Frequently Asked Questions

To run GSEA using DecoPath, you will need:

• An expression dataset
• A file containing class labels (e.g., normal and tumor) for samples in the expression dataset.

You can submit RNA-Seq, microarray, and ChIP-Seq data. Gene identifiers must be HUGO Gene Nomenclature Committee (HGNC) symbols.

The expression data you submit to run GSEA should be in a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) file. The first and second column labels should be 'name' and 'description', respectively. All subsequent column labels should be the sample identifiers. Rows should contain an HGNC symbol, a description (which can be left as NA) and all subsequent columns should contain the expression value for that gene for each of the samples:

gene_symbol	description	sample_1	sample_2	sample_3
TSPAN6	NA	17.844	15.288	19.747
CFH	NA	145.610	209.655	93.282
C1orf112	NA	0.387	0.562	0.144

Much like a categorical class (CLS) file format, this file (a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) file) should contain specifications on the labels for each of the samples in the dataset.

For example, for a dataset with expression values for samples that fall into two distinct classes (e.g., normal and tumor), the class labels file should have a column with the same sample identifiers as the ones in the expression dataset file you submit and another column with their corresponding class labels. Please ensure the order of the samples in the expression dataset is the same as the order of samples in the class labels file. Below is an example of a class labels file:

identifier	class_label
sample_1	normal
sample_2	normal
sample_3	tumor
sample_4	tumor

Yes, GSEA can be used to analyze a pre-ranked list of genes using DecoPath.

To run GSEA pre-ranked, upload a file in a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) format that contains the following 2 columns:

• HGNC symbols
• Class difference metric for the HGNC symbols

In this case, do not include a header (column names) in the file. See the example below:

TSPAN6	2.1
CFH	1.5
C1orf112	0.6
AKT1	-1.3

To run ORA using DecoPath, you will need:

• A gene list

To run ORA, submit a file that contains a single column with HGNC symbols in a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) format.

You can also submit the results of an enrichment analysis and use the DecoPath visualizations and functionalities to compare and explore the consensus around different pathway databases. If you do opt to submit your own results, you must use the databases provided in DecoPath. We strongly recommend using gene set files from the following links:

• KEGG
• PathBank
• Reactome
• WikiPathways
• DecoPath

If you submit ORA results, ensure they are in a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) text file and the file contains the columns: pathway, p_value, q_value. For example:

pathway	p_value	q_value
WP1403	1.18e-06	9.35e-06
WP411	2.61e-09	5.72e-08
hsa04210	0.074	0.1

If you submit GSEA results, ensure they are in a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) text file and the file contains the columns: pathway, es, nes, p_value and q_value. For example:

pathway	es	nes	p_value	q_value
WP2636	-0.25	-0.25	0.2	0.22
hsa05031	-0.3	-0.13	2.61e-07	2.61e-09
hsa04210	0.074	0.1	2.3e-02	1.2e-03

No, this is an optional field and can be skipped.

While you can run DecoPath without performing differential expression analysis, we also generate visualizations to identify genes that are differentially expressed according to a fold change cutoff.

The file you submit containing log2 fold changes should be a comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) file with the columns, gene_symbol, log2fc, p-value and q-value. Please ensure that the log2 fold changes of the genes uploaded correspond to the expression dataset or GSEA results file of the same experimental groups in the case of GSEA and to the log2 fold changes of genes in the gene list in the case of ORA. For example:

gene_symbol	log2fc	p_value	q_value
AK2	-0.16	0.0017	0.002
CD38	1.97	0.1	0.11
FKBP4	-0.79	4.15e-5	2.59e-4

You will require two files (comma-separated (*.csv), tab-separated (*.tsv) or plain-text (*.txt) files):

• un-normalized read counts in the form of a matrix of integer values
• class labels

The files must be consistently ordered such that the samples in the counts matrix are in the same order as the samples in the class labels file. If you are running GSEA, you only need to submit one class labels file corresponding to both the expression dataset to perform GSEA and the counts matrix to calculate the fold changes. Example files are given below:

Counts matrix

---

gene_symbol	sample_1	sample_2	sample_3
TSPAN6	2393	3187	4964
CFH	641	182	404
C1orf112	321	474	198

Class labels

---

identifier	class_label
sample_1	normal
sample_2	normal
sample_3	tumor
sample_4	tumor

Currently, we only provide 4 databases (i.e., KEGG, Reactome, WikiPathways and PathBank) to run pathway analysis. You can add more databases by uploading files in the gene matrix transposed (GMT) format containing HGNC symbols and their corresponding gene sets. You must also upload corresponding mapping files between each database you select as input.

So, if you wish to run the comparative analysis on KEGG, Reactome and your uploaded database, you need to first create mappings between KEGG and your uploaded database as well as Reactome and your uploaded database. For example, 'Citric acid cycle (TCA cycle)' pathway from Reactome is equivalent to 'TCA Cycle' from WikiPathways.

You can see example files here:

• GMT file
• Mapping file

If you have prepared these files, you can follow this link to run DecoPath using your preferred gene set databases.

You must register with an email address to use DecoPath. Once you have registered, you can view the experiments you have run on the Experiments page.

You can delete your account at anytime on the Account page.