Package: pcaExplorer
Authors: Federico Marini [aut, cre]
Version: 2.2.1
Compiled date: 2017-08-30
License: MIT + file LICENSE

1 Getting started

pcaExplorer is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("pcaExplorer")

If you prefer, you can install and use the development version, which can be retrieved via Github (https://github.com/federicomarini/pcaExplorer). To do so, use

library("devtools")
install_github("federicomarini/pcaExplorer")

Once pcaExplorer is installed, it can be loaded by the following command.

library("pcaExplorer")

2 Introduction

pcaExplorer is a Bioconductor package containing a Shiny application for analyzing expression data in different conditions and experimental factors.

It is a general-purpose interactive companion tool for RNA-seq analysis, which guides the user in exploring the Principal Components of the data under inspection.

pcaExplorer provides tools and functionality to detect outlier samples, genes that show particular patterns, and additionally provides a functional interpretation of the principal components for further quality assessment and hypothesis generation on the input data.

Moreover, a novel visualization approach is presented to simultaneously assess the effect of more than one experimental factor on the expression levels.

Thanks to its interactive/reactive design, it is designed to become a practical companion to any RNA-seq dataset analysis, making exploratory data analysis accessible also to the bench biologist, while providing additional insight also for the experienced data analyst.

Starting from development version 1.1.3, pcaExplorer supports reproducible research with state saving and automated report generation. Each generated plot and table can be exported by simple mouse clicks on the dedicated buttons.

2.1 Citation info

If you use pcaExplorer for your analysis, please cite it as here below:

citation("pcaExplorer")


To cite package 'pcaExplorer' in publications use:

  Federico Marini (2017). pcaExplorer: Interactive Visualization
  of RNA-seq Data Using a Principal Components Approach. R package
  version 2.2.1. https://github.com/federicomarini/pcaExplorer

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach},
    author = {Federico Marini},
    year = {2017},
    note = {R package version 2.2.1},
    url = {https://github.com/federicomarini/pcaExplorer},
  }

3 Launching the application

After loading the package, the pcaExplorer app can be launched in different modes:

pcaExplorer(dds = dds, rlt = rlt), where dds is a DESeqDataSet object and rlt is a DESeqTransform object, which were created during an existing session for the analysis of an RNA-seq dataset with the DESeq2 package
pcaExplorer(dds = dds), where dds is a DESeqDataSet object. The rlt object is automatically computed upon launch.
pcaExplorer(countmatrix = countmatrix, coldata = coldata), where countmatrix is a count matrix, generated after assigning reads to features such as genes via tools such as HTSeq-count or featureCounts, and coldata is a data frame containing the experimental covariates of the experiments, such as condition, tissue, cell line, run batch and so on.
pcaExplorer(), and then subsequently uploading the count matrix and the covariates data frame through the user interface. These files need to be formatted as tab separated files, which is a common format for storing such count values.

Additional parameters and objects that can be provided to the main pcaExplorer function are:

pca2go, which is an object created by the pca2go function, which scans the genes with high loadings in each principal component and each direction, and looks for functions (such as GO Biological Processes) that are enriched above the background. The offline pca2go function is based on the routines and algorithms of the topGO package, but as an alternative, this object can be computed live during the execution of the app exploiting the goana function, provided by the limma package. Although this likely provides more general (and probably less informative) functions, it is a good compromise for obtaining a further data interpretation.
annotation, a data frame object, with row.names as gene identifiers (e.g. ENSEMBL ids) identical to the row names of the count matrix or dds object, and an extra column gene_name, containing e.g. HGNC-based gene symbols. This can be used for making information extraction easier, as ENSEMBL ids (a usual choice when assigning reads to features) do not provide an immediate readout for which gene they refer to. This can be either passed as a parameter when launching the app, or also uploaded as a tab separated text file. The package provides two functions, get_annotation and get_annotation_orgdb, as a convenient wrapper to obtain the updated annotation information, respectively from biomaRt or via the org.XX.eg.db packages.

4 The controls sidebar

Most of the input controls are located in the sidebar, some are as well in the individual tabs of the app. By changing one or more of the input parameters, the user can get a fine control on what is displayed.

4.1 App settings

Here are the parameters that set input values for most of the tabs. By hovering over with the mouse, the user can receive additional information on how to set the parameter, powered by the shinyBS package.

x-axis PC - Select the principal component to display on the x axis
y-axis PC - Select the principal component to display on the y axis
Group/color by - Select the group of samples to stratify the analysis. Can also assume multiple values.
Nr of (most variant) genes - Number of genes to select for computing the principal components. The top n genes are selected ranked by their variance inter-samples
Alpha - Color transparency for the plots. Can assume values from 0 (transparent) to 1 (opaque)
Labels size - Size of the labels for the samples in the principal components plots
Points size - Size of the points to be plotted in the principal components plots
Variable name size - Size of the labels for the genes PCA - correspond to the samples names
Scaling factor - Scale value for resizing the arrow corresponding to the variables in the PCA for the genes. It should be used for mere visualization purposes
Color palette - Select the color palette to be used in the principal components plots. The number of colors is selected automatically according to the number of samples and to the levels of the factors of interest and their interactions
Plot style for gene counts - Plot either boxplots or violin plots, with jittered points superimposed

4.2 Plot export settings

Width and height for the figures to export are input here in cm.

Additional controls available in the single tabs are also assisted by tooltips that show on hovering the mouse. Normally they are tightly related to the plot/output they are placed nearby.

5 The task menu

The task menu, accessible by clicking on the cog icon in the upper right part of the application, provides two functionalities:

Exit pcaExplorer & save will close the application and store the content of the input and values reactive objects in two list objects made available in the global environment, called pcaExplorer_inputs_YYYYMMDD_HHMMSS and pcaExplorer_values_YYYYMMDD_HHMMSS
Save State as .RData will similarly store LiveInputs and r_data in a binary file named pcaExplorerState_YYYYMMDD_HHMMSS.Rdata, without closing the application

6 The app panels

The pcaExplorer app is structured in different panels, each focused on a different aspect of the data exploration.

Most of the panels work extensively with click-based and brush-based interactions, to gain additional depth in the explorations, for example by zooming, subsetting, selecting. This is possible thanks to the recent developments in the shiny package/framework.

The available panels are the described in the following subsections.

6.1 Data Upload

These file input controls are available when no dds or countmatrix + coldata are provided. Additionally, it is possible to upload the annotation data frame.

When the objects are already passed as parameters, a brief overview/summary for them is displayed.

6.2 Instructions

This is where you most likely are reading this text (otherwise in the package vignette).

6.3 Counts Table

Interactive tables for the raw, normalized or (r)log-transformed counts are shown in this tab. The user can also generate a sample-to-sample correlation scatter plot with the selected data.

6.4 Data Overview

This panel displays information on the objects in use, either passed as parameters or generated from the count matrix provided. Displayed information comprise the design metadata, a sample to sample distance heatmap, the number of million of reads per sample and some basic summary for the counts.

6.5 Samples View

This panel displays the PCA projections of sample expression profiles onto any pair of components, a scree plot, a zoomed PCA plot, a plot of the genes with top and bottom loadings. Additionally, this section presents a PCA plot where it is possible to remove samples deemed to be outliers in the analysis, which is very useful to check the effect of excluding them. If needed, an interactive 3D visualization of the principal components is also available.

6.6 Genes View

This panel displays the PCA projections of genes abundances onto any pair of components, with samples as biplot variables, to identify interesting groups of genes. Zooming is also possible, and clicking on single genes, a boxplot is returned, grouped by the factors of interest. A static and an interactive heatmap are provided, including the subset of selected genes, also displayed as (standardized) expression profiles across the samples. These are also reported in datatable objects, accessible in the bottom part of the tab.

6.7 GeneFinder

The user can search and display the expression values of a gene of interest, either by ID or gene name, as provided in the annotation. A handy panel for quick screening of shortlisted genes, again grouped by the factors of interest. The graphic can be readily exported as it is, and this can be iterated on a shortlisted set of genes. For each of them, the underlying data is displayed in an interactive table, also exportable with a click.

6.8 PCA2GO

This panel shows the functional annotation of the principal components, with GO functions enriched in the genes with high loadings on the selected principal components. It allows for the live computing of the object, that can otherwise provided as a parameter when launching the app. The panel displays a PCA plot for the samples, surrounded on each side by the tables with the functions enriched in each component and direction.

6.9 Multifactor Exploration

This panel allows for the multifactor exploration of datasets with 2 or more experimental factors. The user has to select first the two factors and the levels for each. Then, it is possible to combine samples from Factor1-Level1 in the selected order by clicking on each sample name, one for each level available in the selected Factor2. In order to build the matrix, an equal number of samples for each level of Factor 1 is required, to keep the design somehow balanced. A typical case for choosing factors 1 and 2 is for example when different conditions and tissues are present.

Once constructed, a plot is returned that tries to represent simultaneously the effect of the two factors on the data. Each gene is represented by a dot-line-dot structure, with the color that is indicating the tissue (factor 2) where the gene is mostly expressed. Each gene has two dots, one for each condition level (factor 1), and the position of the points is dictated by the scores of the principal components calculated on the matrix object. The line connecting the dots is darker when the tissue where the gene is mostly expressed varies throughout the conditions.

This representation is under active development, and it is promising for identifying interesting sets or clusters of genes according to their behavior on the Principal Components subspaces. Zooming and exporting of the underlying genes is also allowed by brushing on the main plot.

6.10 Report Editor

The report editor is the backbone for generating and editing the interactive report on the basis of the uploaded data and the current state of the application. General Markdown options and Editor options are available, and the text editor, based on the shinyAce package, contains a comprehensive template report, that can be edited to the best convenience of the user.

The editor supports R code autocompletion, making it easy to add new code chunks for additional sections. A preview is available in the tab itself, and the report can be generated, saved and subsequently shared with simple mouse clicks.

6.11 About

Contains general information on pcaExplorer, including the developer’s contact, the link to the development version in Github, as well as the output of sessionInfo, to use for reproducibility sake - or bug reporting. Information for citing pcaExplorer is also reported.

7 Running `pcaExplorer` on published datasets

We can run pcaExplorer for demonstration purpose on published datasets that are available as SummarizedExperiment in an experiment Bioconductor packages.

We will use the airway dataset, which can be installed with this command

source("https://bioconductor.org/biocLite.R")
biocLite("airway")

This package provides a RangedSummarizedExperiment object of read counts in genes for an RNA-Seq experiment on four human airway smooth muscle cell lines treated with dexamethasone. More details such as gene models and count quantifications can be found in the airway package vignette.

To run pcaExplorer on this dataset, the following commands are required. First, prepare the objects to be passed as parameters of pcaExplorer

library(airway)
library(DESeq2)

data(airway)

dds_airway <- DESeqDataSet(airway,design= ~ cell + dex)
dds_airway

class: DESeqDataSet 
dim: 64102 8 
metadata(2): '' version
assays(1): counts
rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
rowData names(0):
colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
colData names(9): SampleName cell ... Sample BioSample

rld_airway <- rlogTransformation(dds_airway)
rld_airway

class: DESeqTransform 
dim: 64102 8 
metadata(2): '' version
assays(1): ''
rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
rowData names(6): baseMean baseVar ... dispFit rlogIntercept
colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
colData names(10): SampleName cell ... BioSample sizeFactor

Then launch the app itself

pcaExplorer(dds = dds_airway,
            rlt = rld_airway)

The annotation for this dataset can be built by exploiting the org.Hs.eg.db package

library(org.Hs.eg.db)
genenames_airway <- mapIds(org.Hs.eg.db,keys = rownames(dds_airway),column = "SYMBOL",keytype="ENSEMBL")
annotation_airway <- data.frame(gene_name = genenames_airway,
                                row.names = rownames(dds_airway),
                                stringsAsFactors = FALSE)
head(annotation_airway)

                gene_name
ENSG00000000003    TSPAN6
ENSG00000000005      TNMD
ENSG00000000419      DPM1
ENSG00000000457     SCYL3
ENSG00000000460  C1orf112
ENSG00000000938       FGR

or alternatively, by using the get_annotation or get_annotation_orgdb wrappers.

anno_df_orgdb <- get_annotation_orgdb(dds = dds_airway,
                                      orgdb_species = "org.Hs.eg.db",
                                      idtype = "ENSEMBL")

anno_df_biomart <- get_annotation(dds = dds_airway,
                                  biomart_dataset = "hsapiens_gene_ensembl",
                                  idtype = "ensembl_gene_id")

'select()' returned 1:many mapping between keys and columns

head(anno_df_orgdb)

                        gene_id gene_name
ENSG00000000003 ENSG00000000003    TSPAN6
ENSG00000000005 ENSG00000000005      TNMD
ENSG00000000419 ENSG00000000419      DPM1
ENSG00000000457 ENSG00000000457     SCYL3
ENSG00000000460 ENSG00000000460  C1orf112
ENSG00000000938 ENSG00000000938       FGR

Then again, the app can be launched with

pcaExplorer(dds = dds_airway,
            rlt = rld_airway,
            annotation = annotation_airway) # or anno_df_orgdb, or anno_df_biomart

If desired, alternatives can be used. See the well written annotation workflow available at the Bioconductor site (https://bioconductor.org/help/workflows/annotation/annotation/).

8 Running `pcaExplorer` on synthetic datasets

For testing and demonstration purposes, a function is also available to generate synthetic datasets whose counts are generated based on two or more experimental factors.

This can be called with the command

dds_multifac <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 1)

See all the available parameters by typing ?makeExampleDESeqDataSet_multifac. Credits are given to the initial implementation by Mike Love in the DESeq2 package.

The following steps run the app with the synthetic dataset

dds_multifac <- makeExampleDESeqDataSet_multifac(betaSD_condition = 1,betaSD_tissue = 3)
dds_multifac

class: DESeqDataSet 
dim: 1000 12 
metadata(1): version
assays(1): counts
rownames(1000): gene1 gene2 ... gene999 gene1000
rowData names(4): trueIntercept trueBeta_condition trueBeta_tissue
  trueDisp
colnames(12): sample1 sample2 ... sample11 sample12
colData names(2): condition tissue

rld_multifac <- rlogTransformation(dds_multifac)
rld_multifac

class: DESeqTransform 
dim: 1000 12 
metadata(1): version
assays(1): ''
rownames(1000): gene1 gene2 ... gene999 gene1000
rowData names(10): trueIntercept trueBeta_condition ... dispFit
  rlogIntercept
colnames(12): sample1 sample2 ... sample11 sample12
colData names(3): condition tissue sizeFactor

## checking how the samples cluster on the PCA plot
pcaplot(rld_multifac,intgroup = c("condition","tissue"))

Launch the app for exploring this dataset with

pcaExplorer(dds = dds_multifac,
            rlt = rld_multifac)

When such a dataset is provided, the panel for multifactorial exploration is also usable at its best.

9 Functions exported by the package for standalone usage

The functions exported by the pcaExplorer package can be also used in a standalone scenario, provided the required objects are in the working environment. They are listed here for an overview, but please refer to the documentation for additional details. Where possible, for each function a code snippet will be provided for its typical usage.

9.1 `pcaplot`

pcaplot plots the sample PCA for DESeqTransform objects, such as rlog-transformed data. This is the workhorse of the Samples View tab

pcaplot(rld_airway,intgroup = c("cell","dex"),ntop = 1000,
        pcX = 1, pcY = 2, title = "airway dataset PCA on samples - PC1 vs PC2")

# on a different set of principal components...
pcaplot(rld_airway,intgroup = c("dex"),ntop = 1000,
        pcX = 1, pcY = 4, title = "airway dataset PCA on samples - PC1 vs PC4",
        ellipse = TRUE)

9.2 `pcaplot3d`

Same as for pcaplot, but it uses the threejs package for the 3d interactive view.

pcaplot3d(rld_airway,intgroup = c("cell","dex"),ntop = 1000,
        pcX = 1, pcY = 2, pcZ = 3)
# will open up in the viewer

9.3 `pcascree`

pcascree produces a scree plot of the PC computed on the samples. A prcomp object needs to be passed as main argument

pcaobj_airway <- prcomp(t(assay(rld_airway)))
pcascree(pcaobj_airway,type="pev",
         title="Proportion of explained proportion of variance - airway dataset")

9.4 `correlatePCs` and `plotPCcorrs`

correlatePCs and plotPCcorrs respectively compute and plot significance of the (cor)relation of each covariate versus a principal component. The input for correlatePCs is a prcomp object

res_pcairway <- correlatePCs(pcaobj_airway,colData(dds_airway))

res_pcairway

     SampleName       cell        dex albut       Run avgLength Experiment
PC_1  0.4288799 0.68227033 0.02092134    NA 0.4288799 0.2554109  0.4288799
PC_2  0.4288799 0.11161023 0.56370286    NA 0.4288799 0.1993592  0.4288799
PC_3  0.4288799 0.10377716 0.38647623    NA 0.4288799 0.1864725  0.4288799
PC_4  0.4288799 0.08331631 0.56370286    NA 0.4288799 0.4635148  0.4288799
        Sample BioSample
PC_1 0.4288799 0.4288799
PC_2 0.4288799 0.4288799
PC_3 0.4288799 0.4288799
PC_4 0.4288799 0.4288799

plotPCcorrs(res_pcairway)

9.5 `hi_loadings`

hi_loadings extracts and optionally plots the genes with the highest loadings

# extract the table of the genes with high loadings
hi_loadings(pcaobj_airway,topN = 10,exprTable=counts(dds_airway))

                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
ENSG00000143127         11        108         24        485         41
ENSG00000168309         12        274         35        451          1
ENSG00000101347       1632      17126       2098      19694       1598
ENSG00000211445        916      15749       3142      24057       1627
ENSG00000096060        260       4652        381       3875        601
ENSG00000163884         70       1325         52        702         36
ENSG00000171819          4         50         19        543          1
ENSG00000127954         13        247         25        889          2
ENSG00000152583         62       2040         99       1172        100
ENSG00000109906          4        739          5        429          1
ENSG00000162692        914         62       1192         55       1359
ENSG00000178695       4746        830       4805        414       5321
ENSG00000214814        312         24        193         28        501
ENSG00000164742       1506        347        275         14        137
ENSG00000138316       1327        207       1521        118       1962
ENSG00000123610        444        136        303         36       1170
ENSG00000124766       2483        406       2057        185       2829
ENSG00000105989        562         47       1575        106        106
ENSG00000013293        268         23        435         56        558
ENSG00000146250        330         41        907         89        720
                SRR1039517 SRR1039520 SRR1039521
ENSG00000143127        607         77        660
ENSG00000168309         65          4        193
ENSG00000101347      17697       1683      32036
ENSG00000211445      16274       1741      24883
ENSG00000096060       5493        154       4118
ENSG00000163884        487         34       1355
ENSG00000171819         10         14       1067
ENSG00000127954        199         20        462
ENSG00000152583       1924         79       2138
ENSG00000109906        581         12       1113
ENSG00000162692        171        646         31
ENSG00000178695       1391       4411        606
ENSG00000214814         65        789         76
ENSG00000164742         37        475         56
ENSG00000138316        618       1045        152
ENSG00000123610        195        473         37
ENSG00000124766        870       1851        301
ENSG00000105989         24        382         46
ENSG00000013293         75        562         74
ENSG00000146250        123        439         60

# or alternatively plot the values
hi_loadings(pcaobj_airway,topN = 10,annotation = annotation_airway)

9.6 `genespca`

genespca computes and plots the principal components of the genes, eventually displaying the samples as in a typical biplot visualization. This is the function in action for the Genes View tab

groups_airway <- colData(dds_airway)$dex
cols_airway <- scales::hue_pal()(2)[groups_airway]
# with many genes, do not plot the labels of the genes
genespca(rld_airway,ntop=5000,
         choices = c(1,2),
         arrowColors=cols_airway,groupNames=groups_airway,
         alpha = 0.2,
         useRownamesAsLabels=FALSE,
         varname.size = 5
        )

# with a smaller number of genes, plot gene names included in the annotation
genespca(rld_airway,ntop=100,
         choices = c(1,2),
         arrowColors=cols_airway,groupNames=groups_airway,
         alpha = 0.7,
         varname.size = 5,
         annotation = annotation_airway
        )

9.7 `topGOtable`

topGOtable is a convenient wrapper for extracting functional GO terms enriched in a subset of genes (such as the differentially expressed genes), based on the algorithm and the implementation in the topGO package

# example not run due to quite long runtime
dds_airway <- DESeq(dds_airway)
res_airway <- results(dds_airway)
res_airway$symbol <- mapIds(org.Hs.eg.db,
                            keys=row.names(res_airway),
                            column="SYMBOL",
                            keytype="ENSEMBL",
                            multiVals="first")
res_airway$entrez <- mapIds(org.Hs.eg.db,
                            keys=row.names(res_airway),
                            column="ENTREZID",
                            keytype="ENSEMBL",
                            multiVals="first")
resOrdered <- as.data.frame(res_airway[order(res_airway$padj),])
head(resOrdered)
# extract DE genes
de_df <- resOrdered[resOrdered$padj < .05 & !is.na(resOrdered$padj),]
de_symbols <- de_df$symbol
# extract background genes
bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
bg_symbols <- mapIds(org.Hs.eg.db,
                     keys=bg_ids,
                     column="SYMBOL",
                     keytype="ENSEMBL",
                     multiVals="first")
# run the function
topgoDE_airway <- topGOtable(de_symbols, bg_symbols,
                             ontology = "BP",
                             mapping = "org.Hs.eg.db",
                             geneID = "symbol")

9.8 `pca2go`

pca2go provides a functional interpretation of the principal components, by extracting the genes with the highest loadings for each PC, and then runs internally topGOtable on them for efficient functional enrichment analysis. Needs a DESeqTransform object as main parameter

pca2go_airway <- pca2go(rld_airway,
                        annotation = annotation_airway,
                        organism = "Hs",
                        ensToGeneSymbol = TRUE,
                        background_genes = bg_ids)
# for a smooth interactive exploration, use DT::datatable
datatable(pca2go_airway$PC1$posLoad)
# display it in the normal R session...
head(pca2go_airway$PC1$posLoad)
# ... or use it for running the app and display in the dedicated tab
pcaExplorer(dds_airway,rld_airway,
            pca2go = pca2go_airway,
            annotation = annotation_airway)

9.9 `limmaquickpca2go`

limmaquickpca2go is an alternative to pca2go, used in the live running app, thanks to its fast implementation based on the limma::goana function.

goquick_airway <- limmaquickpca2go(rld_airway,
                                   pca_ngenes = 10000,
                                   inputType = "ENSEMBL",
                                   organism = "Hs")
# display it in the normal R session...
head(goquick_airway$PC1$posLoad)
# ... or use it for running the app and display in the dedicated tab
pcaExplorer(dds_airway,rld_airway,
            pca2go = goquick_airway,
            annotation = annotation_airway)

9.10 `makeExampleDESeqDataSet_multifac`

makeExampleDESeqDataSet_multifac constructs a simulated DESeqDataSet of Negative Binomial dataset from different conditions. The fold changes between the conditions can be adjusted with the betaSD_condition betaSD_tissue arguments

dds_simu <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 0.5)
dds_simu

class: DESeqDataSet 
dim: 1000 12 
metadata(1): version
assays(1): counts
rownames(1000): gene1 gene2 ... gene999 gene1000
rowData names(4): trueIntercept trueBeta_condition trueBeta_tissue
  trueDisp
colnames(12): sample1 sample2 ... sample11 sample12
colData names(2): condition tissue

dds2_simu <- makeExampleDESeqDataSet_multifac(betaSD_condition = 0.5,betaSD_tissue = 2)
dds2_simu

class: DESeqDataSet 
dim: 1000 12 
metadata(1): version
assays(1): counts
rownames(1000): gene1 gene2 ... gene999 gene1000
rowData names(4): trueIntercept trueBeta_condition trueBeta_tissue
  trueDisp
colnames(12): sample1 sample2 ... sample11 sample12
colData names(2): condition tissue

rld_simu <- rlogTransformation(dds_simu)
rld2_simu <- rlogTransformation(dds2_simu)
pcaplot(rld_simu,intgroup = c("condition","tissue")) + 
  ggplot2::ggtitle("Simulated data - Big condition effect, small tissue effect")

pcaplot(rld2_simu,intgroup = c("condition","tissue")) + 
  ggplot2::ggtitle("Simulated data - Small condition effect, bigger tissue effect")

9.11 `distro_expr`

Plots the distribution of expression values, either with density lines, boxplots or violin plots.

distro_expr(rld_airway,plot_type = "density")
distro_expr(rld_airway,plot_type = "violin")

distro_expr(rld_airway,plot_type = "boxplot")

9.12 `geneprofiler`

Plots the profile expression of a subset of genes, optionally as standardized values

dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 1)
rlt <- DESeq2::rlogTransformation(dds)
set.seed(42)
geneprofiler(rlt,paste0("gene",sample(1:1000,20)), plotZ = FALSE)

you provided 20 unique identifiers
20 out of 20 provided genes were found in the data

9.13 `get_annotation` and `get_annotation_orgdb`

These two wrapper functions retrieve the latest annotations for the dds object, to be used in the call to the pcaExplorer function. They use respectively the biomaRt package and the org.XX.eg.db packages.

anno_df_biomart <- get_annotation(dds = dds_airway,
                                  biomart_dataset = "hsapiens_gene_ensembl",
                                  idtype = "ensembl_gene_id")

anno_df_orgdb <- get_annotation_orgdb(dds = dds_airway,
                                      orgdb_species = "org.Hs.eg.db",
                                      idtype = "ENSEMBL")

9.14 `pair_corr`

Plots the pairwise scatter plots and computes the correlation coefficient on the expression matrix provided.

# use a subset of the counts to reduce plotting time, it can be time consuming with many samples
pair_corr(counts(dds_airway)[1:100,])

pcaExplorer User Guide

Federico Marini (marinif@uni-mainz.de) and Harald Binder Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz Center for Thrombosis and Hemostasis (CTH), Mainz

30 August 2017

Abstract

Package version: pcaExplorer 2.2.1

Contents

1 Getting started

2 Introduction

2.1 Citation info

3 Launching the application

4 The controls sidebar

4.1 App settings

4.2 Plot export settings

5 The task menu

6 The app panels

6.1 Data Upload

6.2 Instructions

6.3 Counts Table

6.4 Data Overview

6.5 Samples View

6.6 Genes View

6.7 GeneFinder

6.8 PCA2GO

6.9 Multifactor Exploration

6.10 Report Editor

6.11 About

7 Running pcaExplorer on published datasets

8 Running pcaExplorer on synthetic datasets

9 Functions exported by the package for standalone usage

9.1 pcaplot

9.2 pcaplot3d

9.3 pcascree

9.4 correlatePCs and plotPCcorrs

9.5 hi_loadings

9.6 genespca

9.7 topGOtable

9.8 pca2go

9.9 limmaquickpca2go

9.10 makeExampleDESeqDataSet_multifac

9.11 distro_expr

9.12 geneprofiler

9.13 get_annotation and get_annotation_orgdb

9.14 pair_corr

10 Further development

11 Session info

Federico Marini (marinif@uni-mainz.de) and Harald Binder
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz
Center for Thrombosis and Hemostasis (CTH), Mainz