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.
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.
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:
This command runs the application with the default parameters,
opens it in the default browser (from
with address http://0.0.0.0:8000.
The starting screen should appear:
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
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.
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
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
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.
OK. A file in text GCT format
will be downloaded.
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.
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.
Tool/Hierarchical clustering menu can be used to cluster samples and highlight outliers (and concordance of other samples) even further.
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).
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 \@ref(serving-phantasus).
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
Note: Self-hosted Phantasus requires additional steps to make AnnotationDB work properly: see section \@ref(serving-phantasus).
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 --
Source column type --
ENTREZID, and Result column type --
Clicking OK will append a new column with correspoding
ENSEMBL identifier to the gene annotation.
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.
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.
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
Also there are options to control orientation of the plot, the ranking column
Current progress on the dataset can be shared via link. Go to File/Get link to a dataset. This will provide you a link, to a current dataset.
Note: The following warning about 30 days to live is not applicable to self-hosted Phantasus
There are three ways to upload a dataset into application:
geoto the link (e.g., http://localhost:8000/?geo=GSE27112).
preloadedto the link (e.g., http://localhost:8000/?preloaded=fileName)
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.
Some of Phantasus features require additional set up.
You can customise serving of the application by specifying following parameters:
tempdir()) -- directory where downloaded datasets will be saved and reused in later sessions;
NULL) -- directory with
ExpressionSetobjects encoded in rda-files, that can be quickly loaded to application by name (see section \@ref(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
into an RData file with
.rda extension into a directory as specified in
Let us preprocess and save
library(GEOquery) library(limma) gse14308 <- getGEO("GSE14308", AnnotGPL = TRUE)[] 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
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.
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
mouse_matrix.h5 should be placed
into Phantasus cache under archs4 folder.
Or you can call
FGSEA requires pathway database in
.rds files under
Pathway database is an
.rds file containing dataframe with columns:
geneSymbol. You can see an example dataframe by entering:
data("fgseaExample", package="phantasus") head(fgseaExample)
AnnotationDB tool requires annotation databases
<cacheDir>/annotationdb folder. For example you can get
Mus Musculus database package from Bioconductor, extract
org.Mm.eg.sqlite, and put it to
You can see known issues and submit yours at GitHub: (https://github.com/ctlab/phantasus/issues)
The authors are very thankful to Joshua Gould for developing Morpheus.
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
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