Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples.
You can learn more about ARCHS4 and its pipeline here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| PGP1 |
GSM4803237 GSM4803238 GSM4803239 GSM4803240
|
|
|
GSM4803245 GSM4803246 GSM4803247 GSM4803248
|
||
|
GSM4803241 GSM4803242 GSM4803243 GSM4803244
|
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
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
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Differential expression signatures are automatically computed using the limma R package.
More options for differential expression are available to compute below.
Signatures:
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.
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.