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single cell rna sequencing scrna seq dataset gse267718  (10X Genomics)
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single cell rna sequencing scrna seq datasets  (10X Genomics)
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10X Genomics single cell rna sequencing scrna seq datasets
<t>Single-cell</t> <t>transcriptomic</t> analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts <t>(nFeature_RNA),</t> UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell <t>RNA-seq</t> data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.
Single Cell Rna Sequencing Scrna Seq Datasets, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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single cell rna sequencing scrna seq datasets - by Bioz Stars, 2026-08
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cell rna seq dataset  (Broad Clinical Labs)
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Broad Clinical Labs cell rna seq dataset
<t>Single-cell</t> <t>transcriptomic</t> analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts <t>(nFeature_RNA),</t> UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell <t>RNA-seq</t> data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.
Cell Rna Seq Dataset, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cell rna seq dataset - by Bioz Stars, 2026-08
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single cell rna sequencing scrna seq 226 dataset  (Biotechnology Information)
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Biotechnology Information single cell rna sequencing scrna seq 226 dataset
<t>Single-cell</t> <t>transcriptomic</t> analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts <t>(nFeature_RNA),</t> UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell <t>RNA-seq</t> data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.
Single Cell Rna Sequencing Scrna Seq 226 Dataset, supplied by Biotechnology Information, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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single cell rna sequencing scrna seq 226 dataset - by Bioz Stars, 2026-08
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single cell rna seq datasets  (Broad Clinical Labs)
96
Broad Clinical Labs single cell rna seq datasets
<t>Single-cell</t> <t>transcriptomic</t> analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts <t>(nFeature_RNA),</t> UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell <t>RNA-seq</t> data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.
Single Cell Rna Seq Datasets, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rna-sequencing+datasets/pm41974707-603-0-7?v=Broad+Clinical+Labs
Average 96 stars, based on 1 article reviews
single cell rna seq datasets - by Bioz Stars, 2026-08
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single cell rna sequencing available dataset  (Broad Clinical Labs)
96
Broad Clinical Labs single cell rna sequencing available dataset
a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the <t>human</t> <t>single-cell</t> <t>RNA</t> <t>sequencing</t> available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.
Single Cell Rna Sequencing Available Dataset, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rna-sequencing+datasets/bio_rxiv__64898__2026__04__10__717646-249-39-47?v=Broad+Clinical+Labs
Average 96 stars, based on 1 article reviews
single cell rna sequencing available dataset - by Bioz Stars, 2026-08
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rna sequencing datasets  (Biotechnology Information)
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Biotechnology Information rna sequencing datasets
a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the <t>human</t> <t>single-cell</t> <t>RNA</t> <t>sequencing</t> available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.
Rna Sequencing Datasets, supplied by Biotechnology Information, 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/product/rna-sequencing+datasets/pm41935326-186-28-37?v=Biotechnology+Information
Average 86 stars, based on 1 article reviews
rna sequencing datasets - by Bioz Stars, 2026-08
86/100 stars
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Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

Journal: Frontiers in Immunology

Article Title: Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

doi: 10.3389/fimmu.2026.1705706

Figure Lengend Snippet: Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

Article Snippet: Single-cell RNA sequencing (scRNA-seq) datasets were obtained from GSE145086 and GSE233084 , both generated using the 10X Genomics platform ( , ).

Techniques: Single Cell, Control, RNA Sequencing, Cell Cycle Assay, Expressing

a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the human single-cell RNA sequencing available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.

Journal: bioRxiv

Article Title: Rare variants alter mitochondrial lipid homeostasis and neuronal excitability in PD patient-derived dopaminergic neurons

doi: 10.64898/2026.04.10.717646

Figure Lengend Snippet: a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the human single-cell RNA sequencing available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.

Article Snippet: However, considering the specific loss of the dopaminergic neurons of the Substantia Nigra pars compacta in PD patients, we tested the expression of the genes encoding for the 62 down-regulated proteins in the SNpc cells, by analyzing the human single-cell RNA sequencing available dataset [ ] ( https://singlecell.broadinstitute.org/single_cell/app/genes ).

Techniques: Derivative Assay, Expressing, Single Cell, RNA Sequencing