This tutorial describes Phantasus – a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API.

The main object in Phantasus is a gene expression matrix. It can either be uploaded from a local text or Excel file or loaded from Gene Expression Omnibus (GEO) database by the series identifier (both microarray and RNA-seq datasets are supported). Aside from basic visualization and filtering methods as implemented in Morpheus, R-based methods such as k-means clustering, principal component analysis, differential expression analysis with limma package are supported.

In this vignette we show example usage of Phantasus for analysis of public gene expression data from GEO database. It starts from loading data, normalization and filtering outliers, to doing differential gene expression analysis and downstream analysis.

1 Example workfow for analysing gene expression changes in macrophage activation

To illustrate the usage of Phantasus let us consider public dataset from Gene Expression Omnibus (GEO) database GSE53986. This dataset contains data from experiments, where bone marrow derived macrophages were treated with three stimuli: LPS, IFNg and combined LPS+INFg.

1.1 Starting application

The simplest way to try Phantasus application is to go to web-site https://genome.ifmo.ru/phantasus or its mirror https://artyomovlab.wustl.edu/phantasus where the latest version is deployed.

Alternatively, Phantaus can be start locally using the corresponding R package:

library(phantasus)
servePhantasus()

This command runs the application with the default parameters, opens it in the default browser (from browser option) with address http://0.0.0.0:8000. The starting screen should appear:

1.2 Preparing the dataset for analysis

Opening the dataset

Let us open the dataset. To do this, select GEO Datasets option in Choose a file… dropdown menu. There, a text field will appear where GSE53986 should be entered. Clicking the Load button (or pressing Enter on the keyboard) will start the loading. After a few seconds, the corresponding heatmap should appear.

On the heatmap, the rows correspond to genes (or microarray probes). The rows are annotated with Gene symbol and Gene ID annotaions (as loaded from GEO database). Columns correspond to samples. They are annotated with titles, GEO sample accession identifiers and treatment field. The annotations, such as treatment, are loaded from user-submitted GEO annotations (they can be seen, for example, in Charateristics secion at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1304836). We note that not for all of the datasets in GEO such proper annotations are supplied.

Adjusting expression values

By hovering at heatmap cell, gene expression values can be viewed. The large values there indicate that the data is not log-scaled, which is important for most types of gene expression analysis.

For the proper further analysis it is recommended to normalize the matrix. To normalize values go to Tools/Adjust menu and check Log 2 and Quantile normalize adjustments.

The new tab with adjusted values will appear. All operations that modify gene expression matrix (such as adjustment, subsetting and several others) create a new tab. This allows to revert the operation by going back to one of the previous tabs.

Removing duplicate genes

Since the dataset is obtained with a mircroarray, a single gene can be represented by several probes. This can be seen, for example, by sorting rows by Gene symbol column (one click on column header), entering Actb in the search field and going to the first match by clicking down-arrow next to the field. There are five probes corresponding to Actb gene in the considered microarray.

To simplify the analysis it is better to have one row per gene in the gene expression matrix. One of the easiest ways is to chose only one row that has the maximal median level of expression across all samples. Such method removes the noise of lowly-expressed probes. Go to Tools/Collapse and choose Maximum Median Probe as the method and Gene ID as the collapse field.

The result will be shown in a new tab.

Filtering lowly-expressed genes

Additionally, lowly-epxressed genes can be filtered explicitly. It helps to reduce noise and increase power of downstream analysis methods.

First, we calculate mean expression of each gene using Tools/Create Calculated Annotation menu. Select Mean operation, optionally enter a name for the resulting column, and click OK. The result will appear as an additional column in row annotations.

Now this annotation can be used to filter genes. Open Tools/Filter menu. Click Add to add a new filter. Choose mean_expression as a Field for filtering. Then press Switch to top filter button and input the number of genes to keep. A good choice for a typical mammalian dataset is to keep around 10–12 thousand most expressed genes. Filter is applied automatically, so after closing the dialog with Close button only the genes passing the filter will be displayed.

It is more convenient to extract these genes into a new tab. For this, select all genes (click on any gene and press Ctrl+A) and use Tools/New Heat Map menu (or press Ctrl+X).

Now you have the tab with a fully prepared dataset for the further analysis. To easily distinguish it from other tabs, you can rename it by right click on the tab and choosing Rename option. Let us rename it to GSE53986_norm.

It is also useful to save the current result to be able to return to it later. In order to save it use File/Save Dataset menu. Enter an appropriate file name (e.g. GSE53986_norm) and press OK. A file in text GCT format will be downloaded.

1.3 Exploring the dataset

PCA Plot

One of the ways to asses quality of the dataset is to use principal component analysis (PCA) method. This can be done using Tools/Plots/PCA Plot menu.

You can customize color, size and labels of points on the chart using values from annotation. Here we set color to come from treatment annotation.

It can be seen that in this dataset the first replicates in each condition are outliers.

K-means clustering

Another useful dataset exploration tool is k-means clustering. Use Tools/Clustering/k-means to cluster genes into 16 clusters.

Afterwards, rows can be sorted by clusters column. By using menu View/Fit to window one can get a “bird’s-eye view” on the dataset. Here also one can clearly see outlying samples.

Hierarchical clustering

Tool/Hierarchical clustering menu can be used to cluster samples and highlight outliers (and concordance of other samples) even further.

Filtering outliers

Now, when outliers are confirmed and easily viewed with the dendrogram from the previous step, you can select the good samples and extract them into another heatmap (by clicking Tools/New Heat Map or pressing Ctrl+X).

1.4 Differential gene expression

Apllying limma tool

Differential gene expression analysis can be carried out with Tool/Diffential Expression/limma menu. Choose treatment as a Field, with Untreated and LPS as classes. Clicking OK will call differential gene expression analysis method with limma R package.

The rows can be ordered by decreasing t-statistic column to see which genes are the most up-regulated upon LPS treatment.

Pathway analysis with FGSEA

Note: Self-hosted Phantasus requires additional steps to make FGSEA work properly: see section 3.

The results of differential gene expression can be used for pathway enrichment analysis with FGSEA tool.

Open Tools/Pathway Analysis/Perform FGSEA, then select Pathway database, which corresponds specimen used in dataset (Mus Musculus in this example), ranking column and column with ENTREZID or Gene IDs.

Clicking OK will open new tab with pathways table.

Clicking on table row will provide additional information on pathway: pathway name, genes in pathway, leading edge. You can save result of analysis in TSV format.

2 Advanced usage

2.1 Gene identifiers conversion with AnnotationDB

Note: Self-hosted Phantasus requires additional steps to make AnnotationDB work properly: see section 3.

AnnotationDB provides an opportunity to convert annotation columns in dataset. Open Tools/Annotate/Annotate Rows/From Database. Example: converting ENTREZID column in GSE53986 to ENSEMBL. Select org.Mm.hg.sqlite specimen database with Source column – Gene ID, Source column type – ENTREZID, and Result column type – ENSEMBL.

Clicking OK will append a new column with correspoding ENSEMBL identifier to the gene annotation.

2.2 Pathway analysis with Enrichr

The results of differential gene expression can be used, for example, for pathway enrichment analysis with online tools such as MSigDB or Enrichr.

For ease of use Enrichr is integrated into Phantasus. Open Tools/Pathway Analysis/Submit to Enrichr menu and select about top 200 genes up-regulated on LPS. Also select Gene symbol as the column with gene symbols.

Clicking OK will result in a new browser tab with Enrichr being opened with results of pathway enrichment analysis.

2.3 Metabolic network analysis with Shiny GAM

Another analysis integrated into Phantasus is metabolic network analysis with Shiny GAM. After differential expression you can submit the table with limma results using Tools/Pathway Analysis/Sumbit to Shiny GAM menu.

After successful submission, a new browser tab will be opened with Shiny GAM interface and the submitted data.

2.4 GSEA enrichment plot

The results of pathway analysis with FGSEA can be used for generating enrichment plot. Clicking on table row will provide you a list of Gene IDs. Copy the list, switch back to dataset tab and paste to search field. This will select the corresponding genes in dataset. Then proceed to Tools/Plots/GSEA plot Then use Export to SVG button to save plot in .svg format. Also there are options to control orientation of the plot, the ranking column and annotation.

2.6 Loading dataset options

There are three ways to upload a dataset into application:

You can either open the dataset from the main page, or if you are already looking at some datasets and don’t want to lose your progress you can use File/Open (Ctrl+O), choose Open dataset in new tab and then select the open option.

3 Serving Phantasus

Some of Phantasus features require additional set up.

3.1 Options for servePhantasus

You can customise serving of the application by specifying following parameters:

  • host and port (by default: "0.0.0.0" and 8000);
  • cacheDir (by default: tempdir()) – directory where downloaded datasets will be saved and reused in later sessions;
  • preloadedDir (by default: NULL) – directory with ExpressionSet objects encoded in rda-files, that can be quickly loaded to application by name (see section 3.2);
  • openInBrowser (by default TRUE).

3.2 Preloaded datasets

Preloaded datasets is a feature that allows quick access to frequently-accessed datasets or to share them inside the research group.

To store dataset on a server, on nead to save list ess of ExpressionSet objects into an RData file with .rda extension into a directory as specified in servePhantasus.

Let us preprocess and save GSE14308 dataset:

library(GEOquery)
library(limma)
gse14308 <- getGEO("GSE14308", AnnotGPL = TRUE)[[1]]
gse14308$condition <- sub("-.*$", "", gse14308$title)
pData(gse14308) <- pData(gse14308)[, c("title", "geo_accession", "condition")]
gse14308 <- gse14308[, order(gse14308$condition)]

fData(gse14308) <- fData(gse14308)[, c("Gene ID", "Gene symbol")]
exprs(gse14308) <- normalizeBetweenArrays(log2(exprs(gse14308)+1), method="quantile")

ess <- list(GSE14308_norm=gse14308)

preloadedDir <- tempdir()

save(ess, file=file.path(preloadedDir, "GSE14308_norm.rda"))

Next we can serve Phantasus with set preloadedDir option:

servePhantasus(preloadedDir=preloadedDir)

There you can either put GSE14308_norm name when using open option Saved on server datasets or just open by specifying the name in URL: http://localhost:8000/?preloaded=GSE14308_norm.

3.3 Support for RNA-seq datasets

Phantasus supports loading RNA-seq datasets from GEO using gene expression counts as computed by ARCHS4 project. To make it work one need to download gene level expression from the Download section. The downloaded files human_matrix.h5 and mouse_matrix.h5 should be placed into Phantasus cache under archs4 folder. Or you can call

updateARCHS4(cacheDir=cacheDir)

3.4 Pathway database for FGSEA

FGSEA requires pathway database in .rds files under <cacheDir>/fgsea folder. Pathway database is an .rds file containing dataframe with columns: geneID, pathName, geneSymbol. You can see an example dataframe by entering:

data("fgseaExample", package="phantasus")
head(fgseaExample)
##   geneID                                pathName geneSymbol
## 1  11461 5991955_Cell-cell_junction_organization       Actb
## 2  11465 5991955_Cell-cell_junction_organization      Actg1
## 3  12385 5991955_Cell-cell_junction_organization     Ctnna1
## 4  12388 5991955_Cell-cell_junction_organization     Ctnnd1
## 5  12550 5991955_Cell-cell_junction_organization       Cdh1
## 6  12552 5991955_Cell-cell_junction_organization      Cdh11

3.5 Annotation database for AnnotationDB tool

AnnotationDB tool requires annotation databases under <cacheDir>/annotationdb folder. For example you can get Mus Musculus database package from Bioconductor, extract org.Mm.eg.sqlite, and put it to <cacheDir>/annotationdb folder.

4 Feedback

You can see known issues and submit yours at GitHub: (https://github.com/ctlab/phantasus/issues)

5 Acknowledgments

The authors are very thankful to Joshua Gould for developing Morpheus.

6 Citation

Please cite us as:

Zenkova D., Kamenev V., Sablina R., Artyomov M., Sergushichev A. Phantasus: visual and interactive gene expression analysis. https://genome.ifmo.ru/phantasus doi: 10.18129/B9.bioc.phantasus