer: | Nancy Mah <[email protected]> |
| License: | GPL-3 |
| Version: | 1.27.0 |
| Built: | 2024-11-18 03:10:32 UTC |
| Source: | https://github.com/bioc/CellScore |
This function will generate a horizontal barplot of the OnOff scores of test cell types, as defined by the eset$sub_cell_type1 column of the input dataset. Note that if the cell types as provided in the second argument (score data frame as produced by the function OnOff, are not matching the phenotype of the input dataset, the function will return an error.
BarplotOnOff(eset, group.score)BarplotOnOff(eset, group.score)
eset |
an ExpressionSet containing data matrices of normalized expression data, present/absent calls, a gene annotation data.frame and a phenotype data.frame. |
group.score |
a data frame with cell type specific on/off scores as generated by the OnOff function. |
This function returns a list of two objects, as follows:
GroupComparisonsForPlot |
an ordered data.frame of on/off scores, |
OnOffBarplotData |
a data frame of marker gain/loss and aditional features, used for making the plot. |
OnOff for details on on/off score
calculations, and hgu133plus2CellScore for details
on the specific expressionSet object that should be provided as an input.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate a marker list group.OnOff <- OnOff(eset, cell.change, out.put="marker.list") ## Calculate on/off score for individual samples individ.OnOff <- OnOff(eset, cell.change, out.put="individual") ## Plot pyramid bar plot of on/off scores BarplotOnOff(eset, group.OnOff$scores) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate a marker list group.OnOff <- OnOff(eset, cell.change, out.put="marker.list") ## Calculate on/off score for individual samples individ.OnOff <- OnOff(eset, cell.change, out.put="individual") ## Plot pyramid bar plot of on/off scores BarplotOnOff(eset, group.OnOff$scores) }
This function will plot a boxplot of the CellScore values for each selected transition (defined in the cell.change data frame). The function will only plot the scores for the test samples of valid subtypes (as annotated by cellscore$sub_cell_type1). Scores for the standards are not included. Note that if a subtype is specified by two different transitions, the coresponding scores will be plotted in both transitions.
BoxplotCellScore(cellscore, cell.change)BoxplotCellScore(cellscore, cell.change)
cellscore |
a data.frame of CellScore values as calculated by CellScore(). |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data frame describes one transition from the start to a target cell type. |
Invisibly, it returns list of the CellScore values by groups (in the same order as on the plot)
CellScore for details on CellScore
calculation.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Make the boxplot of CellScore values BoxplotCellScore(cellscore, cell.change) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Make the boxplot of CellScore values BoxplotCellScore(cellscore, cell.change) }
This function will calculate the CellScore (summary score) for a cell that is undergoing a transition in cell identity from a starting cell type to a target cell type. 'transitions' is mandatory, and either 'data' or all three of 'scores.onoff', 'scores.cosine' and 'pdata' are as well. If you provide 'data', then 'scores.onoff', 'scores.cosine' and 'pdata' will override the respective calculations only.
CellScore( transitions, data = NULL, scores.onoff = NULL, scores.cosine = NULL, pdata = NULL )CellScore( transitions, data = NULL, scores.onoff = NULL, scores.cosine = NULL, pdata = NULL )
transitions |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data. frame describes one transition from the start to a target cell type. |
data |
a SummarizedExperiment or ExpressionSet containing data matrices of normalized expression data, present/absent calls, a gene annotation data frame and a phenotype data frame. |
scores.onoff |
a data.frame of OnOff Scores for all samples in the expression matrix as generated by the function OnOff(). |
scores.cosine |
a numeric matrix of cosine similarity between general groups, subgroups and individual samples as calculated by the function CosineSimScore(). |
pdata |
a data frame with samples as rows, variables as columns. |
The function returns a data frame with 29 columns and M*N rows, where M is the number of unqiue start and target cell types pairs listed in the cell.change argument, while N is the number of all samples in the input dataset eset. The columns include sample phenotype features and all score (components), including the on/off score, cosine similarity and CellScore.
CosineSimScore, OnOff for
details on specfic score calculations, and
hgu133plus2CellScore for details on the
specific expressionSet object that shoud be provided as an input.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(cell.change, data = eset.sub, scores.onoff = individ.OnOff$scores, scores.cosine = cs$cosine.samples) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(cell.change, data = eset.sub, scores.onoff = individ.OnOff$scores, scores.cosine = cs$cosine.samples) }
This function will generates a CellScore report for each study and transition that can be saved as a pdf. The report includes: 1) scatterplot of the donor-like and target-like scores of relevant test samples and the standards; 2) a density plot of the test and standard cellscores; 3) a rugplot of the cellscores, focussing on the test samples; 4) a heatmap of the OnOff Marker genes for all standards and test samples.
CellScoreReport(cellscore, cell.change, marker.genes, eset)CellScoreReport(cellscore, cell.change, marker.genes, eset)
cellscore |
a data.frame of CellScore values, as calculated by CellScore(). |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data frame describes one transition from the start to a target cell type. |
marker.genes |
a data.frame of marker genes as generated by function OnOff() |
eset |
an ExpressionSet containing data matrices of normalized expression data, present/absent calls, a gene annotation data.frame and a phenotype data.frame. |
This function outputs the plots on the active graphical device and returns invisibly NULL.
CellScore for details on CellScore, and
hgu133plus2CellScore for details on the
specific ExpressionSet object that shoud be provided as an input.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Generate the group on/off scores for the combined data group.OnOff <- OnOff(eset.sub, cell.change, out.put="marker.list") ## Make a report and save it the current working directory pdf("TestReport.pdf", width=8, height=12) CellScoreReport(cellscore, cell.change, group.OnOff$markers, eset.sub) dev.off() }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Generate the group on/off scores for the combined data group.OnOff <- OnOff(eset.sub, cell.change, out.put="marker.list") ## Make a report and save it the current working directory pdf("TestReport.pdf", width=8, height=12) CellScoreReport(cellscore, cell.change, group.OnOff$markers, eset.sub) dev.off() }
This function calculates the cosine similarity for cell transitions.
CosineSimScore(eset, cell.change, iqr.cutoff = 0.1)CosineSimScore(eset, cell.change, iqr.cutoff = 0.1)
eset |
an ExpressionSet containing data matrices of normalized expression data, present/absent calls, a gene annotation data frame and a phenotype data frame. |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data frame describes one transition from the start to a target cell type. |
iqr.cutoff |
set the threshold for top most variable genes which should be included for the cosine similarity calculation. Default is the top 10 genes, expressed as a fraction. All samples that are annotated as standards will be used for the iqr calculation. |
This function returns a list of five objects, as follows:
the phenotype data frame describing the standard samples
the expression value matrix, as filtered by IQR threshold
a numeric matrix of cosine similarity between the centroids of all groups defined by eset@general_cell_types
a numeric matrix of cosine similarity between the centroids of all gsubroups defined by eset@sub_cell_types1
a numeric matrix of cosine similarity between general groups, subgroups and individual samples.
hgu133plus2CellScore for details on the
specific ExpressionSet object that shoud be provided as an input.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) }
This function extracts the values of the CellScore for all the test samples of a given set of (valid) cell transition. While it can be used as standalone, it serves as an internal function for several other CellScore functions.
extractTransitions(cellscore, cell.change)extractTransitions(cellscore, cell.change)
cellscore |
a data.frame of CellScore values as calculated by the function CellScore(). |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data. frame describes one transition from the start to a target cell type. |
This function returns a data frame with the same columns as the input data frame cellscore, extended with additional column that is used as a single identifier of each valid cell transition. Technically, the output is subselection of the input data frame.
CellScore for details on CellScore
calcualtion.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Extract the scores for the transitions given in cell.change cellscore.cc <- extractTransitions(cellscore, cell.change) ## View the sub_cell_type1 in the extracted object, it should be the same ## as the test cell types named in cell.change table(cellscore.cc$sub_cell_type1) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Extract the scores for the transitions given in cell.change cellscore.cc <- extractTransitions(cellscore, cell.change) ## View the sub_cell_type1 in the extracted object, it should be the same ## as the test cell types named in cell.change table(cellscore.cc$sub_cell_type1) }
This function is called by CellScoreReport to make a heatmap of the standards (donor and target) marker genes and the test samples for the defined transition, as generated by the OnOff() function. Gene symbols are not plotted as this is only intended as an overview of marker expression in test samples.
heatmapOnOffMarkers(test.data, markergenes, pdata, calls)heatmapOnOffMarkers(test.data, markergenes, pdata, calls)
test.data |
a data.frame of CellScore values as calculated by CellScore(), for only a group of test samples. |
markergenes |
a data.frame of marker genes, as calculated by OnOff(). |
pdata |
a data.frame containing the phenotype of the expression dataset. |
calls |
a matrix containing the present/absent calls where genes are in rows and samples in columns. |
This function returns invisibly the visualised binary matrix of the absence/presence of the cell type markers (rows) across the samples (columns) in the given study.
CellScore for details on CellScore, and
hgu133plus2CellScore for details on the
specific ExpressionSet object that shoud be provided as an input.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Get the CellScore fvalues rom valid transitions defined by cell.change ## table plot.data <- extractTransitions(cellscore, cell.change) ## Define a plot group variable plot.data$plot_group <- paste(plot.data$experiment_id, plot.data$cxkey.subcelltype, sep="_") ## Sort the scores 1) by target 2) by donor 3) by study plot.data.ordered <- plot.data[order(plot.data$target, plot.data$donor_tissue, plot.data$experiment_id), ] ## How many plot_groups are there? table(plot.data$plot_group) ## pick one plot_group to plot group <- unique(plot.data$plot_group)[4] ## Select scores for only one plot group test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ] ## Generate the group on/off scores for the combined data group.OnOff <- OnOff(eset.sub, cell.change, out.put="marker.list") calls <- assayDataElement(eset.sub, "calls") rownames(calls) <- if("feature_id" %in% names(fData(eset.sub))) { fData(eset.sub)[, "feature_id"] } else { fData(eset.sub)[, "probe_id"] } ## Plot heatmapOnOffMarkers(test.data, group.OnOff$markers, pData(eset.sub), calls) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Get the CellScore fvalues rom valid transitions defined by cell.change ## table plot.data <- extractTransitions(cellscore, cell.change) ## Define a plot group variable plot.data$plot_group <- paste(plot.data$experiment_id, plot.data$cxkey.subcelltype, sep="_") ## Sort the scores 1) by target 2) by donor 3) by study plot.data.ordered <- plot.data[order(plot.data$target, plot.data$donor_tissue, plot.data$experiment_id), ] ## How many plot_groups are there? table(plot.data$plot_group) ## pick one plot_group to plot group <- unique(plot.data$plot_group)[4] ## Select scores for only one plot group test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ] ## Generate the group on/off scores for the combined data group.OnOff <- OnOff(eset.sub, cell.change, out.put="marker.list") calls <- assayDataElement(eset.sub, "calls") rownames(calls) <- if("feature_id" %in% names(fData(eset.sub))) { fData(eset.sub)[, "feature_id"] } else { fData(eset.sub)[, "probe_id"] } ## Plot heatmapOnOffMarkers(test.data, group.OnOff$markers, pData(eset.sub), calls) }
This function calculates the on/off score for cell transitions. The score takes into account the cell type spcefific and most variable portion of the detected transcriptome. It can be calculated for a sample or group of samples representing specific (standard or engineered) cell type.
OnOff( eset, cell.change, out.put = c("marker.list", "individual"), min.diff.cutoff = 0.8, test.cutoff = 0.95 )OnOff( eset, cell.change, out.put = c("marker.list", "individual"), min.diff.cutoff = 0.8, test.cutoff = 0.95 )
eset |
an ExpressionSet containing data matrices of normalized expression data, present/absent calls, a gene annotation data frame and a phenotype data frame. |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data frame describes one transition from the start to a target cell type. |
out.put |
a character flag with two possible values, "marker.list" and "individual". The former means the on/off scores will be aggregated accross cell groups and also the marker genes for each cell transition (in cell.change) will be calculated, while the latter will generate the on/off scores for all individual samples. |
min.diff.cutoff |
a real number that represents the minimum difference between the fraction of present calls in donor vs target (in the standards), in order to define the markers for a given cell transition. Default is 0.8. |
test.cutoff |
a real number in (0, 1] that is the minimum fraction of present calls in a test sample/group to decide if a gene is present in a test sample/group. Default is stringently set at 0.95. |
This function returns a list of two objects, as follows:
scores |
a data.frame of on/off scores for each cell group given in cell.change(out.put="marker.list") or for each individual sample (out.put="idividual") |
markers |
a list of marker genes for the selected cell transitions in cell.change (out.put="marker.list") or NULL (out.put="individual") |
hgu133plus2CellScore for details on the
specific ExpressionSet object that shoud be provided as an input.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate a marker list group.OnOff <- OnOff(eset, cell.change, out.put="marker.list") ## Calculate the on/off scores for individual samples individ.OnOff <- OnOff(eset, cell.change, out.put="individual") }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate a marker list group.OnOff <- OnOff(eset, cell.change, out.put="marker.list") ## Calculate the on/off scores for individual samples individ.OnOff <- OnOff(eset, cell.change, out.put="individual") }
This function will generate a principal component analysis (PCA) plot of the IQR-filtered expression values that were used to generate the cosine similarity scores.
PcaStandards( label, label.name, exps, text.label = NULL, col.palette = c("blue", "magenta", "green", "red", "goldenrod", "mediumslateblue", "olivedrab", "navyblue", "plum", "tomato", "thistle", "limegreen", "burlywood4", "cornflowerblue", "deeppink", "chartreuse", "forestgreen", "darkslateblue", "blueviolet", "gray50", "darkorange", "black", "lightsalmon4", "mediumseagreen", "palegreen4", "palevioletred4", "peachpuff4", "plum4", "mediumspringgreen", "darkred", "khaki4", "lawngreen", "lightseagreen", "orange", "orchid3", "sienna4", "snow4", "turquoise3", "wheat3", "goldenrod2", "darkorange3") )PcaStandards( label, label.name, exps, text.label = NULL, col.palette = c("blue", "magenta", "green", "red", "goldenrod", "mediumslateblue", "olivedrab", "navyblue", "plum", "tomato", "thistle", "limegreen", "burlywood4", "cornflowerblue", "deeppink", "chartreuse", "forestgreen", "darkslateblue", "blueviolet", "gray50", "darkorange", "black", "lightsalmon4", "mediumseagreen", "palegreen4", "palevioletred4", "peachpuff4", "plum4", "mediumspringgreen", "darkred", "khaki4", "lawngreen", "lightseagreen", "orange", "orchid3", "sienna4", "snow4", "turquoise3", "wheat3", "goldenrod2", "darkorange3") )
label |
vector to be used for the point colours |
label.name |
name of the label |
exps |
an expression matrix of the IQR-filtered values as obtained by the function CosineSimScore(). |
text.label |
a vector of characters to label each point. |
col.palette |
a vector of colours to be used. There are 41 default colours. |
The function will plot two panels, a PCA plot on the left and a legend on the right. This is to accommodate that fact that the cell types names are NOT abbreviated and the legend might not fit in the plot area.
CosineSimScore for details on cosine
similarity calculation.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) PcaStandards(cs$pdataSub$experiment_id, "Experiment ID", cs$esetSub.IQR) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) PcaStandards(cs$pdataSub$experiment_id, "Experiment ID", cs$esetSub.IQR) }
This function plots a triangular heatmap of the cosine similarity scores.
PlotCosineSimHeatmap( data, desc = "xx", width = 20, height = 20, x = -30, y = 3 )PlotCosineSimHeatmap( data, desc = "xx", width = 20, height = 20, x = -30, y = 3 )
data |
a data.frame of cosine similarity scores, as generated by the function CosineSimScore(). |
desc |
a single character, with description for the file name. Suggested are "general.groups", "subgroups", and "samples". |
width |
the width of the output pdf, in inches. |
height |
the height of the output pdf, in inches. |
x |
the x-position of the heatmap legend. It may be necessary to change the value to position the legend in a suitable place on the plot. |
y |
the y-position of the heatmap legend. It may be necessary to change the value to position the legend in a suitable place on the plot. |
This function will print a pdf of the cosine similarity scores in the current working directory.
CosineSimScore for details on cosine
similarity calculation.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object, ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate pdf of cosine similarity heatmap in the working directory PlotCosineSimHeatmap(cs$cosine.general.groups, "general groups", width=7, height=7, x=-3.5, y=1) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object, ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate pdf of cosine similarity heatmap in the working directory PlotCosineSimHeatmap(cs$cosine.general.groups, "general groups", width=7, height=7, x=-3.5, y=1) }
This function will plot a rugplot of all CellScore values for each transition selected in the cell.change data frame. The function will only plot the scores for the test samples (annotated by the cellscore$column sub_cell_type1). Standards are not included. Samples are coloured by a secondary property, which must be a single column in the cellscore data frame.
RugplotCellScore(cellscore, cell.change, colour.by = NULL)RugplotCellScore(cellscore, cell.change, colour.by = NULL)
cellscore |
a data.frame of CellScore values as calculated by CellScore(). |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data. frame describes one transition from the start to a target cell type. |
colour.by |
the name of the column in the cellscore argument that contains the secondary property. |
This function outputs the plot on the active graphical device and returns invisibly NULL.
CellScore for details on CellScore.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Rugplot of CellScore, colour samples by transition induction method RugplotCellScore(cellscore, cell.change, "transition_induction_method") }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Rugplot of CellScore, colour samples by transition induction method RugplotCellScore(cellscore, cell.change, "transition_induction_method") }
This function is called by CellScoreReport to make a rugplot showing the CellScore of all test samples, in relation to the standards. Donor and target individual CellScore values are plotted in one horizontal lane, then test CellScore values are are in another horizontal lane. Z-score cutoffs based on the target standards are shown as dashed vertical lines.
rugplotDonorTargetTest(test.data, cellscore)rugplotDonorTargetTest(test.data, cellscore)
test.data |
a data.frame of CellScore values as calculated by CellScore(), for only plot group of test samples. |
cellscore |
a data.frame of CellScore values as calculated by CellScore(). |
This function outputs the plot on the active graphical device and returns invisibly NULL.
CellScore for details on CellScore.
## Not run: ## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset, cell.change, out.put="individual") ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object cs <- CosineSimScore(eset, cell.change, iqr.cutoff=0.05) ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Get the CellScore fvalues rom valid transitions defined by cell.change ## table plot.data <- extractTransitions(cellscore, cell.change) ## Define a plot group variable plot.data$plot_group <- paste(plot.data$experiment_id, plot.data$cxkey.subcelltype, sep="_") ## Sort the scores 1) by target 2) by donor 3) by study plot.data.ordered <- plot.data[order(plot.data$target, plot.data$donor_tissue, plot.data$experiment_id), ] ## How many plot_groups are there? table(plot.data$plot_group) ## pick one plot_group to plot group <- unique(plot.data$plot_group)[4] ## Select scores for only one plot group test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ] ## Plot rugplotDonorTargetTest(test.data, cellscore) } ## End(Not run)## Not run: ## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset, cell.change, out.put="individual") ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object cs <- CosineSimScore(eset, cell.change, iqr.cutoff=0.05) ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Get the CellScore fvalues rom valid transitions defined by cell.change ## table plot.data <- extractTransitions(cellscore, cell.change) ## Define a plot group variable plot.data$plot_group <- paste(plot.data$experiment_id, plot.data$cxkey.subcelltype, sep="_") ## Sort the scores 1) by target 2) by donor 3) by study plot.data.ordered <- plot.data[order(plot.data$target, plot.data$donor_tissue, plot.data$experiment_id), ] ## How many plot_groups are there? table(plot.data$plot_group) ## pick one plot_group to plot group <- unique(plot.data$plot_group)[4] ## Select scores for only one plot group test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ] ## Plot rugplotDonorTargetTest(test.data, cellscore) } ## End(Not run)
This function will plot the components of the CellScore, namely the donor- like and the target-like scores. The function will only plot the scores for the test samples (annotated by the cellscore$column sub_cell_type1). Standards are not included.
ScatterplotCellScoreComponents(cellscore, cell.change, index.plot = FALSE)ScatterplotCellScoreComponents(cellscore, cell.change, index.plot = FALSE)
cellscore |
a data.frame of CellScore values as calculated by CellScore() |
cell.change |
a data.frame with 3 columns: start cell type, test cell type, target cell type |
index.plot |
a logical variable, with TRUE meaning sample index should be plotted for easy identification of spots. Default is FALSE. This is useful if you want to see where the samples are located on the plot. |
This function outputs the plot on the active graphical device and returns invisibly NULL.
CellScore for details on CellScore.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Make the scaterplot of CellScore components ScatterplotCellScoreComponents(cellscore, cell.change, FALSE) }## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Make the scaterplot of CellScore components ScatterplotCellScoreComponents(cellscore, cell.change, FALSE) }
This function is called by CellScoreReport to make a scatterplot of test and standard samples (donor and target).
scatterplotDonorTargetTest(test.data, cellscore, index.plot = FALSE)scatterplotDonorTargetTest(test.data, cellscore, index.plot = FALSE)
test.data |
a data.frame of CellScore values as calculated by CellScore(), for a group of test samples. |
cellscore |
a data.frame of CellScore values as calculated by CellScore(). |
index.plot |
a logical variable, with TRUE meaning sample index should be plotted for easy identification of spots. Default is FALSE. |
This function outputs the plot on the active graphical device and returns invisibly NULL.
## Not run: ## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset, cell.change, out.put="individual") ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object cs <- CosineSimScore(eset, cell.change, iqr.cutoff=0.05) ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Get the CellScore fvalues rom valid transitions defined by cell.change ## table plot.data <- extractTransitions(cellscore, cell.change) ## Define a plot group variable plot.data$plot_group <- paste(plot.data$experiment_id, plot.data$cxkey.subcelltype, sep="_") ## Sort the scores 1) by target 2) by donor 3) by study plot.data.ordered <- plot.data[order(plot.data$target, plot.data$donor_tissue, plot.data$experiment_id), ] ## How many plot_groups are there? table(plot.data$plot_group) ## pick one plot_group to plot group <- unique(plot.data$plot_group)[4] ## Select scores for only one plot group test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ] ## save current graphical parameters old.par <- par(no.readonly=TRUE) ## Plot: this will plot a 2-paneled plot par(mfrow=c(1,2)) scatterplotDonorTargetTest(test.data, cellscore, FALSE) ## Reset graphical parameters par(old.par) } ## End(Not run)## Not run: ## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset, cell.change, out.put="individual") ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object cs <- CosineSimScore(eset, cell.change, iqr.cutoff=0.05) ## Generate the CellScore values for all samples cellscore <- CellScore(data=eset, transitions=cell.change, scores.onoff=individ.OnOff$scores, scores.cosine=cs$cosine.samples) ## Get the CellScore fvalues rom valid transitions defined by cell.change ## table plot.data <- extractTransitions(cellscore, cell.change) ## Define a plot group variable plot.data$plot_group <- paste(plot.data$experiment_id, plot.data$cxkey.subcelltype, sep="_") ## Sort the scores 1) by target 2) by donor 3) by study plot.data.ordered <- plot.data[order(plot.data$target, plot.data$donor_tissue, plot.data$experiment_id), ] ## How many plot_groups are there? table(plot.data$plot_group) ## pick one plot_group to plot group <- unique(plot.data$plot_group)[4] ## Select scores for only one plot group test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ] ## save current graphical parameters old.par <- par(no.readonly=TRUE) ## Plot: this will plot a 2-paneled plot par(mfrow=c(1,2)) scatterplotDonorTargetTest(test.data, cellscore, FALSE) ## Reset graphical parameters par(old.par) } ## End(Not run)