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2. RNA-Seq & Omics Data Analysis Software — Visualize and Understand Your Data | Subio Platform

You followed the tutorial.
You ran the pipeline.
You got results.

But then you wonder—

• Is this normalization really appropriate?
• Does this cluster mean anything?
• Can these p-values be trusted?

Have you ever ended your analysis with these questions still unanswered?

R-based tools such as DESeq2 play an important role in RNA-Seq analysis.
However, beginner-oriented learning often emphasizes running commands and getting results first.

As a result, it is easy to follow commands step by step
without sufficiently examining data distributions or differences between samples.

GUI tools make it easy to obtain results.
However, they may not always make it easy to examine the assumptions behind the process
or the state of the data itself.

Subio Platform gives you a way to examine these questions visually.
It lets you analyze your data while actually seeing and understanding it.

Stop Running Tools. Start Understanding Your Data.

With Subio Platform, you can finally:

• Detect batch effects before they mislead your analysis
• Verify your results by examining the data behind them
• Combine visual workflows with R, Python, and AI
• Turn your data into a reusable research asset

Trusted by researchers since 2009.
See publications and user voices.

Most RNA-Seq tools run the workflow.
Subio Platform helps you understand it—visually.

See what’s really happening in your data—before you trust the results.

No programming required. Works on Windows and Mac.
Start exploring your data right away.
Start RNA-Seq Tutorial
No setup required. Includes demo data.

Statistical tools such as DESeq2 and GSEA play an important role in RNA-Seq analysis.
However, obtaining statistical results is not the same as understanding, validating,
and applying those results in your research.

When analysis is completed with statistical tools alone,
modern omics analysis can face the following challenges.

Beyond Statistical Tools:
Four Challenges Often Encountered in Modern Omics Analysis

1. Lack of Visual Validation Risk of Missing Hidden Biases
Without visualization, batch effects and artifacts may go unnoticed.

2. Lack of Continuity Lost Knowledge and Poor Reproducibility
Can you reproduce or explain your analysis months later?

3. Low Efficiency Time Lost Outside Analysis
Too much time spent fixing environments and debugging scripts.

4. Lack of Exploration Static Reports Can End Exploration Too Early
Excel or PDF outputs can limit further exploration.

Different tools offer different strengths and limitations.

The table below summarizes how they compare in usability, interpretability, and flexibility.

Subio Platform provides a GUI designed specifically for omics data.

• Instantly visualize batch effects and biases
• Detect issues that may be less visible in some workflows
• Validate normalization and preprocessing visually

Why visualization matters

Visualization is an essential part of reliable analysis.

Batch effects are not uncommon, especially in large datasets.

See the relationship between clusters and batch effects at a glance.

View the data distribution of each sample alongside your main plots.
With Subio Platform, you can visually verify your data at every step of the analysis.

Want to see a real example?

• RNA-Seq normalization pitfalls (TPM and FPKM) — Case Study 379

Even when using correction methods like ComBat,
you must verify whether the correction actually worked.

Without seeing raw data:

• You may mistake technical artifacts for biological signals
• You may trust “clean” normalized data that hides real problems

Good analysis starts with one simple rule:
A useful principle is to verify data visually before trusting it.

Subio Platform complements — not replaces — R and Python.

• Use Subio for intuitive visualization and data management
• Use R/Python for advanced statistical analysis
• Safely run AI-generated scripts in your local environment

Typical workflow

1. Explore and prepare data in Subio
2. Run analysis in R/Python (or AI-generated code)
3. Import results back into Subio for validation and interpretation

For large datasets:

• Use Subio for exploratory analysis (e.g., ~100 samples)
• Then scale with scripts for full datasets

Your data shouldn’t disappear after one analysis.

With Subio:

• Integrate your own data with GEO and TCGA datasets
• Reanalyze past experiments instantly
• Compare across platforms (RNA-Seq, microarray, etc.)

Your data becomes a living, searchable archive.

Subio Platform itself is provided free of charge.
Data visualization and data management are available for free,
while more advanced analysis functions and support are provided through plug-ins and analysis services.

If you would like to see what you can do with the free core features, please also see:
What You Can Do with the Free Core Features of Subio Platform

For pricing details, please see the price list.

At the same time, Subio Platform is designed as an environment for long-term research use.

• Less affected by OS updates and changes in the analysis environment
• Helps reduce the risk of research projects being disrupted by changes in analysis tools

Subio’s SSA format goes beyond static results.

It packages:

• Raw data
• Analysis results
• Parameters
• Notes and documents

into a single file.

This enables:

• Instant reproducibility
• Interactive reanalysis by collaborators
• A shift from “reporting” to discussion-driven science

What happens when you receive an SSA file?

This 90-second video shows how recipients can open an SSA file,
inspect the data, and start reanalysis without rebuilding the environment.

Visualization is not just a “nice-to-have” feature.
It is a critical process that determines whether your analysis is trustworthy.

Most tutorials focus on command-line operations.
But without visualization, you are effectively proceeding without fully understanding the overall structure of your data.

Batch effects are not a special case.
In omics data, they should be assumed to exist until proven otherwise.
The risk increases as the size of the dataset grows.
Even in small datasets, it cannot be assumed to be completely absent.

Correction methods such as ComBat will automatically produce results.
However, it is up to the analyst to verify whether the correction has actually worked.

If you inspect the raw data, you may recognize that a particular cluster is likely caused by batch effects.
But if you only analyze the corrected, superficially adjusted data,
you may mistakenly interpret such clusters as biologically meaningful.

This is very similar to what was often observed in microarray data analysis after RMA normalization.
(An example where data “cleaned” by RMA does not necessarily reflect the underlying biological reality is here.)

Reliable analysis starts with a simple premise:
assume bias may exist—and verify it visually.

Explore Real-World Examples That Change How You Analyze Data

• Apparent Differences That Can Appear After CPM Normalization
  A real-data example of unexpected effects of normalization
• Before Taking FPKM/TPM Data as “Cleaned”
  The importance of data validation, shown with GSE159751
• Why Count Data Outperforms FPKM/TPM in RNA-Seq Analysis
  Understand what actually improves reliability.
• Why You Need Multiple Visualizations — Not Just One
  Avoid misleading conclusions from single plots.

In practice, data import, normalization, and preprocessing can all be handled within Subio Platform.
Filtering and standard statistical analyses (such as t-tests, PCA, and clustering) can be performed easily using Plug-ins.

For more advanced or specialized analyses, data can be exported as tab-delimited text and passed to R or Python,
with the results then re-imported into Subio Platform—maintaining a consistent analytical workflow.

As dataset size increases (as a rough guideline, around 100 samples or more),
the speed and scalability of command-line tools become increasingly valuable.
However, applying pipelines directly to the full dataset may lead to suboptimal or misleading results.

A more practical approach is to first analyze a subset of the data using Subio Platform’s visualization capabilities,
clarify your analysis strategy, and then implement it as a script to run across the full dataset.

Even without coding expertise, researchers can now generate R/Python scripts using AI.
However, determining whether the results are valid remains the responsibility of the analyst.

Subio Platform enables you to manage your data locally while integrating seamlessly with R and Python,
allowing the entire analysis workflow to remain within a local environment.
At the same time, it provides a framework to visually verify AI-generated results with your own eyes.

1. [Preparation] Use Subio to understand the data and export datasets for scripting
2. [Computation] Execute AI-generated code in R/Python
3. [Interpretation] Bring results back into Subio for visual validation and interpretation

For an overview of this hybrid analysis approach, see the following article with practical examples:

• A New Style of RNA-Seq Data Analysis | Analyze with R/Python × Visualize and Manage Data with Subio

For step-by-step examples, see:

• Integrate Subio Platform with R (generate code with ChatGPT and run edgeR and DESeq2)
• Integrate Subio Platform with Python (generate code with ChatGPT and run it in Jupyter)

Cross-dataset insights

Case Study 339: Gene expression is consistent across datasets. miRNA is not.
When multiple hepatocellular carcinoma (HCC) datasets were compared, gene expression remained consistent across platforms (RNA-Seq and microarrays), whereas miRNA expression showed striking discrepancies between datasets.
Importantly, miRNA expression appears internally consistent within each dataset. However, this issue cannot be recognized from a single dataset—it only becomes evident when multiple datasets are compared.

Case Study 189: Cross-study consistency builds confidence
When independent datasets on mouse mammary gland development were compared, highly consistent expression patterns were observed across studies.
This demonstrates that by accumulating data and enabling cross-dataset comparisons, it becomes possible to assess the reliability of information and identify results that can be trusted.

Scan Genes over Series tool (System Plug-in):
This feature enables searches similar to GEO Profiles, but applied to your own accumulated datasets within Subio Platform.
You can instantly explore how a gene of interest behaves across different datasets (Series),
transcending differences in species and experimental platforms.

Subio Platform supports a wide range of omics data in the following formats. It also allows you to import and directly re-analyze data from public databases such as GEO and TCGA.

• Tabular data (tab-delimited): RNA-Seq, microarray, DNA methylation, proteomics
• Sequence data: RNA-Seq FASTQ files
• Genome browser formats: BED / WIG / BigWig (primary analysis results such as ChIP-Seq and ATAC-Seq)

If you find it difficult:

Public databases contain a wide variety of omics data, and you may encounter data types that are unfamiliar.
Working with such data requires both broad knowledge and practical experience.

If you find certain datasets difficult to handle, you can use Subio’s Data Analysis Service.
We can process your requested datasets and deliver them in SSA format, ready to be added to your archive.

90-Second Demo: What Happens When You Receive an SSA File

Watch this short demo first to see how an SSA file lets collaborators open, explore, and reanalyze omics data immediately.

• Researchers who want to understand their data deeply
• Labs managing multiple datasets
• Scientists integrating public and private data

• Those focused only on programming skills
• One-off analyses with no need for reuse
• Fully automated pipelines without interpretation

• Tutorials, case studies, and troubleshooting
• Data analysis services
• Online training

These are common challenges researchers face when using standard RNA-Seq tools.

Why do many RNA-Seq tools fail to detect batch effects?

Many RNA-Seq tools focus on executing pipelines, and may provide limited support for visual inspection. As a result, batch effects and technical biases can remain hidden.

Subio Platform allows you to visualize data distributions and sample relationships at every step, making it easier to detect issues before they affect your results.

Why is it difficult to interpret RNA-Seq results with standard tools?

Standard tools often produce statistical outputs without providing sufficient context for interpretation. This can make it hard to determine whether results reflect true biological signals or artifacts.

Subio Platform helps you interpret results by combining statistical outputs with visual exploration, so you can understand what is happening in your data.

What are the limitations of GUI-based tools?

While GUI tools improve usability, many are optimized for ease of use, which may limit deeper analysis in some cases. They may limit flexibility or prevent detailed validation of intermediate steps.

Subio Platform combines an intuitive GUI with the ability to integrate R, Python, and AI workflows, allowing both ease of use and analytical depth.

Thank you for downloading.

Start by loading your data into Subio Platform
and visually checking distributions and potential batch effects.

If you are new, you can begin with a tutorial using demo data:
RNA-Seq Tutorial

With Subio Platform,
data visualization and management are fully available for free.

Core analysis steps (such as statistical analysis, clustering, and differential expression analysis)
can also be performed using R or Python at no cost.

However, this often requires writing and adjusting code, which can take significant time and effort.

By using plug-ins,
you can perform these analyses more efficiently,
without spending time on operations—
allowing you to focus on understanding your data and interpreting the results.

Some functions are also available only through plug-ins,
and can be difficult to reproduce using scripts alone.

Plug-ins are affordably priced,
and you can start with a 5-day free trial.

Results generated using plug-ins remain fully accessible,
even after the plug-in license has expired—
your data and results are always yours.

Plug-ins and plug-in bundles are 30% off for first-time users.

In addition,

You can receive $50 or €40 off per referral link
(based on your billing currency, up to 50% total discount)
* To apply, publish your link before purchase
and include the URL when requesting a quote.
* Available for both new and existing users.

See all discount options and conditions

• Learn the workflow step by step and explore at your own pace
  RNA-Seq Analysis Tutorial
• Move forward with your own data efficiently while building understanding
  Data Analysis Service
  Online Training