heatmaps for gene expression Search Results


gene expression heatmap  (RStudio)
90
RStudio gene expression heatmap
Gene Expression Heatmap, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gene expression heatmap/product/RStudio
Average 90 stars, based on 1 article reviews
gene expression heatmap - by Bioz Stars, 2026-06
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global gene expression heatmap  (GraphPad Software Inc)
90
GraphPad Software Inc global gene expression heatmap
Global Gene Expression Heatmap, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
global gene expression heatmap - by Bioz Stars, 2026-06
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heatmap presentation of hypothalamic gene expression  (GraphPad Software Inc)
90
GraphPad Software Inc heatmap presentation of hypothalamic gene expression
Heatmap Presentation Of Hypothalamic Gene Expression, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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heatmap presentation of hypothalamic gene expression - by Bioz Stars, 2026-06
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gene expression heatmap  (Gallus BioPharmaceuticals)
90
Gallus BioPharmaceuticals gene expression heatmap
Gene Expression Heatmap, supplied by Gallus BioPharmaceuticals, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gene expression heatmap/product/Gallus BioPharmaceuticals
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gene expression heatmap - by Bioz Stars, 2026-06
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gene expression heatmap analysis  (Human Protein Atlas)
86
Human Protein Atlas gene expression heatmap analysis
Characterization of cHep. A. Direct differentiation of cryopreserved pre-cHep using a protocol equivalent to 3D PHH spheroid formation. B. Representative images of 3D PHH and cHep. C. Principal component analysis (PCA) plot including cHep generated from day 30 (D30) J03, J06, and J13 L4 pre-cHep, 3D PHH, and published hepatocyte-like cells (HLC). D. <t>Heatmap</t> showing expression of 264 liver-enriched genes. E–H. Distance-based similarity scores based on gene expression associated with xenobiotic metabolism (E), amino acid biosynthesis (F), bile acid biosynthesis (G), and the urea cycle (H). I. Immunofluorescence staining for albumin, HNF4α, CYP2E1, and ASGR1. J. Cholyl-lysyl-fluorescein (CLF) staining.
Gene Expression Heatmap Analysis, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gene expression heatmap analysis/product/Human Protein Atlas
Average 86 stars, based on 1 article reviews
gene expression heatmap analysis - by Bioz Stars, 2026-06
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characteristic gene expression heatmap  (Cell Signaling Technology Inc)
86
Cell Signaling Technology Inc characteristic gene expression heatmap
Multi-level integrated analysis of bladder cancer single-cell transcriptome. (A) Cell type identification based on NMF algorithm. (B) Differential gene expression <t>heatmap;</t> (C-D) Cell trajectory inference analysis; (E) Cell-cell interaction network; (F-G) GAR and HGF signaling pathway network activity analysis
Characteristic Gene Expression Heatmap, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/characteristic gene expression heatmap/product/Cell Signaling Technology Inc
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characteristic gene expression heatmap - by Bioz Stars, 2026-06
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gene expression heatmaps  (Plotly Technologies Inc)
86
Plotly Technologies Inc gene expression heatmaps
Multi-level integrated analysis of bladder cancer single-cell transcriptome. (A) Cell type identification based on NMF algorithm. (B) Differential gene expression <t>heatmap;</t> (C-D) Cell trajectory inference analysis; (E) Cell-cell interaction network; (F-G) GAR and HGF signaling pathway network activity analysis
Gene Expression Heatmaps, supplied by Plotly Technologies Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gene expression heatmaps/product/Plotly Technologies Inc
Average 86 stars, based on 1 article reviews
gene expression heatmaps - by Bioz Stars, 2026-06
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Image Search Results


Characterization of cHep. A. Direct differentiation of cryopreserved pre-cHep using a protocol equivalent to 3D PHH spheroid formation. B. Representative images of 3D PHH and cHep. C. Principal component analysis (PCA) plot including cHep generated from day 30 (D30) J03, J06, and J13 L4 pre-cHep, 3D PHH, and published hepatocyte-like cells (HLC). D. Heatmap showing expression of 264 liver-enriched genes. E–H. Distance-based similarity scores based on gene expression associated with xenobiotic metabolism (E), amino acid biosynthesis (F), bile acid biosynthesis (G), and the urea cycle (H). I. Immunofluorescence staining for albumin, HNF4α, CYP2E1, and ASGR1. J. Cholyl-lysyl-fluorescein (CLF) staining.

Journal: bioRxiv

Article Title: Precursor of chemically expanded hepatocytes (pre-cHep) with 1-million-fold expansion potential and liver repopulation capacity

doi: 10.64898/2026.05.15.725446

Figure Lengend Snippet: Characterization of cHep. A. Direct differentiation of cryopreserved pre-cHep using a protocol equivalent to 3D PHH spheroid formation. B. Representative images of 3D PHH and cHep. C. Principal component analysis (PCA) plot including cHep generated from day 30 (D30) J03, J06, and J13 L4 pre-cHep, 3D PHH, and published hepatocyte-like cells (HLC). D. Heatmap showing expression of 264 liver-enriched genes. E–H. Distance-based similarity scores based on gene expression associated with xenobiotic metabolism (E), amino acid biosynthesis (F), bile acid biosynthesis (G), and the urea cycle (H). I. Immunofluorescence staining for albumin, HNF4α, CYP2E1, and ASGR1. J. Cholyl-lysyl-fluorescein (CLF) staining.

Article Snippet: Hierarchical clustering and gene expression heatmap analysis of 264 liver-enriched genes from Human Protein Atlas (gene list in Supplementary Table S3) also showed that D30 cHep clustered closely with 3D PHH, although expression of some genes remained lower than in uncultured PHH and liver samples ( ).

Techniques: Generated, Expressing, Gene Expression, Immunofluorescence, Staining

Gene expression analysis of HLC. Distance-based similarity score and gene expression heatmap with KEGG gene sets: Fatty acid metabolism (A, D), Retinol metabolism (B, E) and Complement coagulation cascade (C, F).

Journal: bioRxiv

Article Title: Precursor of chemically expanded hepatocytes (pre-cHep) with 1-million-fold expansion potential and liver repopulation capacity

doi: 10.64898/2026.05.15.725446

Figure Lengend Snippet: Gene expression analysis of HLC. Distance-based similarity score and gene expression heatmap with KEGG gene sets: Fatty acid metabolism (A, D), Retinol metabolism (B, E) and Complement coagulation cascade (C, F).

Article Snippet: Hierarchical clustering and gene expression heatmap analysis of 264 liver-enriched genes from Human Protein Atlas (gene list in Supplementary Table S3) also showed that D30 cHep clustered closely with 3D PHH, although expression of some genes remained lower than in uncultured PHH and liver samples ( ).

Techniques: Gene Expression, Coagulation

Multi-level integrated analysis of bladder cancer single-cell transcriptome. (A) Cell type identification based on NMF algorithm. (B) Differential gene expression heatmap; (C-D) Cell trajectory inference analysis; (E) Cell-cell interaction network; (F-G) GAR and HGF signaling pathway network activity analysis

Journal: Discover Oncology

Article Title: Single cell RNA sequencing decodes cellular heterogeneity and identifies prognostic immune signatures in bladder cancer microenvironment

doi: 10.1007/s12672-025-03878-1

Figure Lengend Snippet: Multi-level integrated analysis of bladder cancer single-cell transcriptome. (A) Cell type identification based on NMF algorithm. (B) Differential gene expression heatmap; (C-D) Cell trajectory inference analysis; (E) Cell-cell interaction network; (F-G) GAR and HGF signaling pathway network activity analysis

Article Snippet: Fig. 4 Comprehensive analysis of bladder cancer cell communication networks and transcriptional regulation. (A) Cell signaling output and reception patterns; (B) Signaling molecule correlation matrix; (C) Biological process enrichment analysis of NMF subtypes; (D) Characteristic gene expression heatmap; (E) Spatial distribution of transcriptional regulator activation states

Techniques: Gene Expression, Activity Assay

Comprehensive analysis of bladder cancer cell communication networks and transcriptional regulation. (A) Cell signaling output and reception patterns; (B) Signaling molecule correlation matrix; (C) Biological process enrichment analysis of NMF subtypes; (D) Characteristic gene expression heatmap; (E) Spatial distribution of transcriptional regulator activation states

Journal: Discover Oncology

Article Title: Single cell RNA sequencing decodes cellular heterogeneity and identifies prognostic immune signatures in bladder cancer microenvironment

doi: 10.1007/s12672-025-03878-1

Figure Lengend Snippet: Comprehensive analysis of bladder cancer cell communication networks and transcriptional regulation. (A) Cell signaling output and reception patterns; (B) Signaling molecule correlation matrix; (C) Biological process enrichment analysis of NMF subtypes; (D) Characteristic gene expression heatmap; (E) Spatial distribution of transcriptional regulator activation states

Article Snippet: Fig. 4 Comprehensive analysis of bladder cancer cell communication networks and transcriptional regulation. (A) Cell signaling output and reception patterns; (B) Signaling molecule correlation matrix; (C) Biological process enrichment analysis of NMF subtypes; (D) Characteristic gene expression heatmap; (E) Spatial distribution of transcriptional regulator activation states

Techniques: Gene Expression, Activation Assay

Dynamic trajectory and communication network analysis of bladder cancer single-cell transcriptome. (A) Cell typeidentification based on NMF; (B) Characteristic gene expression heatmap; (C-D) Cell differentiation trajectory inference; (E) Cell-cell interaction network; (F) Signaling pathway activation state analysis

Journal: Discover Oncology

Article Title: Single cell RNA sequencing decodes cellular heterogeneity and identifies prognostic immune signatures in bladder cancer microenvironment

doi: 10.1007/s12672-025-03878-1

Figure Lengend Snippet: Dynamic trajectory and communication network analysis of bladder cancer single-cell transcriptome. (A) Cell typeidentification based on NMF; (B) Characteristic gene expression heatmap; (C-D) Cell differentiation trajectory inference; (E) Cell-cell interaction network; (F) Signaling pathway activation state analysis

Article Snippet: Fig. 4 Comprehensive analysis of bladder cancer cell communication networks and transcriptional regulation. (A) Cell signaling output and reception patterns; (B) Signaling molecule correlation matrix; (C) Biological process enrichment analysis of NMF subtypes; (D) Characteristic gene expression heatmap; (E) Spatial distribution of transcriptional regulator activation states

Techniques: Gene Expression, Cell Differentiation, Activation Assay

Multi-level analysis of bladder cancer transcriptional regulatory networks. (A) Correlation analysis between transcription factors and target genes; (B) Transcriptional regulator activation heatmap; (C) Spatial distribution of regulatory network activation; (D) Core regulatory factor activity comparison; (E) Biological process enrichment analysis

Journal: Discover Oncology

Article Title: Single cell RNA sequencing decodes cellular heterogeneity and identifies prognostic immune signatures in bladder cancer microenvironment

doi: 10.1007/s12672-025-03878-1

Figure Lengend Snippet: Multi-level analysis of bladder cancer transcriptional regulatory networks. (A) Correlation analysis between transcription factors and target genes; (B) Transcriptional regulator activation heatmap; (C) Spatial distribution of regulatory network activation; (D) Core regulatory factor activity comparison; (E) Biological process enrichment analysis

Article Snippet: Fig. 4 Comprehensive analysis of bladder cancer cell communication networks and transcriptional regulation. (A) Cell signaling output and reception patterns; (B) Signaling molecule correlation matrix; (C) Biological process enrichment analysis of NMF subtypes; (D) Characteristic gene expression heatmap; (E) Spatial distribution of transcriptional regulator activation states

Techniques: Activation Assay, Activity Assay, Comparison