scrna-seq data analysis Search Results


scrna-seq data analysis functions  (Expression Analysis Co)
90
Expression Analysis Co scrna-seq data analysis functions
Scrna Seq Data Analysis Functions, supplied by Expression Analysis Co, 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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scrnaseq data analysis codes  (10X Genomics)
90
10X Genomics scrnaseq data analysis codes
A Schematic work flow for 10× Genomics <t>scRNASeq</t> of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.
Scrnaseq Data Analysis Codes, supplied by 10X Genomics, 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/scrnaseq data analysis codes/product/10X Genomics
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scrnaseq data analysis codes - by Bioz Stars, 2026-03
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flow-based scrna-seq data analysis  (Partek)
90
Partek flow-based scrna-seq data analysis
A Schematic work flow for 10× Genomics <t>scRNASeq</t> of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.
Flow Based Scrna Seq Data Analysis, supplied by Partek, 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/flow-based scrna-seq data analysis/product/Partek
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preprocessed scrnaseq data and analysis scripts  (Mendeley Ltd)
90
Mendeley Ltd preprocessed scrnaseq data and analysis scripts
A Schematic work flow for 10× Genomics <t>scRNASeq</t> of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.
Preprocessed Scrnaseq Data And Analysis Scripts, supplied by Mendeley Ltd, 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/preprocessed scrnaseq data and analysis scripts/product/Mendeley Ltd
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trailmakertm scrna-seq data analysis platform  (Parse Biosciences)
90
Parse Biosciences trailmakertm scrna-seq data analysis platform
A Schematic work flow for 10× Genomics <t>scRNASeq</t> of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.
Trailmakertm Scrna Seq Data Analysis Platform, supplied by Parse Biosciences, 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/trailmakertm scrna-seq data analysis platform/product/Parse Biosciences
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trailmakertm scrna-seq data analysis platform - by Bioz Stars, 2026-03
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scrna-seq data analysis  (Cold Spring Harbor Laboratory Meetings)
90
Cold Spring Harbor Laboratory Meetings scrna-seq data analysis
A Schematic work flow for 10× Genomics <t>scRNASeq</t> of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.
Scrna Seq Data Analysis, supplied by Cold Spring Harbor Laboratory Meetings, 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/scrna-seq data analysis/product/Cold Spring Harbor Laboratory Meetings
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scrna-seq data analysis  (GraphPad Software Inc)
90
GraphPad Software Inc scrna-seq data analysis
A Schematic work flow for 10× Genomics <t>scRNASeq</t> of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.
Scrna Seq Data Analysis, 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
https://www.bioz.com/result/scrna-seq data analysis/product/GraphPad Software Inc
Average 90 stars, based on 1 article reviews
scrna-seq data analysis - by Bioz Stars, 2026-03
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Image Search Results


A Schematic work flow for 10× Genomics scRNASeq of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.

Journal: The EMBO Journal

Article Title: Single‐cell transcriptomics of suprachiasmatic nuclei reveal a Prokineticin‐driven circadian network

doi: 10.15252/embj.2021108614

Figure Lengend Snippet: A Schematic work flow for 10× Genomics scRNASeq of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.

Article Snippet: Codes used to analyse scRNASeq data are publicly available from 10× Genomics at “ https://support.10xgenomics.com/single‐cell‐gene‐expression/software/overview/welcome .” Viral reagents generated in this study have been deposited with Addgene, AAV1.pProk2.Cre.T2A.mCherry Plasmid #169013 and AAV1.pProkR2.Cre.T2A.Venus Plasmid #169014.

Techniques: Sequencing, Suspension, Reverse Transcription, Amplification, Generated, Gene Expression

A Schematic view of populations of interest from Venn analysis of scRNAseq data from daytime SCN neurons. B–E Representative RNAScope in situ hybridisation images from SCN cryostat sections to reveal overlap/non‐overlap of neuropeptide/receptor‐expressing cells. (B) Cells expressing Prok2 (green) and/or Avp (red). (C) Cells expressing Vipr2 and/or ProkR2 . (D) Potential autocrine signalling in cells expressing Prok2 + and/or ProkR2 + . (E) Cells expressing Av p and/or Avpr1a . Left: 40×, right 63×, scale bar: 100 µm. For each gene set, panel A and B show magnified 63× images. White arrows highlight the overlap‐/non‐overlap of neuropeptide/ receptor‐expressing cells. F, G Inferred topology of neuropeptidergic signalling axes between identified neuronal sub‐populations in SCN from circadian day (F) or circadian night (G). Inter‐cluster signalling is unscaled as connections are weighted by highest expression count measured across the entire dataset. Clusters are numbered according to their size and are annotated in the overview t‐SNE for day (bottom left) and night (top right), respectively. Source data are available online for this figure.

Journal: The EMBO Journal

Article Title: Single‐cell transcriptomics of suprachiasmatic nuclei reveal a Prokineticin‐driven circadian network

doi: 10.15252/embj.2021108614

Figure Lengend Snippet: A Schematic view of populations of interest from Venn analysis of scRNAseq data from daytime SCN neurons. B–E Representative RNAScope in situ hybridisation images from SCN cryostat sections to reveal overlap/non‐overlap of neuropeptide/receptor‐expressing cells. (B) Cells expressing Prok2 (green) and/or Avp (red). (C) Cells expressing Vipr2 and/or ProkR2 . (D) Potential autocrine signalling in cells expressing Prok2 + and/or ProkR2 + . (E) Cells expressing Av p and/or Avpr1a . Left: 40×, right 63×, scale bar: 100 µm. For each gene set, panel A and B show magnified 63× images. White arrows highlight the overlap‐/non‐overlap of neuropeptide/ receptor‐expressing cells. F, G Inferred topology of neuropeptidergic signalling axes between identified neuronal sub‐populations in SCN from circadian day (F) or circadian night (G). Inter‐cluster signalling is unscaled as connections are weighted by highest expression count measured across the entire dataset. Clusters are numbered according to their size and are annotated in the overview t‐SNE for day (bottom left) and night (top right), respectively. Source data are available online for this figure.

Article Snippet: Codes used to analyse scRNASeq data are publicly available from 10× Genomics at “ https://support.10xgenomics.com/single‐cell‐gene‐expression/software/overview/welcome .” Viral reagents generated in this study have been deposited with Addgene, AAV1.pProk2.Cre.T2A.mCherry Plasmid #169013 and AAV1.pProkR2.Cre.T2A.Venus Plasmid #169014.

Techniques: RNAscope, In Situ, Hybridization, Expressing

Journal: The EMBO Journal

Article Title: Single‐cell transcriptomics of suprachiasmatic nuclei reveal a Prokineticin‐driven circadian network

doi: 10.15252/embj.2021108614

Figure Lengend Snippet:

Article Snippet: Codes used to analyse scRNASeq data are publicly available from 10× Genomics at “ https://support.10xgenomics.com/single‐cell‐gene‐expression/software/overview/welcome .” Viral reagents generated in this study have been deposited with Addgene, AAV1.pProk2.Cre.T2A.mCherry Plasmid #169013 and AAV1.pProkR2.Cre.T2A.Venus Plasmid #169014.

Techniques: Plasmid Preparation, Recombinant, Modification, RNAscope, Multiplex Assay, Software