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Raw gene Expression data is sourced from GEO, and the appropriate db package for mapping probes to gene symbols was sourced from the Bioconductor AnnotationData packages.
You can read more about microarray data here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| Muscle: Vastus lateralis |
GSM4698327 GSM4698328 GSM4698329 GSM4698330 GSM4698331
|
|
|
GSM4698319 GSM4698320 GSM4698321 GSM4698322 GSM4698323 GSM4698324 GSM4698325 GSM4698326
|
Submission Date: Jul 28, 2020
Summary: Endurance-trained athletes have high oxidative capacity, enhanced insulin sensitivity, and high intracellular lipid accumulation in muscle. These characteristics are likely due to altered gene expression levels in muscle.
We used microarrays to detect gene expression profile in endurance-trained athlete skeletal muscle.
GEO Accession ID: GSE155271
PMID: 33291227
Submission Date: Jul 28, 2020
Summary: Endurance-trained athletes have high oxidative capacity, enhanced insulin sensitivity, and high intracellular lipid accumulation in muscle. These characteristics are likely due to altered gene expression levels in muscle.
We used microarrays to detect gene expression profile in endurance-trained athlete skeletal muscle.
GEO Accession ID: GSE155271
PMID: 33291227
Visualize Samples
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Precomputed Differential Gene Expression
Differential expression signatures are automatically computed using the limma R package.
More options for differential expression are available to compute below.
Signatures:
Select conditions:
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.