Microarray Data Explorer
Info 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.
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GROUP CONDITION SAMPLES
Female subjects fat
GSM4592205 GSM4592206 GSM4592207 GSM4592208 GSM4592209 GSM4592210 GSM4592211 GSM4592212 GSM4592213 GSM4592215
GSM4592203 GSM4592204 GSM4592214 GSM4592216 GSM4592217 GSM4592218 GSM4592219 GSM4592220 GSM4592221 GSM4592222
Female subjects skin
GSM4592185 GSM4592186 GSM4592187 GSM4592188 GSM4592189 GSM4592190 GSM4592191 GSM4592192 GSM4592193 GSM4592195
GSM4592183 GSM4592184 GSM4592194 GSM4592196 GSM4592197 GSM4592198 GSM4592199 GSM4592200 GSM4592201 GSM4592202
Description

Submission Date: Jun 04, 2020

Summary: We studied differences in epithelial thickness by histology and gene expression by Affymetrix gene arrays and PCR in the skin/fat of 10 obese (BMI 35-50) and 10 normal weight (BMI 18.5-26.9) postmenopausal women paired by age and race

GEO Accession ID: GSE151839

PMID: 32826922

Description

Submission Date: Jun 04, 2020

Summary: We studied differences in epithelial thickness by histology and gene expression by Affymetrix gene arrays and PCR in the skin/fat of 10 obese (BMI 35-50) and 10 normal weight (BMI 18.5-26.9) postmenopausal women paired by age and race

GEO Accession ID: GSE151839

PMID: 32826922

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

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Control Condition

Perturbation Condition

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Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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