Package 'systemPipeTools' reference manual

Title:	Tools for data visualization
Description:	systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives.
Authors:	Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang [aut], Thomas Girke [aut]
Maintainer:	Daniela Cassol <[email protected]>
License:	Artistic-2.0
Version:	1.13.0
Built:	2024-06-30 04:13:57 UTC
Source:	https://github.com/bioc/systemPipeTools

exploreDDS

Description

Convenience wrapper function to transform raw read counts using the DESeq2::DESeq2-package() package transformations methods. The input file has to contain all the genes, not just differentially expressed ones.

Usage

exploreDDS(
  countMatrix,
  targets,
  cmp = cmp[[1]],
  preFilter = NULL,
  transformationMethod = "raw",
  blind = TRUE
)
exploreDDS(
  countMatrix,
  targets,
  cmp = cmp[[1]],
  preFilter = NULL,
  transformationMethod = "raw",
  blind = TRUE
)

Arguments

`countMatrix`	`date.frame` or `matrix` containing raw read counts.
`targets`	targets `data.frame`.
`cmp`	`⁠character matrix⁠` where comparisons are defined in two columns. This matrix should be generated with the `systemPipeR::readComp()` function from the targets file. Values used for comparisons need to match those in the `Factor` column of the targets file.
`preFilter`	allows removing rows in which there are very few reads. Accepts a numeric value with the minimum of total reads to keep. Default is `NULL`.
`transformationMethod`	a `⁠character string⁠` indicating which transformation method it will be used on the raw read counts. Supported methods include `rlog` and `vst` using the `DESeq2` package or default `raw` for no data transformation.
`blind`	logical, whether to blind the transformation to the experimental design (see varianceStabilizingTransformation), from `DESeq2::vst()` or `DESeq2::rlog()`.

Details

Note that the recommendation is to use the resulting transformed values in the transformationMethod argument only for visualization and clustering, not for differential expression analysis which needs raw counts. Users are strongly encouraged to consult the DESeq2::DESeq2-package() vignette for more detailed information on this topic and how to properly run DESeq2 on data sets with more complex experimental designs.

Value

returns an object of class DESeq2::DESeqTransform().

Author(s)

Daniela Cassol

References

For more details on DESeq2, please consult the following page: DESeq2. For more details on targets file definition, please consult the following page: systemPipeR.

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Run
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "raw"
)
exploredds
## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Run
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "raw"
)
exploredds

exploreDDSplot

Description

Scatterplot of transformed counts from two samples or grid of all samples

Usage

exploreDDSplot(
  countMatrix,
  targets,
  cmp = cmp[[1]],
  preFilter = NULL,
  samples,
  blind = TRUE,
  scattermatrix = FALSE,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)
exploreDDSplot(
  countMatrix,
  targets,
  cmp = cmp[[1]],
  preFilter = NULL,
  samples,
  blind = TRUE,
  scattermatrix = FALSE,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)

Arguments

`countMatrix`	`date.frame` or `matrix` containing raw read counts
`targets`	targets `data.frame`
`cmp`	`⁠character matrix⁠` where comparisons are defined in two columns. This matrix should be generated with the `systemPipeR::readComp()` function from the targets file. Values used for comparisons need to match those in the `Factor` column of the targets file.
`preFilter`	allows removing rows in which there are very few reads. Accepts a numeric value with the minimum of total reads to keep. Default is `NULL`.
`samples`	a `⁠character vector⁠` of two samples or `ALL` samples in the dataset. Could be specified the `SampleName` column name of the targets file or the respective numeric values. Also, if set as `ALL`, a correlation matrix it will be plot.
`blind`	logical, whether to blind the transformation to the experimental design (see varianceStabilizingTransformation), from `DESeq2::vst()` or `DESeq2::rlog()`.
`scattermatrix`	if `samples` set as `ALL`, requires to assign `TRUE` to build a correlation matrix and plot the correlogram of all the samples.
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.

Value

returns an object of ⁠ggplot2 plot⁠.

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
    file = targetspath,
    format = "matrix", delim = "-"
)
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
    package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploreDDSplot(countMatrix, targets,
    cmp = cmp[[1]], preFilter = NULL,
    samples = c(3, 4)
)
## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
    file = targetspath,
    format = "matrix", delim = "-"
)
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
    package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploreDDSplot(countMatrix, targets,
    cmp = cmp[[1]], preFilter = NULL,
    samples = c(3, 4)
)

Dimension Reduction with GLMplot

Description

This function computes and plots generalized principal components analysis for dimension reduction of count expression matrix.

Usage

GLMplot(
  exploredds,
  L = 2,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)
GLMplot(
  exploredds,
  L = 2,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)

Arguments

`exploredds`	object of class `DESeq2::DESeqDataSet()`, generated from `exploreDDS` function.
`L`	desired number of latent dimensions (positive integer).
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.
`...`	additional parameters for the `glmpca::glmpca()` function.

Value

returns an object of ggplot or plotly class.

References

F. William Townes and Kelly Street (2020). glmpca: Dimension Reduction of Non-Normally Distributed Data. R package version 0.2.0. https://CRAN.R-project.org/package=glmpca

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "raw")
GLMplot(exploredds, plotly = FALSE)
## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "raw")
GLMplot(exploredds, plotly = FALSE)

Hierarchical Clustering Dendrogram (hclustplot)

Description

This function computes the sample-wise correlation coefficients using the stats::cor() function from the transformed expression values. After transformation to a distance matrix, hierarchical clustering is performed with the stats::hclust() function, and the result is plotted as a dendrogram.

Usage

hclustplot(
  exploredds,
  method = "spearman",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)
hclustplot(
  exploredds,
  method = "spearman",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)

Arguments

`exploredds`	object of class `DESeq2::DESeqTransform()`.
`method`	a `⁠character string⁠` indicating which correlation coefficient is to be computed, based on the `stats::cor()` function. Options are: c("pearson" "kendall", "spearman").
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.

Value

returns an object of ggplot or plotly class.

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", 
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, 
format = "matrix", delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
 package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "rlog"
)
hclustplot(exploredds, method = "spearman")
## Targets file
targetspath <- system.file("extdata", "targets.txt", 
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, 
format = "matrix", delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
 package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "rlog"
)
hclustplot(exploredds, method = "spearman")

Hierarchical Clustering HeatMap (heatMaplot)

Description

This function performs hierarchical clustering on the transformed expression matrix generated with the DESeq2 package. It uses, by default, a Pearson correlation-based distance measure and complete linkage for cluster join.

Usage

heatMaplot(
  exploredds,
  clust,
  DEGlist = NULL,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)
heatMaplot(
  exploredds,
  clust,
  DEGlist = NULL,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)

Arguments

`exploredds`	object of class `DESeq2::DESeqTransform()`.
`clust`	select the data to apply the distance matrix computation. If `samples` selected, it will be applied the `stats::dist()` function to the transformed count matrix to get sample-to-sample distances. If `ind`, it is necessary to provide the list of differentially expressed genes, for the `exploredds` subset.
`DEGlist`	List of up or down regulated gene/transcript identifiers meeting the chosen filter settings for all comparisons defined in data frames `pval` and `log2FC`.
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.
`...`	additional parameters for the `pheatmap::pheatmap()` function.

Value

returns an object of pheatmap or plotly class.

References

Raivo Kolde (2019). pheatmap: Pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap

Examples

### Load data
targetspath <- system.file("extdata", "targets.txt", 
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, 
format = "matrix", delim = "-")
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Samples plot
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "rlog"
)
heatMaplot(exploredds, clust = "samples", plotly = TRUE)
## Individuals genes identified in DEG analysis
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix,
    targets = targets, cmp = cmp[[1]], independent = FALSE
)
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10)
)
### Plot
heatMaplot(exploredds,
    clust = "ind",
    DEGlist = unique(as.character(unlist(DEG_list[[1]])))
)
### Load data
targetspath <- system.file("extdata", "targets.txt", 
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, 
format = "matrix", delim = "-")
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Samples plot
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "rlog"
)
heatMaplot(exploredds, clust = "samples", plotly = TRUE)
## Individuals genes identified in DEG analysis
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix,
    targets = targets, cmp = cmp[[1]], independent = FALSE
)
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10)
)
### Plot
heatMaplot(exploredds,
    clust = "ind",
    DEGlist = unique(as.character(unlist(DEG_list[[1]])))
)

MAplot

Description

This function plots log2 fold changes (y-axis) versus the mean of normalized counts (on the x-axis). Statistically significant features are colored.

Usage

MAplot(
  degseqDF,
  FDR.cutoff = 0.05,
  comparison,
  filter = c(Fold = 2, FDR = 10),
  genes = "NULL",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)
MAplot(
  degseqDF,
  FDR.cutoff = 0.05,
  comparison,
  filter = c(Fold = 2, FDR = 10),
  genes = "NULL",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)

Arguments

`degseqDF`	object of class `data.frame` generated by `systemPipeR::run_edgeR()` or `systemPipeR::run_DESeq2()`.
`FDR.cutoff`	filter cutoffs for the p-value adjusted.
`comparison`	`⁠character vector⁠` specifying the factor names for comparison.
`filter`	Named vector with filter cutoffs of format c(Fold=2, FDR=1) where Fold refers to the fold change cutoff (unlogged) and FDR to the p-value cutoff.
`genes`	`⁠character vecto⁠`r of genes names to show on the plot.
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.

Value

returns an object of ggplot or plotly class.

Examples

## Load targets file and count reads dataframe
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
    file = targetspath, format = "matrix",
    delim = "-"
)
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
    package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix, targets = targets,
    cmp = cmp[[1]], independent = FALSE
)
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10)
)
## Plot
MAplot(degseqDF,
    comparison = "M12-A12", filter = c(Fold = 1, FDR = 20),
    genes = "ATCG00280"
)
## Load targets file and count reads dataframe
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
    file = targetspath, format = "matrix",
    delim = "-"
)
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
    package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix, targets = targets,
    cmp = cmp[[1]], independent = FALSE
)
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10)
)
## Plot
MAplot(degseqDF,
    comparison = "M12-A12", filter = c(Fold = 1, FDR = 20),
    genes = "ATCG00280"
)

Multidimensional scaling with MDSplot

Description

This function computes and plots multidimensional scaling analysis for dimension reduction of count expression matrix. Internally, it is applied the stats::dist() function to the transformed count matrix to get sample-to-sample distances.

Usage

MDSplot(
  exploredds,
  method = "spearman",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)
MDSplot(
  exploredds,
  method = "spearman",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)

Arguments

`exploredds`	object of class `DESeq2::DESeqDataSet()`, generated from `exploreDDS` function.
`method`	a `⁠character string⁠` indicating which correlation coefficient is to be computed, based on the `stats::cor()` function. Options are: c("pearson" "kendall", "spearman").
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.

Value

returns an object of ggplot or plotly class.

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "rlog")
MDSplot(exploredds, plotly = FALSE)
## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "rlog")
MDSplot(exploredds, plotly = FALSE)

PCAplot

Description

This function plots a Principal Component Analysis (PCA) from transformed expression matrix. This plot shows samples variation based on the expression values and identifies batch effects.

Usage

PCAplot(exploredds, plotly = FALSE, savePlot = FALSE, filePlot = NULL)
PCAplot(exploredds, plotly = FALSE, savePlot = FALSE, filePlot = NULL)

Arguments

`exploredds`	object of class `DESeq2::DESeqTransform()`.
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.

Value

returns an object of ggplot or plotly class.

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "rlog")
PCAplot(exploredds, plotly = TRUE)
## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "rlog")
PCAplot(exploredds, plotly = TRUE)

Create an HTML table using DT package with fixed columns

Description

Create an HTML table using DT package with fixed columns

Usage

showDT(data, ...)
showDT(data, ...)

Arguments

`data`	data object (either a matrix or a data frame).
`...`	Additional arguments used by dDT::atatable() function.

Value

returns an object of datatables and htmlwidget.

Examples

showDT(iris)
showDT(iris)

t-Distributed Stochastic Neighbor embedding with tSNEplot

Description

This function computes and plots t-Distributed Stochastic Neighbor embedding (t-SNE) analysis for unsupervised nonlinear dimensionality reduction of count expression matrix. Internally, it is applied the Rtsne::Rtsne() function, using the exact t-SNE computing with theta=0.0.

Usage

tSNEplot(
  countMatrix,
  targets,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)
tSNEplot(
  countMatrix,
  targets,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)

Arguments

`countMatrix`	`date.frame` or `matrix` containing raw read counts.
`targets`	targets `data.frame`.
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.
`...`	additional parameters for the `Rtsne::Rtsne()` function.

Value

returns an object of ggplot or plotly class.

References

Jesse H. Krijthe (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation, URL: https://github.com/jkrijthe/Rtsne

Examples

targetspath <- system.file("extdata", "targets.txt",
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix",
delim = "-")
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
set.seed(42)
tSNEplot(countMatrix, targets, perplexity = 5)
targetspath <- system.file("extdata", "targets.txt",
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix",
delim = "-")
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
set.seed(42)
tSNEplot(countMatrix, targets, perplexity = 5)

Volcano plot with `volcanoplot`

Description

A simple function that shows statistical significance (p-value) versus magnitude of change (⁠log2 fold change⁠).

Usage

volcanoplot(
  degseqDF,
  comparison,
  filter = c(Fold = 2, FDR = 10),
  genes = "NULL",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)
volcanoplot(
  degseqDF,
  comparison,
  filter = c(Fold = 2, FDR = 10),
  genes = "NULL",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)

Arguments

`degseqDF`	object of class `data.frame` generated by `systemPipeR::run_edgeR()` or `systemPipeR::run_DESeq2()`.
`comparison`	`⁠character vector⁠` specifying the factor names for comparison.
`filter`	Named vector with filter cutoffs of format c(Fold=2, FDR=1) where Fold refers to the fold change cutoff (unlogged) and FDR to the p-value cutoff.
`genes`	`⁠character vector⁠` of genes names to show on the plot.
`plotly`	logical: when `FALSE` (default), the `ggplot2` plot will be returned. `TRUE` option returns the `plotly` version of the plot.
`savePlot`	logical: when `FALSE` (default), the plot will not be saved. If `TRUE` the plot will be saved, and requires the `filePlot` argument.
`filePlot`	file name where the plot will be saved. For more information, please consult the `ggplot2::ggsave()` function.

Value

returns an object of ggplot or plotly class.

Examples

## Load targets file and count reads dataframe
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
    file = targetspath, format = "matrix",
    delim = "-"
)
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
    package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix,
    targets = targets, cmp = cmp[[1]], independent = FALSE)
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10))
## Plot
volcanoplot(degseqDF,
    comparison = "M12-A12", filter = c(Fold = 1, FDR = 20),
    genes = "ATCG00280")
## Load targets file and count reads dataframe
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
    file = targetspath, format = "matrix",
    delim = "-"
)
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
    package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
    countDF = countMatrix,
    targets = targets, cmp = cmp[[1]], independent = FALSE)
DEG_list <- systemPipeR::filterDEGs(
    degDF = degseqDF,
    filter = c(Fold = 2, FDR = 10))
## Plot
volcanoplot(degseqDF,
    comparison = "M12-A12", filter = c(Fold = 1, FDR = 20),
    genes = "ATCG00280")

Package 'systemPipeTools'

Help Index

exploreDDS

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

exploreDDSplot

Description

Usage

Arguments

Value

Examples

Dimension Reduction with GLMplot

Description

Usage

Arguments

Value

References

Examples

Hierarchical Clustering Dendrogram (hclustplot)

Description

Usage

Arguments

Value

Examples

Hierarchical Clustering HeatMap (heatMaplot)

Description

Usage

Arguments

Value

References

Examples

MAplot

Description

Usage

Arguments

Value

Examples

Multidimensional scaling with MDSplot

Description

Usage

Arguments

Value

Examples

PCAplot

Description

Usage

Arguments

Value

Examples

Create an HTML table using DT package with fixed columns

Description

Usage

Arguments

Value

Examples

t-Distributed Stochastic Neighbor embedding with tSNEplot

Description

Usage

Arguments

Value

References

Examples

Volcano plot with volcanoplot

Description

Usage

Arguments

Value

Examples

Volcano plot with `volcanoplot`