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Cell Response Discovery
Understand how cells respond to genetic or chemical perturbations across conditions, models, and datasets, revealing patterns missed by single-study analysis.
Discover from Perturbation Data
Cell Response Discovery Software
Explore, Compare & Interpret Perturbation Data across experiments pathways, diseases, without pipelines, infrastructure, complexity, or specialized expertise.
The Power of Cell Response
Understanding Biology Through Perturbation
Chemical and genetic perturbations trigger measurable cellular responses that reveal biological pathways, mechanisms, and therapeutic opportunities. protoXell helps researchers explore these responses across experiments at scale.
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Mechanism of Action Clarity
Connect perturbation signatures to biological pathways and molecular mechanisms, moving from observation to understanding.
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Novel Therapeutic Identification
Uncovering unexpected biological connections to identify new therapeutic opportunities beyond hypothesis-driven approaches.
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Drug Repositioning
Identify existing compounds with signatures aligned to new disease indications, surfacing hidden opportunities in known molecules.
Challenges
But Understanding Perturbation Data is Still hard.
Advances in single-cell perturbation sequencing have created unprecedented visibility into cellular responses. Yet translating these signals into biological insight remains slow, fragmented, and resource-intensive. As a result, answering even simple biological questions can take weeks or months.
Data fragmented across experiments, modalities, and teams
Perturbation datasets spanning gene targets, compounds, and disease models are disconnected, limiting cross-study comparison and integrative analysis.
Infrastructure complexity
slows analysis
slows analysis
Large-scale perturbation data requires sophisticated, compute-intensive pipelines that are difficult to scale across cloud and HPC environments.
Interpreting results requires specialized computational expertise
Extracting insights requires coding skills and deep understanding of data structures, statistical models, and algorithms.
Connecting results to mechanisms and literature is time-consuming
Linking computational findings to known molecular pathways and prior studies requires extensive manual curation and domain expertise.
We Made It Easy
See how protoXell instantly unlocks a new level of discovery
Exploring perturbation data thoroughly can revolutionize your understanding of biology and advance therapy development.
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Core Capabilities
How protoXell Works
protoXell combines curated perturbation data, comparative analytics, AI-powered interpretation, and enterprise deployment flexibility into a unified scientific workflow.
Easily explore large-scale, harmonized datasets across cell lines, tissues, and conditions—continuously updated and analysis-ready.
Enterprise deployments can extend the catalog to include proprietary data.
Enterprise deployments can extend the catalog to include proprietary data.
Efficiently compare perturbation effects across experiments to uncover shared and distinct biological responses at the gene and pathway level.
inXighter Interpreter, DataXight’s AI-powered capability, connects analytical results to underlying biological mechanisms and relevant scientific literature, accelerating hypothesis generation and validation.
Deploy protoXell across cloud, on-premise, or hybrid environments with a platform-agnostic architecture. Integrate proprietary data and existing systems as part of enterprise deployments.
Individually, these capabilities are powerful.
Together, they create something fundamentally new.
Where Capabilities
Converge
At the intersection of data access, comparative analysis, and AI-driven interpretation, protoXell enables a new mode of discovery—where researchers can move seamlessly from perturbation data to mechanistic insight.
This unified system connects diverse data sources to biological understanding, supporting hypothesis generation, target discovery, and decision-making.
Inputs
Giga large perturbation datasets
Target gene of interest
Proprietary Data
Biological pathway
Biology-
Grounded AI
Grounded AI
Cross-Study
Insight
Insight
AI-Augmented
Mechanistic Insights
Mechanistic Insights
Insights
Mechanism of Action
Drug Repositioning
Cell Response Discovery
Novel Drug Targets
- Access curated perturbation catalog
- Integrate public + proprietary data
- Query across experiments at scale
Perturbation Intelligence
Biology-
Grounded AI
Grounded AI
Cross-Study
Insight
Insight
AI-Augmented
Mechanistic Insights
Mechanistic Insights
AI-Driven Insight
- Interpret results with AI
- Support virtual cell modeling
- Power LLM-driven agents
Comparative Insight Engine
- Compare perturbations
- Uncover biological relationships
- Reveal mechanisms of action
This unified system connects diverse data sources to biological understanding, supporting hypothesis generation, target discovery, and decision-making.
Giga large perturbation datasets
Target gene of interest
Proprietary Data
Biological pathway
Inputs
Insights
Mechanism of Action
Drug Repositioning
Cell Response Discovery
Novel Drug Targets
Make Previously
Impractical
Analyses Routine
Impractical
Analyses Routine
FAQs
Have questions? We're here to help.
Any more questions?
protoXell is a scientific software for cell response discovery, enabling you to explore, compare & interpret perturbation data across experiments, without pipelines, infrastructure, complexity, or specialized expertise.
It unifies data access, comparative analysis, and AI-driven interpretation into a single, continuous workflow, enabling researchers to move from raw data to mechanistic insight in minutes, not months.
protoXell supports both genetic and chemical perturbation data across diverse experimental systems, including cell lines and in vivo models. It also enables cross-species analysis through homolog mapping (e.g., human, mouse).
The platform is optimized to scale across complex experimental designs, enabling consistent analysis and comparison across large, heterogeneous datasets.
protoXell is built on a curated perturbation data catalog from large-scale public datasets (e.g., Tahoe100, Xaira/Orion), harmonized for consistent cross-study analysis. New data is continuously added, with availability as often as 3 days after a dataset is requested.
protoXell also supports private data integration, allowing you to bring your own datasets and have them harmonized into the same catalog for unified analysis.
Yes. protoXell is designed to be platform-agnostic, with modular components that can be deployed within your own infrastructure. Individual components can run on separate environments, allowing you to integrate flexibly with your existing systems while keeping full control over your data.
Yes. Security is a core priority. When deployed in your private cloud, all data remains within your controlled environment. In a SaaS deployment, protoXell follows strict security and privacy standards in line with our terms, ensuring your data is protected. Customer data is never used to train shared AI models without explicit agreement.
protoXell uses large language models (LLMs) through the inXighter tool to explain and summarize analysis results, connecting computational outputs to biological context and helping reduce manual literature review.
The LLM does not generate or alter results; it only interprets deterministic outputs to accelerate understanding and insight generation.
Related Articles
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2 mins readDataXight Launches protoXell to Unlock Mechanistic Insight from Large-Scale Perturbation Data
New scientific software enables researchers to explore chemical and genetic perturbations, accelerating target discovery and drug repurposing MOUNTAIN VIEW, CA – May 19, 2026 – Addressing the persistent challenge scientists face in transforming complex biological perturbation data into actionable mechanistic insight, DataXight today announced protoXell, a new scientific software designed to streamline discovery. To learn more about protoXell and explore access options, visit https://dataxight.c
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7 mins readComparing Perturbations: E-distance and Euclidean distance are Your Best Allies
Summary Our benchmarking reveals a surprising truth: in the race to translate massive perturbation datasets into discovery, the most effective mathematical "lens" isn't the most complex one. While sophisticated metrics like Wasserstein or Mean Pairwise are often favored due to their mathematical impressiveness, we found that E-distance and Euclidean distance provide the superior balance of speed and signal resolution for high-throughput pipelines. By delivering sharper biological contrast at a
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5 mins readPerturbation effect is not an on-off switch
In this blog, we examine how the “perturbation effect” can vary depending on the metrics used to define it, and why these differences matter. While these metrics may appear interchangeable, they often capture fundamentally different aspects of the underlying biology. As Perturb-seq datasets continue to grow exponentially, understanding how perturbation effects are measured becomes critical for reliable downstream analysis. When suppression is not an on-off switch In 2025, Nadig and colleagues
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