multivis 0.1.10

MultiVis is a data visualisation package that produces both static and interactive visualisations targeted towards the Omics community.

MultiVis

The MultiVis package contains the necessary tools for visualisation of multivariate data.

Installation

Dependencies

multivis requires:

• Python (>=3.5)
• NumPy (>=1.12)
• Pandas
• Matplotlib
• Seaborn
• Networkx
• SciPy
• Scikit-learn
• tqdm
• xlrd

User installation

The recommend way to install multivis and dependencies is to using conda:

conda install -c brett.chapman multivis

or pip:

pip install multivis

Alternatively, to install directly from github:

pip install https://github.com/brettChapman/multivis/archive/master.zip

API

For further detail on the usage refer to the docstring.

multivis

• Edge: Generates dataframe of nodes and edges.

  • init_parameters
    • [peaktable] : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'.
    • [similarities] : Pandas dataframe matrix containing similarity scores.
    • [pvalues] : Pandas dataframe matrix containing similarity pvalues.
  • methods

    • [set_params] : Set parameters

      • [filterScoreType] : Value type to filter similarities on (default: 'pvalue')
      • [hard_threshold] : Value to filter similarities on (default: 0.005)
      • [internalSimilarities] : Include similarities within blocks if building multi-block network (default: False)
      • [sign] : The sign of the similarity score to filter on ('pos', 'neg' or 'both') (default: 'both')
      • [node_color_column] : The Peak Table column to use for node colours (default: None sets to black)
      • [node_cmap] : The CMAP colour palette to use for nodes (default: 'brg')

    • [run] : Builds the nodes and edges.

    • [getNodes] : Returns a Pandas dataframe of all nodes.

    • [getEdges] : Returns a Pandas dataframe of all edges.

• Network: Inherits from Edge and generates dataframe of nodes, edges, and a NetworkX graph.

  • init_parameters
    • [peaktable] : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'.
    • [similarities] : Pandas dataframe matrix containing similarity scores.
    • [pvalues] : Pandas dataframe matrix containing similarity pvalues.
  • methods

    • [set_params] : Set parameters

      • [filterScoreType] : Value type to filter similarities on (default: 'pvalue')
      • [hard_threshold] : Value to filter similarities on (default: 0.005)
      • [link_type] : The value type to represent links in the network (default: 'score')
      • [internalSimilarities] : Include similarities within blocks if building multi-block network (default: False)
      • [sign] : The sign of the similarity score to filter on ('pos', 'neg' or 'both') (default: 'both')
      • [node_color_column] : The Peak Table column to use for node colours (default: None sets to black)
      • [node_cmap] : The CMAP colour palette to use for nodes (default: 'brg')

    • [run] : Builds nodes, edges and NetworkX graph.

    • [getNetworkx] : Returns a NetworkX graph.

    • [getLinkType] : Returns the link type parameter used in building the network.

• edgeBundle: Generates and displays a Hierarchical edge bundle plot.

  • init_parameters
    • [edges] : Pandas dataframe containing edges generated from Edge.
  • methods

    • [set_params] : Set parameters

      • [html_file] : Name to save the HTML file as (default: 'hEdgeBundle.html')
      • [innerRadiusOffset] : Sets the inner radius based on the offset value from the canvas width/diameter (default: 120)
      • [groupSeparation] : Value to set the distance between different segmented groups (default: 1)
      • [linkFadeOpacity] : The link fade opacity when hovering over/clicking nodes (default: 0.05)
      • [mouseOver] : Setting to 'True' swaps from clicking to hovering over nodes to select them (default: True)
      • [fontSize] : The font size set to each node (default: 1)
      • [backgroundColor] : Set the background colour of the plot (default: 'white')
      • [foregroundColor] : Set the foreground colour of the plot (default: 'black')
      • [edge_color_scale] : Set the values to colour the edges by. Either 'score or 'pvalue' (default: 'score')
      • [edge_cmap] : Set the CMAP colour palette to use for colouring the edges (default: 'brg')

    • [run] : Generates the JavaScript embedded HTML code and writes to a HTML file

• plotNetwork: Generates and displays a static NetworkX graph given a user defined layout.

  • init_parameters
    • [g] : NetworkX graph.
  • methods

    • [set_params] : Set parameters

      • [imageFileName] : The image file name to save to (default: 'networkPlot.jpg')
      • [edgeLabels] : Setting to 'True' labels all edges with the similarity score (default: True)
      • [saveImage] : Setting to 'True' will save the image to file (default: True)
      • [layout] : Set the NetworkX layout type ('circular', 'kamada_kawai', 'random', 'spring', 'spectral') (default: 'spring')
      • [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)
      • [figSize] : The figure size as a tuple (width,height) (default: (30,20))
      • [sizing_column] : The node sizing colomn to use (default: sizes all nodes to 1)
      • [sizeScale] : The node size scale to apply ("linear", "reverse_linear", "log", "reverse_log", "square", "reverse_square", "area", "reverse_area", "volume", "reverse_volume") (default: 'reverse_linear')
      • [size_range] : The node size scale range to apply. Tuple of length 2. Minimum size to maximum size (default: (150,2000))
      • [alpha] : Node opacity value (default: 0.5)
      • [nodeLabels] : Setting to 'True' will label the nodes (default: True)
      • [fontSize] : The font size set for each node (default: 15)
      • [keepSingletons] : Setting to 'True' will keep any single nodes not connected by edges in the NetworkX graph) (default: True)
      • [column] : Column from Peak Table to filter on (default: no filtering)
      • [threshold] : Value to filter on (default: no filtering)
      • [operator] : The comparison operator to use when filtering (default: '>')
      • [sign] : The sign of the similarity score to filter on ('pos', 'neg' or 'both') (default: 'pos')

    • [run] : Generates and displays the NetworkX graph.

• springNetwork: Interactive spring-embedded network which inherits data from the NetworkX graph.

  • init_parameters
    • [g] : NetworkX graph.
  • methods

    • [set_params] : Set parameters

      • [node_size_scale]: dictionary(Peak Table column name as index: dictionary('scale': ("linear", "reverse_linear", "log", "reverse_log", "square", "reverse_square", "area", "reverse_area", "volume", "reverse_volume") 'range': a number array of length 2 - minimum size to maximum size)) (default: sizes all nodes to 10 with no dropdown menu)
      • [html_file] : Name to save the HTML file as (default: 'springNetwork.html')
      • [backgroundColor] : Set the background colour of the plot (default: 'white')
      • [foregroundColor] : Set the foreground colour of the plot (default: 'black')
      • [chargeStrength] : The charge strength of the spring-embedded network (force between springs) (default: -120)
      • [node_text_size] : The text size for each node (default: 15)
      • [fix_nodes] : Setting to 'True' will fix nodes in place when manually moved (default: False)
      • [displayLabel] : Setting to 'True' will set the node labels to the 'Label' column, otherwise it will set the labels to the 'Name' column from the Peak Table (default: False)
      • [node_data] : Peak Table column names to include in the mouse over information (default: 'Name' and 'Label')
      • [link_type] : The link type used in building the network (default: 'score')
      • [link_width] : The width of the links (default: 0.5)
      • [pos_score_color] : Colour value for positive similarity scores. Can be HTML/CSS name, hex code, and (R,G,B) tuples (default: 'red')
      • [neg_score_color] : Colour value for negative similarity scores. Can be HTML/CSS name, hex code, and (R,G,B) tuples (default: 'black')

    • [run] : Generates the JavaScript embedded HTML code and writes to a HTML file

• clustermap: Hierarchical Clustered Heatmap.

  • init_parameters
    • [similarities] : Pandas dataframe containing similarity scores.
      • [row_linkage] : Precomputed linkage matrix for the rows from a linkage clustered similarities matrix
      • [col_linkage] : Precomputed linkage matrix for the columns from a linkage clustered similarities matrix
  • methods

    • [set_params] : Set parameters

      • [xLabels] : A Pandas Series for labelling the X axis of the HCH
      • [yLabels] : A Pandas Series for labelling the Y axis of the HCH
      • [imageFileName] : The image file name to save to (default: 'clusterMap.png')
      • [saveImage] : Setting to 'True' will save the image to file (default: True)
      • [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)
      • [figSize] : The figure size as a tuple (width,height) (default: (80,70))
      • [dendrogram_ratio_shift] : The ratio to shift the position of the dendrogram in relation to the heatmap (default: 0.0)
      • [fontSize] : The font size set for each node (default: 30)
      • [heatmap_cmap] : The CMAP colour palette to use for the heatmap (default: 'RdYlGn')
      • [cluster_cmap] : The CMAP colour palette to use for the branch seperation of clusters in the dendrogram (default: 'Set1')
      • [rowColorCluster] : Setting to 'True' will display a colour bar for the clustered rows (default: False)
      • [colColorCluster] : Setting to 'True' will display a colour bar for the clustered columns (default: False)
      • [row_color_threshold] : The colouring threshold for the row dendrogram (default: 10)
      • [col_color_threshold] : The colouring threshold for the column dendrogram (default: 10)

    • [run] : : Generates and displays the Hierarchical Clustered Heatmap (HCH).

• polarDendrogram: Polar dendrogram

  • init_parameters
    • [dn] : Dendrogram dictionary labelled by Peak Table index
  • methods

    • set_params : Set parameters

      • [imageFileName] : The image file name to save to (default: 'polarDendrogram.png')
      • [saveImage] : Setting to 'True' will save the image to file (default: True)
      • [branch_scale] : The branch distance scale to apply ('linear', 'log', 'square') (default: 'linear')
      • [gap] : The gap size within the polar dendrogram (default: 0.1)
      • [grid] : Setting to 'True' will overlay a grid over the polar dendrogram (default: False)
      • [style_sheet] : Setting the Seaborn style-sheet (see https://python-graph-gallery.com/104-seaborn-themes/) (default: 'seaborn-white')
      • [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)
      • [figSize] : The figure size as a tuple (width,height) (default: (10,10))
      • [fontSize] : The font size for all text (default: 15)
      • [PeakTable] : The Peak Table Pandas dataframe (default: empty dataframe)
      • [Color_column] : The colour column to use from Peak Table (Can be colour or numerical values such as 'pvalue') (default: 'black')
      • [Label_column] : The label column to use from Peak Table (default: use original Peak Table index from cartesian dendrogram)
      • [text_cmap] : The CMAP colour palette to use (default: 'brg')

    • [getClusterPlots] : Generates plots of mean peak area over the 'Class' variable for each cluster from the polar dendrogram

      • [column_numbers] : The number of columns to display in the plots (default: 2)
      • [log] : Setting to 'True' will log the data (default: True)
      • [autoscale] : Setting to 'True' will scale the data to unit variance (Default: True)
      • [figSize] : The figure size as a tuple (width,height) (default: (10,20))
      • [saveImage] : Setting to 'True' will save the image to file (default: True)
      • [imageFileName] : The image file name to save to (default: 'clusterPlots.png')
      • [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)

    • [run] : Generates and displays the Polar dendrogram.

• pca: Principle Component Analysis (PCA) plot

  • parameters
    • [data] : array-like matrix, shape (n_samples, n_features)
    • [imageFileName] : The image file name to save to (default: 'PCA.png')
    • [saveImage] : Setting to 'True' will save the image to file (default: True)
    • [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)
    • [pcx] : The first component (default: 1)
    • [pcy] : The second component (default: 2)
    • [group_label] : Labels to assign to each group/class in the PCA plot (default: None)
    • [sample_label] : Labels to assign to each sample in the PCA plot (default: None)
    • [peak_label] : Labels to assign to each peak in the loadings biplot (default: None)
    • [markerSize] : The size of each marker (default: 100)
    • [fontSize] : The font size set for each node (default: 12)
    • [figSize] : The figure size as a tuple (width,height) (default: (20,10))

• pcoa: Principle Coordinates Analysis (PCoA) plot

  • parameters
    • [similarities] : array-like matrix, shape (n_samples, n_features)
    • [imageFileName] : The image file name to save to (default: 'PCOA.png')
    • [saveImage] : Setting to 'True' will save the image to file (default: True)
    • [dpi] : The number of Dots Per Inch (DPI) for the image (default: 200)
    • [n_components] : Number of components (default: 2)
    • [max_iter] : Maximum number of iterations of the SMACOF algorithm (default: 300)
    • [eps] : Relative tolerance with respect to stress at which to declare convergence (default: 1e-3)
    • [seed] : Seed number used by the random number generator for the RandomState instance (default: 3)
    • [group_label] : Labels to assign to each group/class (default: None)
    • [peak_label] : Labels to assign to each peak (default: None)
    • [markerSize] : The size of each marker (default: 100)
    • [fontSize] : The font size set for each node (default: 12)
    • [figSize] : The figure size as a tuple (width,height) (default: (20,10))

multivis.utils

• loadData: Loads and validates the Data and Peak sheet from an excel file.

  • parameters
    • [filename] : The name of the excel file (.xlsx file) e.g. 'Data.xlsx'.
    • [DataSheet] : The name of the data sheet in the file e.g. 'Data'. The data sheet must contain an 'Idx', 'SampleID', and 'Class' column.
    • [PeakSheet] : The name of the peak sheet in the file e.g. 'Peak'. The peak sheet must contain an 'Idx', 'Name', and 'Label' column.
  • Returns
    • DataTable: Pandas dataFrame
    • PeakTable: Pandas dataFrame

• mergeBlocks: Merges multiply different Data Tables and Peak Tables into a single Peak Table and Data Table (used for multi-block/multi-omics data preparation). The 'Name' column needs to be unique across all blocks. Automatically annotates the merged Peak Table with a 'Block' column using the dictionary keys.

  • parameters
    • [peak_blocks] : A dictionary of Pandas Peak Table dataframes from different datasets indexed by dataset type.
    • [data_blocks] : A dictionary of Pandas Data Table dataframes from different datasets indexed by dataset type.
  • Returns
    • [DataTable] : Merged Pandas dataFrame
    • [PeakTable] : Merged Pandas dataFrame

• range_scale: Scales a range of values in a numpy array between a minimum and maximum value.

  • parameters
    • [data] : A 1D numpy array of values
    • [newMin] : The minimum value to scale the numpy array to
    • [newMax] : The maximum value to scale the number array to
  • Returns
    • [scaled_data] : A scaled numpy array

• corrAnalysis: Correlation analysis with Pearson, Spearman or Kendall's Tau.

  • parameters
    • [df_data] : A Pandas dataframe matrix of values
    • [correlationType] : The correlation type to apply. Either 'Pearson', 'Spearman' or 'KendallTau'
  • Returns
    • [df_corr] : Pandas dataframe matrix of all correlation coefficients
    • [df_pval] : Pandas dataframe matrix of all correlation pvalues

• cluster: Clusters data using a linkage cluster method. If the data is correlated the correlations are first preprocessed, then clustered, otherwise a distance metric is applied to non-correlated data before clustering.

  • parameters
    • [matrix] : A Pandas dataframe matrix of values (may or may not be a matrix of correlation coefficients)
    • [transpose_non_correlated] : Setting to 'True' will transpose the matrix if it is not correlated data
    • [is_correlated] : Setting to 'True' will treat the matrix as if it contains correlation coefficients
    • [distance_metric] : Set the distance metric. Used if the matrix does not contain correlation coefficients.
    • [linkage_method] : Set the linkage method for the clustering.
  • Returns
    • [matrix] : The original matrix, transposed if transpose_non_correlated is 'True' and is_correlated is 'False'.
    • [row_linkage] : linkage matrix for the rows from a linkage clustered similarities matrix
    • [col_linkage] : linkage matrix for the columns from a linkage clustered similarities matrix

License

Multivis is licensed under the MIT license.

Authors

• Brett Chapman
• https://scholar.google.com.au/citations?user=A_wYNAQAAAAJ&hl=en

Correspondence

Dr. Brett Chapman, Post-doctoral Research Fellow at the Centre for Integrative Metabolomics & Computational Biology at Edith Cowan University. E-mail: [e-mail], [e-mail]

Citation

If you would cite multivis in a scientific publication, you can use the following: [currently pending publication submission