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<t>scSGS</t> reproduces similar STAT1 functional inferences from three independent <t>PBMC</t> datasets. (a) Extractions of CD4+ T cells from three independent Human PBMC datasets (PBMC 5K, PBMC 10K, PBMC 20K) for scSGS analysis. (b) Significantly enriched GO biological processes terms using the common SGS-responsive genes from the three PBMC datasets. (c) STRING interaction network of common STAT1 -specific SGS-responsive genes from the 3 datasets. Edge thickness indicates the strength of data support and node size indicates node degree. (d) Overlap of significant SGS-responsive genes from the three PBMC datasets. (e) Overlap of significant GO terms inferred from the top 200 SGS-responsive genes from the three PBMC datasets, respectively.
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scSGS reproduces similar STAT1 functional inferences from three independent PBMC datasets. (a) Extractions of CD4+ T cells from three independent Human PBMC datasets (PBMC 5K, PBMC 10K, PBMC 20K) for scSGS analysis. (b) Significantly enriched GO biological processes terms using the common SGS-responsive genes from the three PBMC datasets. (c) STRING interaction network of common STAT1 -specific SGS-responsive genes from the 3 datasets. Edge thickness indicates the strength of data support and node size indicates node degree. (d) Overlap of significant SGS-responsive genes from the three PBMC datasets. (e) Overlap of significant GO terms inferred from the top 200 SGS-responsive genes from the three PBMC datasets, respectively.

Journal: bioRxiv

Article Title: Gene Function Revealed at the Moment of Stochastic Gene Silencing

doi: 10.1101/2024.07.16.603770

Figure Lengend Snippet: scSGS reproduces similar STAT1 functional inferences from three independent PBMC datasets. (a) Extractions of CD4+ T cells from three independent Human PBMC datasets (PBMC 5K, PBMC 10K, PBMC 20K) for scSGS analysis. (b) Significantly enriched GO biological processes terms using the common SGS-responsive genes from the three PBMC datasets. (c) STRING interaction network of common STAT1 -specific SGS-responsive genes from the 3 datasets. Edge thickness indicates the strength of data support and node size indicates node degree. (d) Overlap of significant SGS-responsive genes from the three PBMC datasets. (e) Overlap of significant GO terms inferred from the top 200 SGS-responsive genes from the three PBMC datasets, respectively.

Article Snippet: To test the scSGS analysis framework, we selected three human PBMC datasets of varied sizes from the 10x Genomics datasets repository ( ).

Techniques: Functional Assay