colocalization analysis Search Results


90
universal imaging inc colocalization and integrated morphometric analysis applications
Impaired mitochondrial fusion in the soma and motor axons of G93A SOD1 motor neurons. A, Cell bodies from non-transgenic control and G93A SOD1 motor neurons containing mitoDendra-labeled mitochondria before (−1 min) and after (0 min) photo-activation. Subsets of mitochondria (green and red fluorescence) were followed over time (10, 40, 70, and 100 min) by live imaging microscopy. Note that the appearance of yellow fluorescence, as a result of the mixing between green and red mitochondria (i.e. fusion, indicated by arrows), was delayed in mutant SOD1 motor neurons. Scale bar, 10 μm. B, Fusion rates were obtained in single optical z-sections by measuring the <t>colocalization</t> (in %) of red over green fluorescent mitochondria at the indicated time points. The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. C, In the inset, example of a time-lapse recording of axonal mitochondria (numbers indicate time in min) showing a fusion event (arrow). Scale bar, 5 μm. The graph shows the analysis of fusion (% of fusion events of total moving mitochondria) in control and G93A SOD1 motor axons. n (axons) = 24 non-transgenic, 28 WT and 16 G93A axonal segments. *P<0.05 versus non-transgenic. D, Time-lapse microscopy of mitochondrial transport in the soma of non-transgenic and G93A SOD1 motor neurons. All mitochondria in a ROI of the soma were photo-converted, while only non-photo-converted (green fluorescent) mitochondria were followed over time. Note a decrease of mobile mitochondria towards the ROI in mutant SOD1 motor neurons compared to controls. Scale bar, 10 μm. E, Analysis of the transport of green mitochondria over the photo-activated area (no green mitochondria present). The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. F, Analysis of the transport of red mitochondria over the non-photo-activated area (no red mitochondria present). The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. All data obtained from 3–5 independent experiments. The error bars represent ± SE.
Colocalization And Integrated Morphometric Analysis Applications, supplied by universal imaging 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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MetaMorph Inc software colocalization analysis
A pool of myosin VI localizes on the microvillus of intestinal epithelial cells. (A) Immunofluorescence (confocal microscopic) detection of myosin VI expression and localization in Caco-2/Bbe cells and mouse ileum. In Caco-2/Bbe cells, myosin VI is present throughout the brush border, including in the microvillus and in a vesicular pattern in the terminal web and other intracellular compartments (a1–a4). In mouse ileum, myosin VI is present to a lesser extent on the microvillus and in the terminal web (b1–b4). As a negative control, anti-myosin VI antibody was used to show there is no myosin VI staining in ileum from myosin VI KO mice (d) and myosin VI KD Caco-2/Bbe cells (f). (c) wild-type (WT) control; (e) empty-vector-infected control. Scale bar: 20 µm. (B) Analysis of the overlap of myosin VI and protein markers of microvillus by MetaMorph <t>colocalization</t> software. Please note this software shows presence of two proteins in the same intracellular compartment but does not establish physical association. In Caco-2/Bbe cells, ∼46% of myosin VI overlaps with villin, and in mouse ileum ∼9% of myosin VI overlaps with aquaporin 7. For both Caco-2/Bbe cells and mouse ileum, results are mean±s.e.m. of three separate experiments with five or six images analyzed in each experiment (the total number of images analyzed was 16).
Software Colocalization Analysis, supplied by MetaMorph 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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Matisse Pharmaceuticals colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Colocalization Analysis, supplied by Matisse Pharmaceuticals, 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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Visiopharm AS colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Colocalization Analysis, supplied by Visiopharm AS, 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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Aperio Technologies image-scope 10.0 colocalization analysis algorithm
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Image Scope 10.0 Colocalization Analysis Algorithm, supplied by Aperio Technologies, 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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Boston Biomedical colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Colocalization Analysis, supplied by Boston Biomedical, 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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Institut Curie colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Colocalization Analysis, supplied by Institut Curie, 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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Comeau Technique colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Colocalization Analysis, supplied by Comeau Technique, 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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KNIME GmbH custom-made knime analysis pipeline (termed ‘track-based colocalization analysis (with recursive track pairing and particle number correction)
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Custom Made Knime Analysis Pipeline (Termed ‘Track Based Colocalization Analysis (With Recursive Track Pairing And Particle Number Correction), supplied by KNIME GmbH, 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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KNIME GmbH high-throughput segmentation and colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
High Throughput Segmentation And Colocalization Analysis, supplied by KNIME GmbH, 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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KNIME GmbH high-throughput colocalization analysis
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
High Throughput Colocalization Analysis, supplied by KNIME GmbH, 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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Aperio Technologies imagescope 10.0 colocalization analysis algorithm
Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, <t>colocalization</t> analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others
Imagescope 10.0 Colocalization Analysis Algorithm, supplied by Aperio Technologies, 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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Image Search Results


Impaired mitochondrial fusion in the soma and motor axons of G93A SOD1 motor neurons. A, Cell bodies from non-transgenic control and G93A SOD1 motor neurons containing mitoDendra-labeled mitochondria before (−1 min) and after (0 min) photo-activation. Subsets of mitochondria (green and red fluorescence) were followed over time (10, 40, 70, and 100 min) by live imaging microscopy. Note that the appearance of yellow fluorescence, as a result of the mixing between green and red mitochondria (i.e. fusion, indicated by arrows), was delayed in mutant SOD1 motor neurons. Scale bar, 10 μm. B, Fusion rates were obtained in single optical z-sections by measuring the colocalization (in %) of red over green fluorescent mitochondria at the indicated time points. The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. C, In the inset, example of a time-lapse recording of axonal mitochondria (numbers indicate time in min) showing a fusion event (arrow). Scale bar, 5 μm. The graph shows the analysis of fusion (% of fusion events of total moving mitochondria) in control and G93A SOD1 motor axons. n (axons) = 24 non-transgenic, 28 WT and 16 G93A axonal segments. *P<0.05 versus non-transgenic. D, Time-lapse microscopy of mitochondrial transport in the soma of non-transgenic and G93A SOD1 motor neurons. All mitochondria in a ROI of the soma were photo-converted, while only non-photo-converted (green fluorescent) mitochondria were followed over time. Note a decrease of mobile mitochondria towards the ROI in mutant SOD1 motor neurons compared to controls. Scale bar, 10 μm. E, Analysis of the transport of green mitochondria over the photo-activated area (no green mitochondria present). The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. F, Analysis of the transport of red mitochondria over the non-photo-activated area (no red mitochondria present). The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. All data obtained from 3–5 independent experiments. The error bars represent ± SE.

Journal: The Journal of Neuroscience

Article Title: Mitochondrial Dynamics and Bioenergetic Dysfunction Is Associated with Synaptic Alterations in Mutant SOD1 Motor Neurons

doi: 10.1523/JNEUROSCI.1233-11.2012

Figure Lengend Snippet: Impaired mitochondrial fusion in the soma and motor axons of G93A SOD1 motor neurons. A, Cell bodies from non-transgenic control and G93A SOD1 motor neurons containing mitoDendra-labeled mitochondria before (−1 min) and after (0 min) photo-activation. Subsets of mitochondria (green and red fluorescence) were followed over time (10, 40, 70, and 100 min) by live imaging microscopy. Note that the appearance of yellow fluorescence, as a result of the mixing between green and red mitochondria (i.e. fusion, indicated by arrows), was delayed in mutant SOD1 motor neurons. Scale bar, 10 μm. B, Fusion rates were obtained in single optical z-sections by measuring the colocalization (in %) of red over green fluorescent mitochondria at the indicated time points. The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. C, In the inset, example of a time-lapse recording of axonal mitochondria (numbers indicate time in min) showing a fusion event (arrow). Scale bar, 5 μm. The graph shows the analysis of fusion (% of fusion events of total moving mitochondria) in control and G93A SOD1 motor axons. n (axons) = 24 non-transgenic, 28 WT and 16 G93A axonal segments. *P<0.05 versus non-transgenic. D, Time-lapse microscopy of mitochondrial transport in the soma of non-transgenic and G93A SOD1 motor neurons. All mitochondria in a ROI of the soma were photo-converted, while only non-photo-converted (green fluorescent) mitochondria were followed over time. Note a decrease of mobile mitochondria towards the ROI in mutant SOD1 motor neurons compared to controls. Scale bar, 10 μm. E, Analysis of the transport of green mitochondria over the photo-activated area (no green mitochondria present). The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. F, Analysis of the transport of red mitochondria over the non-photo-activated area (no red mitochondria present). The correlation coefficient (r) for each group is indicated. n (somas) = 8 non-transgenic, and 7 G93A. *P<0.05 by ANOVA with repeated measurements. All data obtained from 3–5 independent experiments. The error bars represent ± SE.

Article Snippet: Colocalization and Integrated Morphometric Analysis applications were from Metamorph software (Universal Imaging Co.).

Techniques: Transgenic Assay, Control, Labeling, Activation Assay, Fluorescence, Imaging, Microscopy, Mutagenesis, Time-lapse Microscopy

A pool of myosin VI localizes on the microvillus of intestinal epithelial cells. (A) Immunofluorescence (confocal microscopic) detection of myosin VI expression and localization in Caco-2/Bbe cells and mouse ileum. In Caco-2/Bbe cells, myosin VI is present throughout the brush border, including in the microvillus and in a vesicular pattern in the terminal web and other intracellular compartments (a1–a4). In mouse ileum, myosin VI is present to a lesser extent on the microvillus and in the terminal web (b1–b4). As a negative control, anti-myosin VI antibody was used to show there is no myosin VI staining in ileum from myosin VI KO mice (d) and myosin VI KD Caco-2/Bbe cells (f). (c) wild-type (WT) control; (e) empty-vector-infected control. Scale bar: 20 µm. (B) Analysis of the overlap of myosin VI and protein markers of microvillus by MetaMorph colocalization software. Please note this software shows presence of two proteins in the same intracellular compartment but does not establish physical association. In Caco-2/Bbe cells, ∼46% of myosin VI overlaps with villin, and in mouse ileum ∼9% of myosin VI overlaps with aquaporin 7. For both Caco-2/Bbe cells and mouse ileum, results are mean±s.e.m. of three separate experiments with five or six images analyzed in each experiment (the total number of images analyzed was 16).

Journal: Journal of Cell Science

Article Title: Myosin VI mediates the movement of NHE3 down the microvillus in intestinal epithelial cells

doi: 10.1242/jcs.149930

Figure Lengend Snippet: A pool of myosin VI localizes on the microvillus of intestinal epithelial cells. (A) Immunofluorescence (confocal microscopic) detection of myosin VI expression and localization in Caco-2/Bbe cells and mouse ileum. In Caco-2/Bbe cells, myosin VI is present throughout the brush border, including in the microvillus and in a vesicular pattern in the terminal web and other intracellular compartments (a1–a4). In mouse ileum, myosin VI is present to a lesser extent on the microvillus and in the terminal web (b1–b4). As a negative control, anti-myosin VI antibody was used to show there is no myosin VI staining in ileum from myosin VI KO mice (d) and myosin VI KD Caco-2/Bbe cells (f). (c) wild-type (WT) control; (e) empty-vector-infected control. Scale bar: 20 µm. (B) Analysis of the overlap of myosin VI and protein markers of microvillus by MetaMorph colocalization software. Please note this software shows presence of two proteins in the same intracellular compartment but does not establish physical association. In Caco-2/Bbe cells, ∼46% of myosin VI overlaps with villin, and in mouse ileum ∼9% of myosin VI overlaps with aquaporin 7. For both Caco-2/Bbe cells and mouse ileum, results are mean±s.e.m. of three separate experiments with five or six images analyzed in each experiment (the total number of images analyzed was 16).

Article Snippet: Overlap of myosin VI with the microvillus marker aquaporin 7 was determined with MetaMorph software colocalization analysis.

Techniques: Immunofluorescence, Expressing, Negative Control, Staining, Plasmid Preparation, Infection, Software

Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, colocalization analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others

Journal: BMC Bioinformatics

Article Title: Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps

doi: 10.1186/s12859-021-04302-5

Figure Lengend Snippet: Schematic representation of the main analysis workflow proposed described in MATISSE. The Cartesian coordinates of all reads found in the section analyzed is used to create the initial Matisse object. Several functionalities including KDE plots, colocalization analysis, gene quantification and gradient identification and analysis can be applied using this object as an input. Data can also be segmented, based on the cell boundaries, the location of individual spots or using a grid equally distributed along the section. Its output, stored in a second Matisse object, can be used for cell typing and clustering the data, among others

Article Snippet: A wide number of different analysis can be performed with Matisse , including colocalization analysis, Kernel Density Estimation (KDE), and the exploration of gradients for unsegmented datasets, as well as, for example, de novo clustering, dimensional reduction, low dimensional RGB representation, probabilistic cell typing (pciSeq) [ ] and gene co-expression for segmented datasets.

Techniques:

Analysis of the expression of 17 genes in the mouse cortex. A One dimensional KDE estimation of the expression of the 17 genes along the dorso-ventral axis of the cortex. Genes are randomly divided in two line plots to facilitate their comprehension. B Heat map representing the colocalization between the genes analyzed. Positive Z-scores (red) represent colocalization of the genes and negative Z-score (blue) represent mutually exclusive expression. C KDE of the expression of several different genes, represented pairwise. Different co-expression patterns are represented including mutually exclusive genes (top,left),colocalizing genes (down,left), partially colocalizing genes (top,right) and genes with non-related expression patterns (down,right). D Two-dimensional map of the bins generated when segmenting the mouse coronal section. Color code corresponds to the RGB loadings of each bin’s score on the top three UMAP components found when doing dimensionality reduction analysis. Different colors, indicating different loadings for each of components are found in different areas of the brain, highlighting the difference in expression found for the genes included in the panel. E Two-dimensional map of the bins generated previously, where each color represents one of the 15 clusters defined by performing hierarchical clustering on the segmented dataset. F Mean expression of each of the clusters defined in E for all the genes included in the analysis. The colors of each cluster, on the Y axis, correspond to the colors used in E for each cluster

Journal: BMC Bioinformatics

Article Title: Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps

doi: 10.1186/s12859-021-04302-5

Figure Lengend Snippet: Analysis of the expression of 17 genes in the mouse cortex. A One dimensional KDE estimation of the expression of the 17 genes along the dorso-ventral axis of the cortex. Genes are randomly divided in two line plots to facilitate their comprehension. B Heat map representing the colocalization between the genes analyzed. Positive Z-scores (red) represent colocalization of the genes and negative Z-score (blue) represent mutually exclusive expression. C KDE of the expression of several different genes, represented pairwise. Different co-expression patterns are represented including mutually exclusive genes (top,left),colocalizing genes (down,left), partially colocalizing genes (top,right) and genes with non-related expression patterns (down,right). D Two-dimensional map of the bins generated when segmenting the mouse coronal section. Color code corresponds to the RGB loadings of each bin’s score on the top three UMAP components found when doing dimensionality reduction analysis. Different colors, indicating different loadings for each of components are found in different areas of the brain, highlighting the difference in expression found for the genes included in the panel. E Two-dimensional map of the bins generated previously, where each color represents one of the 15 clusters defined by performing hierarchical clustering on the segmented dataset. F Mean expression of each of the clusters defined in E for all the genes included in the analysis. The colors of each cluster, on the Y axis, correspond to the colors used in E for each cluster

Article Snippet: A wide number of different analysis can be performed with Matisse , including colocalization analysis, Kernel Density Estimation (KDE), and the exploration of gradients for unsegmented datasets, as well as, for example, de novo clustering, dimensional reduction, low dimensional RGB representation, probabilistic cell typing (pciSeq) [ ] and gene co-expression for segmented datasets.

Techniques: Expressing, Generated