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SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data.

Lang S, Ugale A, Erlandsson E, Karlsson G, Bryder D, Soneji S - BMC Bioinformatics (2015)

Bottom Line: This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype.SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

View Article: PubMed Central - PubMed

Affiliation: Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. stefan.lang@med.lu.se.

ABSTRACT

Background: Single cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.

Results: We present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.

Conclusions: SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

No MeSH data available.


An SCExV session. Single cell qRT-PCR data has been clustered and partitioned into 5 groups (coloured bar in c) which defines the order of the index cell sorting data (d) and the colouring of cells in the PCA plot (b). The violin plot (a) gives an overview of expression within these 5 groups for a gene of interest, here Zfpm1 which shows strong expression in the blue cluster (a and c) which denotes a cluster of cells expressing an erythroid program
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Fig1: An SCExV session. Single cell qRT-PCR data has been clustered and partitioned into 5 groups (coloured bar in c) which defines the order of the index cell sorting data (d) and the colouring of cells in the PCA plot (b). The violin plot (a) gives an overview of expression within these 5 groups for a gene of interest, here Zfpm1 which shows strong expression in the blue cluster (a and c) which denotes a cluster of cells expressing an erythroid program

Mentions: The output from the analysis module is split into three main sections. The first pane shows the expression level of any selected gene within groups (e.g. clusters) as a violin plot (Fig. 1a), and the second displays the output from multidimensional scaling (PCA is shown in Fig. 1b). We have provided three viewing options i) the first 2 components ii) rotatable plot of components 1–3, and iii) 3D densities of components 1–3. Below the violin and MDS plots are heatmaps of the qRT-PCR expression data and surface marker intensities from the index sorting (Fig. 1c and 1d). Along with PCA, we have also implemented isomaps and local loop embedding (LLE) as alternatives [6]. We have provided two clustering methods; hierarchical clustering which uses the correlation distance by default (users have the option to choose the agglomeration rule), and kmeans. These can be applied to the expression and index sorting data.Fig. 1


SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data.

Lang S, Ugale A, Erlandsson E, Karlsson G, Bryder D, Soneji S - BMC Bioinformatics (2015)

An SCExV session. Single cell qRT-PCR data has been clustered and partitioned into 5 groups (coloured bar in c) which defines the order of the index cell sorting data (d) and the colouring of cells in the PCA plot (b). The violin plot (a) gives an overview of expression within these 5 groups for a gene of interest, here Zfpm1 which shows strong expression in the blue cluster (a and c) which denotes a cluster of cells expressing an erythroid program
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4595270&req=5

Fig1: An SCExV session. Single cell qRT-PCR data has been clustered and partitioned into 5 groups (coloured bar in c) which defines the order of the index cell sorting data (d) and the colouring of cells in the PCA plot (b). The violin plot (a) gives an overview of expression within these 5 groups for a gene of interest, here Zfpm1 which shows strong expression in the blue cluster (a and c) which denotes a cluster of cells expressing an erythroid program
Mentions: The output from the analysis module is split into three main sections. The first pane shows the expression level of any selected gene within groups (e.g. clusters) as a violin plot (Fig. 1a), and the second displays the output from multidimensional scaling (PCA is shown in Fig. 1b). We have provided three viewing options i) the first 2 components ii) rotatable plot of components 1–3, and iii) 3D densities of components 1–3. Below the violin and MDS plots are heatmaps of the qRT-PCR expression data and surface marker intensities from the index sorting (Fig. 1c and 1d). Along with PCA, we have also implemented isomaps and local loop embedding (LLE) as alternatives [6]. We have provided two clustering methods; hierarchical clustering which uses the correlation distance by default (users have the option to choose the agglomeration rule), and kmeans. These can be applied to the expression and index sorting data.Fig. 1

Bottom Line: This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype.SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

View Article: PubMed Central - PubMed

Affiliation: Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. stefan.lang@med.lu.se.

ABSTRACT

Background: Single cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.

Results: We present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.

Conclusions: SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

No MeSH data available.