Minor fixes (citations, capitalization, rearranging text)

config/_default/params.toml

@@ -21,7 +21,7 @@ enableCodeCopy = false
   showDate = false
   showAuthor = false
   showReadingTime = false
-  showTableOfContents = true
+  showTableOfContents = false
   showRelatedContent = false
   showEdit = false
 

content/about/_index.md

@@ -13,7 +13,7 @@ Experimental and emerging tools are developed under the [WayScience GitHub organ
 ## Impact
 
 Cytomining tools have been adopted across academia and industry for large-scale drug discovery and functional genomics.
-`pycytominer` underpins some of the largest publicly available image-based profiling datasets, including the [JUMP Cell Painting dataset](https://jump-cellpainting.broadinstitute.org/) (over 136,000 chemical and genetic perturbations profiled across 12 partner sites) the [LINCS Drug Repurposing](https://github.com/broadinstitute/lincs-cell-painting) Cell Painting dataset, and the [EU-OPENSCREEN](https://www.eu-openscreen.eu/) Bioactive Compound Set profiled across multiple imaging sites.
+`Pycytominer` underpins some of the largest publicly available image-based profiling datasets, including the [JUMP Cell Painting dataset](https://jump-cellpainting.broadinstitute.org/) (over 136,000 chemical and genetic perturbations profiled across 12 partner sites) the [LINCS Drug Repurposing](https://github.com/broadinstitute/lincs-cell-painting) Cell Painting dataset, and the [EU-OPENSCREEN](https://www.eu-openscreen.eu/) Bioactive Compound Set profiled across multiple imaging sites.
 It has also processed many of the 31+ datasets in the [Cell Painting Gallery](https://broadinstitute.github.io/cellpainting-gallery/).
 
 The foundational [Caicedo et al. 2017](https://doi.org/10.1038/nmeth.4397) review has accumulated over 670 citations, and individual tool papers have together been cited more than 150 times since 2024.
@@ -26,7 +26,7 @@ The ecosystem traces its roots to the [Imaging Platform](https://www.broadinstit
 In 2016, members of this community co-founded the [CytoData Society](https://www.cytodata.org/) to unite researchers across academia and industry around image-based profiling.
 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`.
+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`.
 Today, Cytomining tools are used by research groups worldwide for drug discovery, functional genomics, and cell biology.
 
 ## Community

content/experimental/buscar.md

@@ -10,15 +10,16 @@ logoUrl: "https://raw.githubusercontent.com/WayScience/buscar/main/logo/just-ico
 <img class="logo-light" src="https://raw.githubusercontent.com/WayScience/buscar/main/logo/with-text-for-light-bg.png" alt="buscar logo" width="400">
 <img class="logo-dark" src="https://raw.githubusercontent.com/WayScience/buscar/main/logo/with-text-for-dark-bg.png" alt="buscar logo" width="400">
 
-**Problem:** Population-level hit calling averages away biologically meaningful cell-to-cell variation, making heterogeneous responses and rare subpopulations invisible to standard metrics.
 `buscar` scores perturbations directly on single-cell distributions using Earth Mover's Distance, preserving heterogeneity throughout hit calling.
 
+**Problem:** Population-level hit calling averages away biologically meaningful cell-to-cell variation, making heterogeneous responses and rare subpopulations invisible to standard metrics.
+
 **Key capabilities:**
 
 - Define on-target and off-target morphology signatures from reference profiles
 - Score perturbation efficacy via Earth Mover's Distance
 - Assess specificity with off-target scoring to reduce false positives
 - Preserve single-cell heterogeneity throughout hit calling
-- Integrates directly with `pycytominer`, `coSMicQC`, and `CytoTable` workflows
+- Integrates directly with `Pycytominer`, `coSMicQC`, and `CytoTable` workflows
 
 **[View on GitHub →](https://github.com/WayScience/buscar)**

content/experimental/iceberg-bioimage.md

@@ -9,9 +9,10 @@ logoUrl: "https://raw.githubusercontent.com/WayScience/iceberg-bioimage/main/doc
 
 <img src="https://raw.githubusercontent.com/WayScience/iceberg-bioimage/main/docs/src/_static/iceberg-bioimage-logo.png" alt="iceberg-bioimage logo" width="400">
 
-**Problem:** Raw bioimaging archives have no standard catalog — finding, versioning, and joining images to downstream data requires bespoke scripts per lab.
 `iceberg-bioimage` scans any image store into a versioned Apache Iceberg catalog that directly exports Cytomining-compatible Parquet warehouses.
 
+**Problem:** Raw bioimaging archives have no standard catalog — finding, versioning, and joining images to downstream data requires bespoke scripts per lab.
+
 **Key capabilities:**
 
 - Scan image stores into canonical `ScanResult` objects
@@ -20,4 +21,4 @@ logoUrl: "https://raw.githubusercontent.com/WayScience/iceberg-bioimage/main/doc
 - Validate profile tables against microscopy join contracts
 - Supports Zarr, OME-TIFF, and Parquet source formats
 
-**[View documentation →](https://wayscience.github.io/iceberg-bioimage/)**
+**[View documentation →](https://wayscience.github.io/iceberg-bioimage/)** · **[View on GitHub →](https://github.com/WayScience/iceberg-bioimage)**

content/experimental/ome-arrow.md

@@ -9,9 +9,10 @@ logoUrl: "https://raw.githubusercontent.com/WayScience/OME-arrow/main/docs/src/_
 
 <img src="https://raw.githubusercontent.com/WayScience/OME-arrow/main/docs/src/_static/ome-arrow-logo.png" alt="OME-arrow logo" width="400">
 
-**Problem:** Images and feature tables live in separate systems — linking a numeric outlier back to its source cell requires error-prone manual joins across formats.
 `OME-arrow` embeds images as first-class columns in Apache Arrow tables, so features, metadata, and pixel data travel together and can be queried or exported as tensors.
 
+**Problem:** Images and feature tables live in separate systems — linking a numeric outlier back to its source cell requires error-prone manual joins across formats.
+
 **Key capabilities:**
 
 - Store images, metadata, and derived features together in a single table
@@ -20,4 +21,4 @@ logoUrl: "https://raw.githubusercontent.com/WayScience/OME-arrow/main/docs/src/_
 - Tensor-focused output compatible with PyTorch, JAX, and DLPack
 - Visualization integrations for matplotlib, PyVista, and Napari
 
-**[View documentation →](https://wayscience.github.io/ome-arrow/)**
+**[View documentation →](https://wayscience.github.io/ome-arrow/)** · **[View on GitHub →](https://github.com/WayScience/OME-arrow)**

content/experimental/zedprofiler.md

@@ -6,9 +6,10 @@ showDate: false
 showAuthor: false
 ---
 
-**Problem:** Classical profiling tools extract only 2D features, leaving organoid, cleared-tissue, and z-stack experiments without a CPU-efficient extractor.
 `zedprofiler` extracts morphological features directly from 3D volumetric images, including anisotropic voxel spacing correction — no GPU required.
 
+**Problem:** Classical profiling tools extract only 2D features, leaving organoid, cleared-tissue, and z-stack experiments without a CPU-efficient extractor.
+
 **Key capabilities:**
 
 - Extract features from 3D volumetric (z-stack) single-cell images

content/media/_index.md

@@ -29,30 +29,30 @@ showAuthor: false
 
 ### Reviews
 
-- Serrano E, Peters J, Way GP et al. (2026) — Progress and new challenges in image-based
-  profiling — _Molecular Systems Biology_ —
-  [10.1038/s44320-026-00197-7](https://doi.org/10.1038/s44320-026-00197-7)
-- Caicedo JC, Cooper S, Singh S, Carpenter AE et al. (2017) — Data-analysis strategies for
-  image-based cell profiling — _Nature Methods_ 14(9):849–863 —
-  [10.1038/nmeth.4397](https://doi.org/10.1038/nmeth.4397)
+- Serrano, E. et al. Progress and new challenges in image-based profiling.
+  *Mol. Syst. Biol.* **22**, 624–658 (2026).
+  [doi:10.1038/s44320-026-00197-7](https://doi.org/10.1038/s44320-026-00197-7)
+- Caicedo, J.C. et al. Data-analysis strategies for image-based cell profiling.
+  *Nat. Methods* **14**, 849–863 (2017).
+  [doi:10.1038/nmeth.4397](https://doi.org/10.1038/nmeth.4397)
 
 ### Tools
 
-- Serrano E, Chandrasekaran SN, Bunten D, Way GP et al. (2025) — Reproducible image-based
-  profiling with Pycytominer — _Nature Methods_ 22:677–680 —
-  [10.1038/s41592-025-02611-8](https://doi.org/10.1038/s41592-025-02611-8)
-- Kalinin AA, Arevalo J, Serrano E, Way GP, Singh S et al. (2025) — A versatile information
-  retrieval framework for evaluating profile strength and similarity — _Nature Communications_ —
-  [10.1038/s41467-025-60306-2](https://doi.org/10.1038/s41467-025-60306-2)
-- Moshkov N, Bornholdt M, Caicedo JC et al. (2024) — Learning representations for image-based
-  profiling of perturbations — _Nature Communications_ —
-  [10.1038/s41467-024-45999-1](https://doi.org/10.1038/s41467-024-45999-1)
-- Bunten D, Tomkinson J, Serrano E, Way GP et al. (2026) — Scalable data harmonization for
-  single-cell image-based profiling with CytoTable — _Patterns_ 7(5):101514 —
-  [10.1016/j.patter.2026.101514](https://doi.org/10.1016/j.patter.2026.101514)
-- Serrano E, Li WS, Way GP (2026) — Single-cell hit calling in high-content imaging screens
-  with Buscar — _bioRxiv_ preprint —
-  [10.64898/2026.04.15.718737](https://doi.org/10.64898/2026.04.15.718737)
-- Serrano E, Tomkinson J, Bunten D, Way GP et al. (2025) — Stellar quality control for
-  single-cell image-based profiling with coSMicQC — _bioRxiv_ preprint —
-  [10.1101/2025.10.14.682427](https://doi.org/10.1101/2025.10.14.682427)
+- Serrano, E. et al. Reproducible image-based profiling with Pycytominer.
+  *Nat. Methods* **22**, 677–680 (2025).
+  [doi:10.1038/s41592-025-02611-8](https://doi.org/10.1038/s41592-025-02611-8)
+- Kalinin, A.A. et al. A versatile information retrieval framework for evaluating profile strength and similarity.
+  *Nat. Commun.* **16**, 5181 (2025).
+  [doi:10.1038/s41467-025-60306-2](https://doi.org/10.1038/s41467-025-60306-2)
+- Moshkov, N. et al. Learning representations for image-based profiling of perturbations.
+  *Nat. Commun.* **15**, 1594 (2024).
+  [doi:10.1038/s41467-024-45999-1](https://doi.org/10.1038/s41467-024-45999-1)
+- Bunten, D. et al. Scalable data harmonization for single-cell image-based profiling with CytoTable.
+  *Patterns* **7**, 101514 (2026).
+  [doi:10.1016/j.patter.2026.101514](https://doi.org/10.1016/j.patter.2026.101514)
+- Serrano, E., Li, W.S. & Way, G.P. Single-cell hit calling in high-content imaging screens with Buscar.
+  *bioRxiv* (2026).
+  [doi:10.64898/2026.04.15.718737](https://doi.org/10.64898/2026.04.15.718737)
+- Tomkinson, J. et al. Stellar quality control for single-cell image-based profiling with coSMicQC.
+  *bioRxiv* (2025).
+  [doi:10.1101/2025.10.14.682427](https://doi.org/10.1101/2025.10.14.682427)

content/tools/copairs.md

@@ -27,7 +27,7 @@ It implements mean Average Precision (mAP) and related metrics widely used in th
     <a href="https://doi.org/10.1038/s41467-025-60306-2">A versatile information retrieval framework for evaluating profile strength and similarity</a>
   </p>
   <p style="font-size: 0.875rem; margin: 0.25rem 0 0; color: #374151;">
-    Kalinin AA, Arevalo J, Serrano E, Vulliard L, Tsang H, et al.
+    Kalinin, A.A. et al. <em>Nat. Commun.</em> <strong>16</strong>, 5181 (2025).
   </p>
   <p style="font-size: 0.8rem; margin: 0.25rem 0 0; color: #6b7280;">
     doi: <a href="https://doi.org/10.1038/s41467-025-60306-2" style="color: #6b7280;">10.1038/s41467-025-60306-2</a>

content/tools/cosmicqc.md

@@ -17,7 +17,7 @@ It catches common problems such as over-segmented nuclei, poorly segmented cells
 - Flag over-segmented, under-segmented, and poorly focused cells
 - Apply threshold-based or z-score-based QC criteria
 - Generate summary reports of QC outcomes
-- Integrate seamlessly with `CytoTable` and `pycytominer` workflows
+- Integrate seamlessly with `CytoTable` and `Pycytominer` workflows
 
 **[View documentation →](https://cytomining.github.io/coSMicQC/)**
 
@@ -31,7 +31,7 @@ It catches common problems such as over-segmented nuclei, poorly segmented cells
     <a href="https://doi.org/10.1101/2025.10.14.682427">Stellar quality control for single-cell image-based profiling with coSMicQC</a>
   </p>
   <p style="font-size: 0.875rem; margin: 0.25rem 0 0; color: #374151;">
-    Tomkinson J, Bunten D, Way GP
+    Tomkinson, J. et al. <em>bioRxiv</em> (2025).
   </p>
   <p style="font-size: 0.8rem; margin: 0.25rem 0 0; color: #6b7280;">
     doi: <a href="https://doi.org/10.1101/2025.10.14.682427" style="color: #6b7280;">10.1101/2025.10.14.682427</a>

content/tools/cytotable.md

@@ -17,7 +17,7 @@ It scales to large datasets using Apache Parquet and DuckDB under the hood.
 - Convert CellProfiler SQLite, CSV, and other formats into Parquet
 - Harmonize schema differences across analysis tools
 - Scale to datasets with millions of single cells
-- Produce outputs compatible with `pycytominer` and AnnData workflows
+- Produce outputs compatible with `Pycytominer` and AnnData workflows
 
 **[View documentation →](https://cytomining.github.io/CytoTable/)**
 
@@ -31,7 +31,7 @@ It scales to large datasets using Apache Parquet and DuckDB under the hood.
     <a href="https://doi.org/10.1016/j.patter.2026.101514">Scalable data harmonization for single-cell image-based profiling with CytoTable</a>
   </p>
   <p style="font-size: 0.875rem; margin: 0.25rem 0 0; color: #374151;">
-    Bunten D, Tomkinson J, Serrano E, Lippincott MJ, Brewer KI, et al.
+    Bunten, D. et al. <em>Patterns</em> <strong>7</strong>, 101514 (2026).
   </p>
   <p style="font-size: 0.8rem; margin: 0.25rem 0 0; color: #6b7280;">
     doi: <a href="https://doi.org/10.1016/j.patter.2026.101514" style="color: #6b7280;">10.1016/j.patter.2026.101514</a>

content/tools/deepprofiler.md

@@ -15,7 +15,7 @@ It is designed for high-throughput screens where deep learning representations o
 - Train and apply convolutional neural networks for feature extraction
 - Support for EfficientNet, ResNet, and custom architectures
 - Crop and embed single cells from large microscopy images
-- Produce embeddings compatible with `pycytominer` and downstream profiling workflows
+- Produce embeddings compatible with `Pycytominer` and downstream profiling workflows
 
 **[View on GitHub →](https://github.com/cytomining/DeepProfiler)**
 
@@ -29,7 +29,7 @@ It is designed for high-throughput screens where deep learning representations o
     <a href="https://doi.org/10.1038/s41467-024-45999-1">Learning representations for image-based profiling of perturbations</a>
   </p>
   <p style="font-size: 0.875rem; margin: 0.25rem 0 0; color: #374151;">
-    Moshkov N, Bornholdt M, Benoit G, Smith K, et al.
+    Moshkov, N. et al. <em>Nat. Commun.</em> <strong>15</strong>, 1594 (2024).
   </p>
   <p style="font-size: 0.8rem; margin: 0.25rem 0 0; color: #6b7280;">
     doi: <a href="https://doi.org/10.1038/s41467-024-45999-1" style="color: #6b7280;">10.1038/s41467-024-45999-1</a>

content/tools/pycytominer.md

@@ -1,15 +1,15 @@
 ---
-title: "pycytominer"
+title: "Pycytominer"
 description: "Core processing pipeline — aggregates, normalizes, and feature-selects morphological profiles for downstream analysis."
 showDate: false
 showAuthor: false
 logoUrl: "https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/just-icon.png"
 ---
 
-<img class="logo-light" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/with-text-for-light-bg.png" alt="pycytominer logo" width="400">
-<img class="logo-dark" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/with-text-for-dark-bg.png" alt="pycytominer logo" width="400">
+<img class="logo-light" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/with-text-for-light-bg.png" alt="Pycytominer logo" width="400">
+<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.
+`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.
 
 **Key capabilities:**
@@ -32,7 +32,7 @@ It provides a clean, composable API for processing single-cell morphological pro
     <a href="https://doi.org/10.1038/s41592-025-02611-8">Reproducible image-based profiling with Pycytominer</a>
   </p>
   <p style="font-size: 0.875rem; margin: 0.25rem 0 0; color: #374151;">
-    Serrano E, Chandrasekaran SN, Bunten D, Brewer KI, Tomkinson J, et al.
+    Serrano, E. et al. <em>Nat. Methods</em> <strong>22</strong>, 677–680 (2025).
   </p>
   <p style="font-size: 0.8rem; margin: 0.25rem 0 0; color: #6b7280;">
     doi: <a href="https://doi.org/10.1038/s41592-025-02611-8" style="color: #6b7280;">10.1038/s41592-025-02611-8</a>

layouts/experimental/list.html

@@ -66,7 +66,7 @@ <h2 class="hero-fade-5" style="font-size: 1.15rem; font-weight: 700; margin-top:
         </div>
         <span style="color: #d1d5db; padding-top: 0.4rem;">→</span>
         <div style="display: flex; flex-direction: column; align-items: center; gap: 0.3rem;">
-          <a href="/tools/pycytominer/" class="pipeline-step v2-pipeline-step-blue" style="background: #dbeafe; border: 1px solid #bfdbfe; border-radius: 8px; padding: 0.4rem 0.75rem; font-size: 0.75rem; font-weight: 600; color: #1e40af; text-decoration: none; white-space: nowrap;"><img class="pill-icon" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/just-icon.png" alt="">pycytominer</a>
+          <a href="/tools/pycytominer/" class="pipeline-step v2-pipeline-step-blue" style="background: #dbeafe; border: 1px solid #bfdbfe; border-radius: 8px; padding: 0.4rem 0.75rem; font-size: 0.75rem; font-weight: 600; color: #1e40af; text-decoration: none; white-space: nowrap;"><img class="pill-icon" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/just-icon.png" alt="">Pycytominer</a>
           <span style="font-size: 0.6rem; color: #9ca3af; text-transform: uppercase; letter-spacing: 0.05em;">process</span>
         </div>
         <span style="color: #d1d5db; padding-top: 0.4rem;">→</span>
@@ -115,7 +115,7 @@ <h2 class="hero-fade-5" style="font-size: 1.15rem; font-weight: 700; margin-top:
         </div>
         <span style="color: #d1d5db; padding-top: 0.4rem;">→</span>
         <div style="display: flex; flex-direction: column; align-items: center; gap: 0.3rem;">
-          <a href="/tools/pycytominer/" class="pipeline-step v2-pipeline-step-blue" style="background: #dbeafe; border: 1px solid #bfdbfe; border-radius: 8px; padding: 0.4rem 0.75rem; font-size: 0.75rem; font-weight: 600; color: #1e40af; text-decoration: none; white-space: nowrap;"><img class="pill-icon" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/just-icon.png" alt="">pycytominer</a>
+          <a href="/tools/pycytominer/" class="pipeline-step v2-pipeline-step-blue" style="background: #dbeafe; border: 1px solid #bfdbfe; border-radius: 8px; padding: 0.4rem 0.75rem; font-size: 0.75rem; font-weight: 600; color: #1e40af; text-decoration: none; white-space: nowrap;"><img class="pill-icon" src="https://raw.githubusercontent.com/cytomining/pycytominer/main/logo/just-icon.png" alt="">Pycytominer</a>
           <span style="font-size: 0.6rem; color: #9ca3af; text-transform: uppercase; letter-spacing: 0.05em;">process</span>
         </div>
         <span style="color: #d1d5db; padding-top: 0.4rem;">→</span>