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gwaybio merged 1 commit into from
May 22, 2026
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Update language to roadmap and a few other tweaks #11

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1 change: 1 addition & 0 deletions content/about/_index.md
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Expand Up @@ -27,6 +27,7 @@ In 2016, members of this community co-founded the [CytoData Society](https://www
The following year, the team contributed the landmark [Caicedo et al. 2017](https://doi.org/10.1038/nmeth.4397) review in _Nature Methods_ that established the field's foundational analysis standards.

Since 2021, the [Way Lab](https://www.waysciencelab.com/) has driven a major expansion of the ecosystem — migrating from R to Python with `Pycytominer` and building a modern infrastructure stack including `CytoTable` and `coSMicQC`.
In 2026, members of the CytoData community published an updated review, [Serrano et al.](https://doi.org/10.1038/s44320-026-00197-7) in _Molecular Systems Biology_, surveying progress and new challenges in image-based profiling.
Today, Cytomining tools are used by research groups worldwide for drug discovery, functional genomics, and cell biology.

## Community
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4 changes: 2 additions & 2 deletions content/experimental/_index.md
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---
title: "Experimental"
description: "Next-generation tools under active development in the WayScience organization."
description: "Tools under active development in the WayScience organization, forming the foundation of the next Cytomining ecosystem."
---

The following tools are under active development in the [WayScience](https://github.com/WayScience) organization and represent the next generation of the Cytomining ecosystem.
The following tools are under active development in the [WayScience](https://github.com/WayScience) organization and form the backbone of the Cytomining roadmap.
2 changes: 1 addition & 1 deletion content/experimental/buscar.md
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---
title: "buscar"
description: "Hit calling — identifies biologically active perturbations from single-cell morphological profiles using distribution-level scoring."
description: "Hit calling — identifies biologically active perturbations from single-cell morphology profiles using distribution-level scoring."
problem: "Population-level hit calling averages away cell-to-cell variation, hiding heterogeneous responses and rare subpopulations."
showDate: false
showAuthor: false
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2 changes: 1 addition & 1 deletion content/tools/copairs.md
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Expand Up @@ -5,7 +5,7 @@ showDate: false
showAuthor: false
---

`copairs` is a Python package for evaluating the quality of morphological profiles by measuring how well a perturbation's profile can be retrieved relative to controls.
`copairs` is a Python package for evaluating the quality of morphology profiles by measuring how well a perturbation's profile can be retrieved relative to controls.
It implements mean Average Precision (mAP) and related metrics widely used in the image-based profiling community.

**Key capabilities:**
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4 changes: 2 additions & 2 deletions content/tools/cytodataframe.md
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---
title: "CytoDataFrame"
description: "Interactive exploration — view and inspect morphological profiles alongside their source cell images directly in Jupyter notebooks."
description: "Interactive exploration — view and inspect morphology profiles alongside their source cell images directly in Jupyter notebooks."
showDate: false
showAuthor: false
logoUrl: "https://raw.githubusercontent.com/cytomining/CytoDataFrame/main/logo/just-icon.png"
Expand All @@ -9,7 +9,7 @@ logoUrl: "https://raw.githubusercontent.com/cytomining/CytoDataFrame/main/logo/j
<img class="logo-light" src="https://raw.githubusercontent.com/cytomining/CytoDataFrame/main/logo/with-text-for-light-bg.png" alt="CytoDataFrame logo" width="400">
<img class="logo-dark" src="https://raw.githubusercontent.com/cytomining/CytoDataFrame/main/logo/with-text-for-dark-bg.png" alt="CytoDataFrame logo" width="400">

`CytoDataFrame` extends the familiar pandas DataFrame interface to let researchers view and analyze single-cell morphological profiles alongside their corresponding microscopy images and segmentation masks — all within a Jupyter notebook.
`CytoDataFrame` extends the familiar pandas DataFrame interface to let researchers view and analyze single-cell morphology profiles alongside their corresponding microscopy images and segmentation masks — all within a Jupyter notebook.

**Key capabilities:**

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Expand Up @@ -10,7 +10,7 @@ logoUrl: "https://raw.githubusercontent.com/cytomining/CytoTable/main/logo/just-
<img class="logo-dark" src="https://raw.githubusercontent.com/cytomining/CytoTable/main/logo/with-text-for-dark-bg.png" alt="CytoTable logo" width="400">

`CytoTable` harmonizes output from different high-content image analysis tools — including CellProfiler, `DeepProfiler`, and IN Carta — into a single, analysis-ready format.
It scales to large datasets using Apache Parquet and DuckDB under the hood.
It scales to large datasets using Apache Parquet, DuckDB, and Parsl under the hood.

**Key capabilities:**

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4 changes: 2 additions & 2 deletions content/tools/pycytominer.md
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---
title: "Pycytominer"
description: "Core processing pipeline — aggregates, normalizes, and feature-selects morphological profiles for downstream analysis."
description: "Core processing pipeline — aggregates, normalizes, and feature-selects morphology profiles for downstream analysis."
showDate: false
showAuthor: false
logoUrl: "https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/just-icon.png"
Expand All @@ -10,7 +10,7 @@ logoUrl: "https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/jus
<img class="logo-dark" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/with-text-for-dark-bg.png" alt="Pycytominer logo" width="400">

`Pycytominer` is the core Python package in the Cytomining ecosystem.
It provides a clean, composable API for processing single-cell morphological profiles produced by tools like CellProfiler.
It provides a clean, composable API for processing single-cell morphology profiles produced by tools like CellProfiler.

**Key capabilities:**

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14 changes: 7 additions & 7 deletions layouts/experimental/list.html
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Expand Up @@ -5,17 +5,17 @@
<h1 class="hero-fade-1 text-4xl font-extrabold mb-2">Experimental Tools</h1>
<p class="hero-fade-2" style="color: #6b7280; font-size: 1.05rem; margin-bottom: 2.5rem; line-height: 1.65;">
Next-generation tools under active development in the <a href="https://github.com/WayScience" style="color: inherit; text-decoration: underline; text-decoration-color: #d1d5db;">WayScience</a> organization,
designed to become the foundation of Cytomining v2.
forming the foundation of the Cytomining roadmap.
</p>

{{/* V2 pitch */}}
{{/* Roadmap pitch */}}
<div class="hero-fade-3 v2-callout" style="border-left: 3px solid #2563eb; padding: 1rem 1.25rem; background: #eff6ff; border-radius: 0 8px 8px 0; margin-bottom: 2rem;">
<p style="font-weight: 700; font-size: 1rem; margin: 0 0 0.5rem; color: #1e40af;">What does Cytomining v2 solve?</p>
<p style="font-weight: 700; font-size: 1rem; margin: 0 0 0.5rem; color: #1e40af;">What gaps does our roadmap address?</p>
<p style="margin: 0; font-size: 0.92rem; color: #1e3a5f; line-height: 1.65;">
The current Cytomining stack was designed around 2D single-cell data from CellProfiler.
As the field moves toward <strong>3D organoid imaging</strong>, <strong>larger-scale archives</strong>, and <strong>deep learning feature extraction</strong>,
several gaps have emerged: no standardized image catalog, images and features stored separately, no 3D support, and hit calling that collapses single-cell heterogeneity.
The tools below are purpose-built to close each of these gaps — together forming a fully traceable, format-agnostic, 3D-capable profiling pipeline.
As the field moves toward <strong>3D organoid imaging</strong>, <strong>single-cell profiling</strong>, <strong>larger-scale screens</strong>, and <strong>deep learning feature extraction</strong>,
several gaps have emerged: no standardized image catalog, images and features stored separately, limited 3D support, and hit calling that collapses single-cell heterogeneity.
We are building the tools below to close each of these gaps and raise the computational pipeline to support the demands of data collection and goals of the moment.
</p>
</div>

Expand All @@ -28,7 +28,7 @@ <h2 style="font-size: 1.15rem; font-weight: 700; margin-bottom: 0.25rem;">Tools<
</div>

{{/* V2 pipelines */}}
<h2 class="hero-fade-5" style="font-size: 1.15rem; font-weight: 700; margin-top: 3rem; margin-bottom: 1.25rem;">Cytomining v2 pipelines</h2>
<h2 class="hero-fade-5" style="font-size: 1.15rem; font-weight: 700; margin-top: 3rem; margin-bottom: 1.25rem;">Roadmap pipelines</h2>

{{/* Pipeline 1: standard (2D) */}}
<div class="hero-fade-6" style="margin-bottom: 1.5rem;">
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