Allen Brain Atlas: Non-Human Primate Macrodissection Microarray
Study Design & Technical Specifications
Sample Size / Cohort
See repository manifest
Subject / Study Model
CNS Experimental / Clinical Model
Analytical Modalities
Primary Abstract & Repository Description
Source: Allen Brain Map / BICCNEditorial Critique, Methodological Value & Reusability Guide
1. Scientific Context & Translational Bottlenecks
This resource from Allen Brain Map / BICCN ("Allen Brain Atlas: Non-Human Primate Macrodissection Microarray") addresses critical scientific questions within Central Nervous System (CNS) Neuroscience. Research in this domain currently faces major translational bottlenecks, including bridging cellular-resolution circuit physiology with macroscale neuroimaging and reproducible cross-cohort computational modeling. By focusing on key thematic elements (Allen Brain Map, Open Science, NHP, NIH Blueprint Non-Human Primate (NHP) Atlas, scRNA-seq), this work contributes empirical evidence toward understanding how molecular, cellular, or systems-level alterations relate to core disease pathways such as multimodal neural oscillations, transcriptomic cell-type taxonomies, structural/functional connectomics, and standardized BIDS/NWB metadata. Integrating these findings into the broader open science ecosystem helps reduce fragmentation across independent laboratories and accelerates hypothesis verification.
日本語解説を表示 (Japanese)
Allen Brain Map / BICCNより公開された本リソース（「Allen Brain Atlas: Non-Human Primate Macrodissection Microarray」）は、中枢神経系（CNS）・基礎および臨床神経科学領域における重要な学術的・臨床的課題に焦点を当てています。現在の中枢神経系（CNS）・基礎および臨床神経科学研究では、単一細胞・神経回路レベルの生理学的知見とマクロ脳画像・臨床表現型の架橋、およびコホート間での高い再現性を持つ計算論的モデルの構築といったトランスレーショナルリサーチ上のボトルネックが存在します。本リソースは関連キーワード（Allen Brain Map, Open Science, NHP, NIH Blueprint Non-Human Primate (NHP) Atlas, scRNA-seq）を軸として、マルチモーダル神経振動・電気生理指標、トランスクリプトーム細胞種分類、構造・機能コネクトーム、およびBIDS/NWB標準化メタデータなどの主要経路や病態メカニズムの理解に資する知見・データを提供しており、研究室や機関ごとに分散しがちな知見をオープンサイエンス基盤上で統合・検証する上で重要な位置づけを持ちます。
2. Methodology & Data Architecture Value
Structured as an open scientific dataset, this repository leverages Transcriptomics across CNS Experimental / Clinical Model (cohort/experimental samples documented in source protocol). Its primary architectural value lies in providing standardized, machine-readable primary records via Allen Brain Map / BICCN under Allen Institute Terms of Use (Open Access / CC-BY). Unlike isolated summary tables in static publications, access to underlying Transcriptomics distributions enables independent inspection of signal-to-noise ratios, preprocessing parameter sensitivity, and cross-modality feature extraction.
日本語解説を表示 (Japanese)
本リソースは公開科学データセットとして、神経科学実験・臨床モデル（サンプル規模: 原著プロトコルに記載されたコホート・実験サンプル）における「Transcriptomics」データを収録しています。Allen Brain Map / BICCNを通じてライセンス（Allen Institute Terms of Use (Open Access / CC-BY)）のもとで構造化データが提供されている点が最大の強みです。論文内の静的な要約統計量のみならず、生データや処理済み特徴量の分布に直接アクセスできるため、信号雑音比（S/N比）の検証、前処理パイプラインの感度分析、およびマルチモーダル特徴量抽出において高い学術的価値を発揮します。
3. Secondary Analysis & Reusability Scenarios
Researchers and computational neuroscientists can reuse this resource across multiple secondary workflows: (1) Cross-Cohort Harmonization & Meta-Analysis — combining these Transcriptomics records with complementary repositories (such as OpenNeuro, DANDI, Agora, or Europe PMC) to increase statistical power; (2) Computational Benchmarking & Machine Learning — utilizing the documented features (Allen Brain Map, Open Science, NHP, NIH Blueprint Non-Human Primate (NHP) Atlas, scRNA-seq) as training, validation, or out-of-distribution test sets for predictive biomarker discovery in Central Nervous System (CNS) Neuroscience; and (3) Experimental Protocol Alignment — referencing the study design (CNS Experimental / Clinical Model, cohort/experimental samples documented in source protocol) to calibrate sample size calculations (power analysis) and endpoint selection for upcoming preclinical or clinical studies.
日本語解説を表示 (Japanese)
本リソースは以下のような二次解析・再利用シナリオに幅広く活用可能です。(1) コホート横断的ハーモナイゼーションとメタ解析：本リソースの「Transcriptomics」データを他の公開リポジトリ（OpenNeuro、DANDI、Agora、ADNI等）の同種データと統合し、統計的検出力（Statistical Power）を高めた統合解析を実施する。(2) 機械学習・バイオマーカー予測モデルの検証：抽出された特徴量や関連因子（Allen Brain Map, Open Science, NHP, NIH Blueprint Non-Human Primate (NHP) Atlas, scRNA-seq）を、中枢神経系（CNS）・基礎および臨床神経科学の進行予測・サブタイプ分類モデルの外部検証（External Validation）や転移学習データとして利用する。(3) 後続研究のプロトコル設計���対象モデル（神経科学実験・臨床モデル）およびサンプル規模（原著プロトコルに記載されたコホート・実験サンプル）の設計仕様を参照し、新規の非臨床試験や臨床研究におけるサンプルサイズ設計（検出力計算）や主要評価項目の妥当性検討に役立てる。
4. Methodological Limitations & Caveats
While curated from an authoritative scientific source, secondary analysts should account for potential cohort selection biases and site-specific batch effects. Specifically, when interpreting Transcriptomics results in Central Nervous System (CNS) Neuroscience, researchers must control for confounding variables (such as age, sex, post-mortem interval, scanner/instrument drift, or medication state) and verify compliance with open-access attribution requirements (Allen Institute Terms of Use (Open Access / CC-BY)).
日本語解説を表示 (Japanese)
信頼性の高い学術情報源から収録されていますが、二次解析や他研究との比較を行う際には、コホート選択バイアスや測定施設間差（バッチエフェクト）に留意する必要があります。特に中枢神経系（CNS）・基礎および臨床神経科学における「Transcriptomics」データの解釈では、年齢・性別・罹病期間・測定機器のキャリブレーション差・併用薬などの交絡因子を適切に補正することが推奨されます。また、データや図表の二次利用時は提供元（Allen Brain Map / BICCN）の利用条件（Allen Institute Terms of Use (Open Access / CC-BY)）および適切な引用表記を遵守してください。
Data Access, Reproducibility & Analysis Starter Guide
Single-Cell & Multi-Omics Matrix Standard (AnnData .h5ad / Loom)
Quick Data Access & Structured Export
Open the primary repository directly or download structured metadata (JSON / CSV) in one click.
This molecular neuroscience resource provides high-dimensional transcriptomic, epigenomic, or proteomic expression matrices accompanied by cell-level annotations (obs), gene/protein metadata (var), and spatial or dimensionality-reduced coordinates (obsm).
Programmatic Data Retrieval (Python Starter Code & Recommended SDKs)
Python Quickstart: Single-Cell / Multi-Omics Analysis with Scanpy
# 1. Install scverse / Scanpy analysis stack
# pip install scanpy anndata leidenalg matplotlib
import scanpy as sc
import pandas as pd
# Load AnnData (.h5ad) or expression matrix from repository download
# Source: Allen Brain Map / BICCN (https://portal.brain-map.org/)
adata = sc.read_h5ad("./data/cns_omics_dataset.h5ad")
# Compute mitochondrial QC metrics & filter low-quality nuclei
adata.var["mt"] = adata.var_names.str.startswith("MT-")
sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], inplace=True)
adata = adata[(adata.obs.n_genes_by_counts > 300) & (adata.obs.pct_counts_mt < 5.0), :].copy()
# Normalize, log-transform, and compute UMAP manifold
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="donor_id")
sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)Standardized 4-Step Workflow & Governance Checklist
Standardized Acquisition & Preprocessing Workflow
1. Load Count Matrix & Cell Metadata (AnnData / Seurat)
Import raw UMI counts or normalized expression tables alongside donor clinical metadata (Braak stage, APOE genotype, PMI).
2. Quality Control & Doublet / Ambient RNA Filtering
Filter low-quality nuclei/cells based on mitochondrial read fraction, minimum detected genes, and Scrublet/CellBender scores.
3. Batch Integration & Cell-Type Taxonomy Mapping
Correct for donor and sequencing batch effects using Harmony or scVI, then map clusters to Allen Brain Map reference taxonomies.
4. Pseudobulk Differential Expression & Pathway Enrichment
Aggregate counts at the donor-celltype level (Pseudobulk DESeq2/edgeR) to avoid pseudoreplication inflation in disease vs. control comparisons.
Reproducibility & Governance Checklist
- Perform donor-level pseudobulk aggregation rather than treating thousands of cells from one donor as independent samples.
- Include post-mortem interval (PMI), RIN (RNA Integrity Number), age, and sex as covariates in linear mixed models.
- Comply with repository Data Use Agreements (e.g., Synapse/Agora controlled-access terms) if linking human genotypes.
Tags & Modalities
Bibliometrics, Peer-Review Status & Citation Export
Cross-referenced via Semantic Scholar, Europe PMC & bioRxiv
1. Citation Metrics & Context Breakdown
Citation Context Breakdown
2. Peer-Review Validation & Revision History
Verified Publication / Open Dataset Record
Metadata and license integrity verified against primary repository records.
3. Standardized Academic Citation & BibTeX Export
Allen Institute for Brain Science & BICCN Consortium (2026). Allen Brain Atlas: Non-Human Primate Macrodissection Microarray. Allen Brain Map / BICCN. https://portal.brain-map.org/
@misc{science2026alle,
title = {Allen Brain Atlas: Non-Human Primate Macrodissection Microarray},
author = {Allen Institute for Brain Science and BICCN Consortium},
year = {2026},
journal = {Allen Brain Map / BICCN},
howpublished = {\url{https://portal.brain-map.org/}},
doi = {},
note = {CNS Open Data: general_cns}
}Related Broad CNS / Neuroimaging Research & Datasets
Explore All DatasetsAllen Brain Atlas: Human Brain ISH Autism Study
High-resolution ISH data detailing gene expression with a unique panel of lamina- and cell type- specific markers to phenotype cortical microstructure during the early years after autism onset.... Freely accessible open data product maintained by the Allen Institute for Brain Science.
BICCN Whole Human Brain Single-Cell Transcriptomic Cell Type Atlas
Cell atlas spanning >3 million single nuclei across 100+ human brain regions, mapping distinct vulnerable neuronal and glial sublineages.
Dandiset 000108: Single-Cell Patch-seq Electrophysiology and Morphometry of Human Neocortical Neurons
Over 1,200 human surgical neocortical pyramidal neurons and interneurons characterized simultaneously with patch-clamp electrophysiology, fluorescent morphological reconstruction, and single-cell RNA sequencing in NWB 2.0 format.
Allen Brain Atlas: Visual Coding Optical Physiology
Time series of 2P in vivo neuronal responses to visual stimuli across Cre lines, higher visual areas and cortical layers... Freely accessible open data product maintained by the Allen Institute for Brain Science.
Dandiset 002027: Behavioral Fever and Compound 3 20-30 C
BRAIN Initiative neurophysiology dataset deposited to DANDI Archive.
Dandiset 002030: One-photon calcium imaging of dorsal CA1 with synchronized behaviour in mice freely exploring a circular open field
BRAIN Initiative neurophysiology dataset deposited to DANDI Archive.
Related Data & Resources
Academic Research & Medical Information Disclaimer
This page provides structured scientific metadata, methodological critiques, and open-data reusability guidelines intended strictly for neuroscience researchers and data scientists. It does not constitute medical advice, clinical diagnosis, or treatment recommendations.
Was this resource helpful?
Help improve data curation by sharing your feedback.