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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Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
PGP1
GSM4803245 GSM4803246 GSM4803247 GSM4803248
GSM4803237 GSM4803238 GSM4803239 GSM4803240
GSM4803241 GSM4803242 GSM4803243 GSM4803244
Description

Submission Date: Sep 25, 2020

Summary: RNA-sequencing data from human iPSC PGP1 cells (n=4) differentiated into mesoderm (day-2) (n=4) or cardiomyocytes (days 25-30) (n=4) through modulation of Wnt/β-catenin signaling as previously described (Cohn et al., 2019; Hinson et al., 2017; Hinson et al., 2015; Lian et al., 2012).

GEO Accession ID: GSE158578

PMID: No Pubmed ID

Description

Submission Date: Sep 25, 2020

Summary: RNA-sequencing data from human iPSC PGP1 cells (n=4) differentiated into mesoderm (day-2) (n=4) or cardiomyocytes (days 25-30) (n=4) through modulation of Wnt/β-catenin signaling as previously described (Cohn et al., 2019; Hinson et al., 2017; Hinson et al., 2015; Lian et al., 2012).

GEO Accession ID: GSE158578

PMID: No Pubmed ID

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.
Select differential expression analysis method:
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.