multivis 0.1.7

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

User installation

The recommend way to install cimcb_vis 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.
    • [scores] : Pandas dataframe containing correlation coefficients.
    • [pvalues] : Pandas dataframe containing correlation pvalues.
  • methods
    • [set_params] : Set parameters - filter score type, hard threshold, internal correlation flag and sign type
    • [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 and edges, and a networkx graph.

  • init_parameters
    • [peaktable] : Pandas dataframe containing peak data.
    • [scores] : Pandas dataframe containing correlation coefficients.
    • [pvalues] : Pandas dataframe containing correlation pvalues.
  • methods
    • [set_params] : Set parameter - filter score type, hard threshold, internal correlation flag and sign type. - [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 - diameter, inner radius offset, group separation, link fade opacity, mouse over flag, font size, background colour, foreground colour, filter slider position offset, colour scale (value to colour edges by: 'Score' or 'Pvalue') and CMAP colour palette for edges.
    • [run] : Generates and outputs the hierarchical edge bundle.

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

  • init_parameters
    • [g] : NetworkX graph.
  • methods
    • [set_params] : Set parameters - node parameters dictionary(node sizing columnn, node size scale, node size range, alpha opacity value, node labelling flag, font size and keep single nodes flag) , filter parameters dictionary(filter column, filter threshold, filter operator and sign) , image filename, label edges flag, save image flag, NetworkX layout type, dpi, figure size.
    • [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 parameters dictionary(node text size, fix node when moved flag, display node label flag , node size scale dictionary(peak table columns as index: dictionary(index as "scale": values ("linear", "reverse_linear", "log", "reverse_log" , "square", "reverse_square", "area", "reverse_area", "volume", "reverse_volume") , index as "range": a number array of length 2))) , link parameters dictionary(link type used in building the network, link width , link score colour dictionary("positive": colour value, "negative": colour value)) # Colour values can be HTML/CSS name, hex code, and (R,G,B) tuples , background colour, foreground colour, canvas size, charge strength
    • [run] : Generates and returns JavaScript embedded HTML code for writing to HTML and displaying.

• clustermap: Hierarchical Clustered Heatmap.

  • init_parameters
    • [scores] : Pandas dataframe containing similarity scores (e.g. correlation coefficients or Euclidean distance values).
    • [row_linkage] : Precomputed linkage matrix for the rows from a linkage clustered scores matrix
    • [col_linkage] : Precomputed linkage matrix for the columns from a linkage clustered scores matrix
  • methods
    • [set_params] : Set parameters - image file name, save image flag, dpi, figure size, ratio to shift position of dendrograms, font size , heatmap parameters dictionary(X axis labels, Y axis labels, CMAP colour palette) , clustering parameters dictionary(CMAP colour palette, colour the row clusters flag, colour the column clusters flag, row colour clustering threshold, coloumn colour clustering threshold)
    • [run] : Generates and displays the Hierarchical Clustered Heatmap (HCH).

• polarDendrogram: Polar dendrogram

  • init_parameters
    • [dn] : Dendrogram dictionary.
  • methods
    • [set_params] : Set parameters - image file name, save image flag, dendrogram branch scale type ('linear', 'log', 'square'), gap value, display grid flag, style-sheet type, dpi, figure size , text parameters dictionary(font size, text colours dictionary(index from peak table: values as colours), text labels dictionary(index from peak table: values as labels from peak table))
    • [run] : Generates and displays the Polar dendrogram.

multivis.utils

TODO --add parameters and methods to below tools

• loadData: Loads and validates the Data and Peak sheet from an excel file.
• mergeBlocks: Merges multiply different data blocks into a single peak table and data table.
• range_scale: Scales a range of values between user chosen values.
• corrAnalysis: Correlation analysis with Pearson, Spearman or Kendall's Tau.
• 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.

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]