Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples.
You can learn more about ARCHS4 and its pipeline here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
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| C57BL/6J mice |
GSM4502748 GSM4502749 GSM4502750
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GSM4502745 GSM4502746 GSM4502747
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GSM4502736 GSM4502737 GSM4502738
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GSM4502742 GSM4502743 GSM4502744
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GSM4502751 GSM4502752 GSM4502753
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GSM4502739 GSM4502740 GSM4502741
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Submission Date: Apr 27, 2020
Summary: To investigate the global gene expression dynamics associated with short-term fasting, we used mRNA-seq to profile the transcriptomes of nine organs obtained from mice subjected to six different STF duration (0, 2, 8, 12, 18 and 22 hours of fasting; n=3 per time point; Fig. 1a). The nine organs profiled were: olfactory bulb (OB), brain (BRN, which includes the telencephalon and diencephalon), cerebellum (CBL), brainstem (BST, which consists of the mesencephalon, pons, and myelencephalon), stomach (STM), liver (LIV), interscapular brown adipose tissue (iBAT), perigonadal white adipose tissue (pgWAT), and posterior-subcutaneous white adipose tissue (psWAT).
GEO Accession ID: GSE149468
PMID: 32526449
Submission Date: Apr 27, 2020
Summary: To investigate the global gene expression dynamics associated with short-term fasting, we used mRNA-seq to profile the transcriptomes of nine organs obtained from mice subjected to six different STF duration (0, 2, 8, 12, 18 and 22 hours of fasting; n=3 per time point; Fig. 1a). The nine organs profiled were: olfactory bulb (OB), brain (BRN, which includes the telencephalon and diencephalon), cerebellum (CBL), brainstem (BST, which consists of the mesencephalon, pons, and myelencephalon), stomach (STM), liver (LIV), interscapular brown adipose tissue (iBAT), perigonadal white adipose tissue (pgWAT), and posterior-subcutaneous white adipose tissue (psWAT).
GEO Accession ID: GSE149468
PMID: 32526449
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Differential expression signatures are automatically computed using the limma R package.
More options for differential expression are available to compute below.
Signatures:
Control Condition
Perturbation Condition
Only conditions with at least 1 replicate are available to select
Differential expression signatures can be computed using DESeq2 or characteristic direction.
This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.