Run analysis

Set up an analysis in 3 steps.

Step 1. Upload a read count matrix

- Count matrix can be obtained using tools such as HTSeq-count and featureCounts.

- Unique gene IDs as row names and sample IDs as column names. (Important! Unidentify sample IDs)

- Accepted file formats:
(Click to download example)

Loaded data:

Step 2. Specify analysis parameters

- Sample names must match column names in matrx.

- Paired test requires the same number of samples in both group.

- Paired test requires the paired samples to be in the same order.

- Genes with total read counts less than the minimum will be excluded from the results.

- Normalization will only applied to methods without their own internal normalization.

   Upload a file or type in the group and sample names:

- Accept

Step 3. Choose one or multiple DE methods

- Choose one or more DE methods using the check boxes.

- The default methods take about 1 minute in total.

- Save analysis ID to retrieve results later when using any slow methods.

Method description


Send a notice to this email address after the analysis is done (optional).

Browse results

Results will available after being loaded.

Load results using one of these options

List and visualize test statistics

Download result

Compare methods

Method comparison will be available when results from 2+ DE methods are available.

Compare global pattern of test statistics between 2 methods


Compare p values and their ranking between 2 methods


Compare results of a single gene between methods



Meta-analysis will be available when results from 2+ DE methods are available.

Set up and run a meta-analysis

Select DE methods to be included in meta-analysis:

Compare meta-analysis to individual DE methods


Download table


Show all results of a single gene



RNA-seq 2G is a web portal with over 25 statistical methods that perform two-group analysis of differential gene expression. It uses read count data from RNA-seq or similar data matrix as input and generates test statistics in consistent format as output.

Two-group comparison of differential expression (DE) is the most common analysis of transcriptome data. For RNA-seq data, the comparison is usually performed on a gene-level matrix of read counts, with the read counts corresponding to the number of sequencing reads mapped to each gene in each RNA-seq sample.
For RNA-seq data the raw sequencing reads need to be aligned to the reference genome and transcriptome, using any alignment program. Next, the aligned reads should be assigned to annotated genes or transcripts to generate a read count matrix. RNA-seq 2G accepts other types of data, such as those generated by the proteomics and Nanostring technologies, as long as the raw data was processed similarly to generate a integer matrix.
RNAseq 2G provides a user-friendly web portal to run a DE analysis using any of the available methods. Each analysis will be assigned a random ID and its results can be re-visited by specifying the ID. To perform an analysis, go to and set it up with the following 3 steps.
An alternative to use RNA-seq 2G is to directly call the DeRNAseq {DEGandMore} function within R. This option is more suitable for DE analysis runs using any of the slow methods (see Table 1). When any slow DE methods are selected, the online waiting might be too long and users should run the DE analysis offline, but can later upload their results to RNAseq 2G for visualization. Running the offline analysis takes some basic R skills and a few simple steps.