# Getting started with PathwaySpace projection methods

#### Sysbiolab Team

#### 2026-05-23

Package: PathwaySpace 1.3.0

# Highlights

- Produces landscape images representing graphs by geodesic paths
- Projects signals using a decay function to model signal attenuation
- Applies a convolution algorithm to combine signals from neighboring vertices

# Overview

For a given igraph object containing vertices, edges, and a signal associated with the vertices, PathwaySpace performs a convolution operation, which involves a weighted combination of neighboring signals on a graph. Figure 1A illustrates the convolution operation problem. Each vertex’s signal is positioned on a grid at specific `x` and `y` coordinates, represented by cones (for available signals) or question marks (for null or missing values).

Figure 1. Signal processing addressed by the PathwaySpace package. A) Graph overlaid on a 2D coordinate system. Each projection cone represents the signal associated with a graph vertex (referred to as vertex-signal positions), while question marks indicate positions with no signal information (referred to as null-signal positions). Inset: Graph layout of the toy example used in this vignette. B) Illustration of signal projection from two neighboring vertices, simplified to one dimension. Right: Signal profiles from aggregation and decay functions.

PathwaySpace model considers the vertex-signal positions as source points (or transmitters) and the null-signal positions as end points (or receivers). The signal values from vertex-signal positions are then projected to the null-signal positions according to a decay function, which will control how the signal values attenuate as they propagate across the 2D space. For a given null-signal position, the k-top signals are used to define the contributing vertices for the convolution operation, which will aggregate the signals from these contributing vertices considering their intensities reaching the end points. Users can adjust both the aggregation and decay functions; the aggregation function can be any arithmetic rule that reduces a numeric vector into a single scalar value (e.g., mean, weighted mean), while available decay functions include linear, exponential, and Weibull models (Fig.1B). Additionally, users can assign vertex-specific decay functions to model signal projections for subsets of vertices that may exhibit distinct behaviors. The resulting image forms geodesic paths in which the signal has been projected from vertex- to null-signal positions, using a density metric to measure the signal intensity along these paths.

# Setting basic input data

    #--- Load required packages for this section
    library(igraph)
    library(ggplot2)
    library(RGraphSpace)
    library(PathwaySpace)

This section will create an igraph object containing a binary signal associated to each vertex. The graph layout is configured manually to ensure that users can easily view all the relevant arguments needed to prepare the input data for the PathwaySpace package. The igraph’s `make_star()` function creates a star-like graph and the `V()` function is used to set attributes for the vertices. The PathwaySpace package will require that all vertices have `x`, `y`, and `name` attributes.

    # Make a 'toy' igraph object, either a directed or undirected graph
    gtoy1 <- make_star(5, mode="undirected")

    # Assign 'x' and 'y' coordinates to each vertex
    # ..this can be an arbitrary unit in (-Inf, +Inf)
    V(gtoy1)$x <- c(0, 2, -2, -4, -8)
    V(gtoy1)$y <- c(0, 0,  2, -4,  0)

    # Assign a 'name' to each vertex (here, from n1 to n5)
    V(gtoy1)$name <- paste0("n", 1:5)

# Checking graph validity

Next, we will create a GraphSpace-class object using the `GraphSpace()` constructor, followed by a call to `normalizeGraphSpace()`. The constructor ensures the validity of the input igraph object, while `normalizeGraphSpace()` scales the network coordinates. In this example, we set `mar = 0.2` to define the graph’s outer margins.

    # Check graph validity
    gs1 <- GraphSpace(gtoy1)
    # Normalize node coordinates
    gs1 <- normalizeGraphSpace(gs1, mar = 0.2)

Our graph is now ready for the PathwaySpace package. We can check its layout using the `plotGraphSpace()` function.

    # Check the graph layout
    plotGraphSpace(gs1, add.labels = TRUE)

# Creating a PathwaySpace object

Next, we will create a PathwaySpace-class object using the `buildPathwaySpace()` constructor. This will calculate pairwise distances between vertices, subsequently required by the signal projection methods.

    # Run the PathwaySpace constructor
    p_space1 <- buildPathwaySpace(gs1)

As a default behavior, the `buildPathwaySpace()` constructor initializes the signal of each vertex as `0`. We can use the `vertexSignal()` accessor to get and set vertex signals in a PathwaySpace object; for example, in order to get vertex names and signal values:

    # Check the number of vertices in the PathwaySpace object
    length(p_space1)
    ## [1] 5

    # Check vertex names
    names(p_space1)
    ## [1] "n1" "n2" "n3" "n4" "n5"

    # Check signal (initialized with '0')
    vertexSignal(p_space1)
    ## n1 n2 n3 n4 n5
    ##  0  0  0  0  0

…and for setting new signal values in PathwaySpace objects:

    # Set new signal to all vertices
    vertexSignal(p_space1) <- c(1, 4, 2, 4, 3)

    # Set a new signal to the 1st vertex
    vertexSignal(p_space1)[1] <- 2

    # Set a new signal to vertex "n1"
    vertexSignal(p_space1)["n1"] <- 6

    # Check updated signal values
    vertexSignal(p_space1)
    ## n1 n2 n3 n4 n5
    ##  6  4  2  4  3

# Signal projection

## Circular projection

Following that, we will use the `circularProjection()` function to project the network signals by the `weibullDecay()` function with `pdist = 0.4`, which is passed by the `decay.fun` argument. This term determines a distance unit for the signal convolution, affecting the extent over which the convolution operation projects the signal. For example, when `pdist = 1`, it will represent the diameter of the inscribed circle within the coordinate space. We also set `k = 1`, which defines the contributing vertices for signal convolution.

    # Run signal projection
    p_space1 <- circularProjection(p_space1, k = 1,
      decay.fun = weibullDecay(pdist = 0.4))

    # Plot a PathwaySpace image
    plotPathwaySpace(p_space1, add.marks = TRUE)

Next, we reassess the same PathwaySpace object, using `pdist = 0.2`, `k = 2` and adjusting the `shape` of the decay function (for further details, see the online tutorials).

    # Re-run signal projection, adjusting Weibull's shape
    p_space1 <- circularProjection(p_space1, k = 2,
      decay.fun = weibullDecay(shape = 2, pdist = 0.2))

    # Plot PathwaySpace
    plotPathwaySpace(p_space1, marks = "n1", theme = "th2")

The `shape` parameter allows a projection to take a variety of shapes. When `shape = 1` the projection follows an exponential decay, and when `shape > 1` the projection is first convex, then concave with an inflection point along the decay path. For additional examples see the modeling signal decay tutorial.

## Polar projection

In this section we will project network signals using a polar coordinate system. This representation may be useful for certain types of data, for example, to highlight patterns of signal propagation on directed graphs, especially to explore the orientation aspect of signal flow. To demonstrate this feature we will used the `gtoy2` directed graph, available in the RGraphSpace package.

    # Load a pre-processed directed igraph object
    data("gtoy2", package = "RGraphSpace")
    # Check graph validity
    gs2 <- GraphSpace(gtoy2)
    gs2 <- normalizeGraphSpace(gs2, mar = 0.2)

    # Check the graph layout
    plotGraphSpace(gs2, add.labels = TRUE)

    # Build a PathwaySpace for the 'gs2'
    p_space2 <- buildPathwaySpace(gs2)

    # Set '1s' as vertex signal
    vertexSignal(p_space2) <- 1

For fine-grained modeling of signal decay, the `vertexDecay()` accessor allows assigning decay functions at the level of individual vertices. For example, adjusting Weibull’s `shape` argument for node `n6`:

    # Modify decay function
    # ..for all vertices
    vertexDecay(p_space2) <- weibullDecay(shape=2, pdist = 1)
    # ..for individual vertices
    vertexDecay(p_space2)[["n6"]] <- weibullDecay(shape=3, pdist = 1)

In polar projections, the `pdist` term defines a reference distance related to edge length, aiming to constrain signal projections within edge bounds. Here we set `pdist = 1` to reach full edge lengths. Next, we run the signal projection using polar coordinates. The `beta` exponent will control the angular span; for values greater than zero, `beta` will progressively narrow the projection along the edge axis.

    # Run signal projection using polar coordinates
    p_space2 <- polarProjection(p_space2, beta = 10)

    # Plot PathwaySpace
    plotPathwaySpace(p_space2, theme = "th2", add.marks = TRUE)

Note that this projection distributes signals on the edges regardless of direction. To incorporate edge orientation, we set `directional = TRUE`, which channels the projection along the paths:

    # Re-run signal projection using 'directional = TRUE'
    p_space2 <- polarProjection(p_space2,
      beta = 10, directional = TRUE)

    # Plot PathwaySpace
    plotPathwaySpace(p_space2, theme = "th2",
      marks = c("n1","n3","n4","n5"))

This PathwaySpace polar projection emphasizes the signal flow along the directional pattern of a directed graph (see the igraph plot above). When interpreting, users should note that this approach introduces simplifications; for example, depending on the network topology, the polar projection may fail to capture complex features of directed graphs, such as cyclic dependencies, feedforward and feedback loops, or other intricate interactions.

# Signal types

The PathwaySpace accepts binary, integer, and numeric signal types, including `NAs`. If a vertex signal is assigned with `NA`, it will be ignored by the convolution algorithm. Logical values are also allowed, but it will be treated as binary. Next, we show the projection of a signal that includes negative values, using the `p_space1` object created previously.

    # Set a negative signal to vertices "n3" and "n4"
    vertexSignal(p_space1)[c("n3","n4")] <- c(-2, -4)

    # Check updated signal vector
    vertexSignal(p_space1)
    # n1 n2 n3 n4 n5
    #  6  4 -2 -4  3

    # Re-run signal projection
    p_space1 <- circularProjection(p_space1,
      decay.fun = weibullDecay(shape = 2))

    # Plot PathwaySpace
    plotPathwaySpace(p_space1, bg.color = "white",
      font.color = "grey20", add.marks = TRUE,
      mark.color = "magenta", theme = "th3")

Note that the original signal vector was rescale to `[-1, +1]`. If the signal vector is `>=0`, then it will be rescaled to `[0, 1]`; if the signal vector is `<=0`, it will be rescaled to `[-1, 0]`; and if the signal vector is in `(-Inf, +Inf)`, then it will be rescaled to `[-1, +1]`. To override this signal processing, simply set `rescale = FALSE` in the projection function.

# Online tutorials

https://sysbiolab.github.io/PathwaySpace/

# Citation

If you use PathwaySpace, please cite:

- Tercan & Apolonio et al. Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods. STAR Protocols 6(2):103681, 2025. https://doi.org/10.1016/j.xpro.2025.103681

- Ellrott et al. Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets. Cancer Cell 43(2):195-212.e11, 2025. https://doi.org/10.1016/j.ccell.2024.12.002

# Other useful links

- RGraphSpace: A Lightweight Interface Between ‘ggplot2’ and ‘igraph’ Objects https://cran.r-project.org/package=RGraphSpace

- RedeR: Interactive visualization and manipulation of nested networks https://bioconductor.org/packages/RedeR/

# Session information

    ## R version 4.6.0 (2026-04-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
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    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
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    ## other attached packages:
    ## [1] PathwaySpace_1.3.0 RGraphSpace_1.3.0  ggplot2_4.0.3      igraph_2.3.1
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