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. Enter mean_expression as the annotation name and MEAN() in the Formula field 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/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/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/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.

Pahway 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/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.

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/Sumbit to Shiny GAM menu.

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

2.1 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 2.3);
• openInBrowser (by default TRUE).

2.2 Loading dataset options

There are three ways to upload a dataset into application:

• As a file from:
  • computer;
  • URL;
  • Dropbox.
• By GEO identifier:
  • with Open file interface;
  • with adding parameter geo to the link (e.g., http://localhost:8000/?geo=GSE27112).
• By an identifier of a dataset saved on the server (see section 2.3)
  • with Saved on server datasets
  • with adding parameter preloaded to 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.

2.3 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]]

## Warning: 64 parsing failures.
##   row             col           expected    actual         file
## 45038 Platform_SPOTID 1/0/T/F/TRUE/FALSE --Control literal data
## 45039 Platform_SPOTID 1/0/T/F/TRUE/FALSE --Control literal data
## 45040 Platform_SPOTID 1/0/T/F/TRUE/FALSE --Control literal data
## 45041 Platform_SPOTID 1/0/T/F/TRUE/FALSE --Control literal data
## 45042 Platform_SPOTID 1/0/T/F/TRUE/FALSE --Control literal data
## ..... ............... .................. ......... ............
## See problems(...) for more details.

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.

2.4 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 folder.