Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
THP1
GSM5410589 GSM5410590 GSM5410591
GSM5410592 GSM5410593 GSM5410594
Description

Submission Date: Jun 29, 2021

Summary: Betel-nut consumption is the fourth most common addictive habit globally and there is good evidence linking the habit to obesity, type 2 diabetes (T2D) and the metabolic syndrome. The aim of our pilot study was to identify gene expression relevant to obesity, T2D and the metabolic syndrome using a genome-wide transcriptomic approach in a human monocyte cell line incubated with arecoline and its nitrosated products.

GEO Accession ID: GSE179143

PMID: 34391409

Description

Submission Date: Jun 29, 2021

Summary: Betel-nut consumption is the fourth most common addictive habit globally and there is good evidence linking the habit to obesity, type 2 diabetes (T2D) and the metabolic syndrome. The aim of our pilot study was to identify gene expression relevant to obesity, T2D and the metabolic syndrome using a genome-wide transcriptomic approach in a human monocyte cell line incubated with arecoline and its nitrosated products.

GEO Accession ID: GSE179143

PMID: 34391409

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.

Signatures:

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Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

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.