GSE136053: Comparative Analysis of the Transcriptome of Latent Autoimmune Diabetes (LADA) Patients from Eastern China

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
peripheral blood mononuclear cells
GSM4040151 GSM4040152 GSM4040153 GSM4040154 GSM4040155
GSM4040147 GSM4040148 GSM4040149 GSM4040150
Description

Submission Date: Aug 20, 2019

Summary: We did the transcriptome analysis of peripheral blood mononuclear cells of LADA patients and healthy controls

GEO Accession ID: GSE136053

PMID: 31950067

Description

Submission Date: Aug 20, 2019

Summary: We did the transcriptome analysis of peripheral blood mononuclear cells of LADA patients and healthy controls

GEO Accession ID: GSE136053

PMID: 31950067

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

Precomputed Differential Gene Expression

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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.