Diffusion¶

The methods in this modules manage the treatment of the different score diffusion methods applied to/from a path set of labels/scores of/on a certain network (as a graph format or a graph kernel matrix stemming from a graph).

Diffusion methods procedures provided in this package differ on: (a) How to distinguish positives, negatives and unlabelled examples. (b) Their statistical normalisation.

Input scores can be specified in three formats: 1. A named numeric vector, whereas if several of these vectors that share the node names need to be smoothed. 2. A column-wise matrix. However, if the unlabelled entities are not the same from one case to another. 2. A named list of such score matrices can be passed to this function. The path format will be kept in the output.

If the path labels are not quantitative, i.e. positive(1), negative(0) and possibly unlabelled, all the scores raw, gm, ml, z, mc, ber_s, ber_p can be used.

Methods¶

The provided methods can be elected for the diffusion computation through the paramter method.

from diffupy.diffuse import run_diffusion

diffusion_scores = run_diffusion(input_scores, network, method = 'raw')

Methods without statistical normalisation¶

raw: positive nodes introduce unitary flow {y_raw[i] = 1} to the network, whereas either negative and unlabelled nodes introduce null diffusion {y_raw[j] = 0}. 1. They are computed as: f_{raw} = K · y_{raw}. Where K is a graph kernel, see kernels. These scores treat negative and unlabelled nodes equivalently.
ml: Same as raw, but negative nodes introduce a negative unit of flow. Therefore not equivalent to unlabelled nodes 2.
gl: Same as ml, but the unlabelled nodes are assigned a (generally non-null) bias term based on the total number of positives, negatives and unlabelled nodes 3.
ber_s: A quantification of the relative change in the node score before and after the network smoothing. The score for a particular node i can be written as f_{ber_s}[i] = f_{raw}[i] / (y_{raw}[i] + eps). Where eps is a parameter controlling the importance of the relative change.

Methods with statistical normalisation¶

z: a parametric alternative to the raw score of node is subtracted its mean value and divided by its standard deviation. Differential trait of this package. The statistical moments have a closed analytical form and are inspired in 4.
mc: the score of node code {i} is based on its empirical p-value, computed by permuting the path {n.perm} times. It is roughly the proportion of path permutations that led to a diffusion score as high or higher than the original diffusion score.
ber_p: used in 5, this score combines raw and mc, in order to take into account both the magnitude of the {raw} scores and the effect of the network topology: this is a quantification of the relative change in the node score before and after the network smoothing.

Summary tables¶

Methods without statistical normalization¶

Scores	y+	y-	yn	Normalized	Stochastic	Quantitative	Reference
raw	1	0	0	No	No.	Yes	1
ml	1	-1	0	No	No	No	6
gm	1	-1	k	No	No	No	3
ber_s	1	0	0	No	No	Yes	5

Methods with statistical normalization¶

Scores	y+	Normalized	Stochastic	Quantitative	Reference
ber_p	1	Yes	Yes	Yes	5
mc	1	Yes	Yes	Yes	5
z	1	Yes	No	Yes	4

Method modularity¶

Through the parameter method can also be provided a callable function as a custom method.

from diffupy.diffuse import run_diffusion
from networkx import pagerank

diffusion_scores = run_diffusion(input_scores, network, method = pagerank)

References¶

1(1,2): Vandin, F., et al. (2010). Algorithms for detecting significantly mutated pathways in cancer. Lecture Notes in Computer Science. 6044, 506–521.
2: Zoidi, O., et al. (2015). Graph-based label propagation in digital media: A review. ACM Computing Surveys (CSUR), 47(3), 1-35.
3(1,2): Mostafavi, S., et al. (2008). Genemania: a real-time multiple association network integration algorithm for predicting gene function.Genome Biology. (9), S4.
4(1,2): Harchaoui, Z., et al. (2013). Kernel-based methods for hypothesis testing: a unified view. IEEE Signal Processing Magazine. (30), 87–97.
5(1,2,3,4): Bersanelli, M. et al. (2016). Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules. Scientific Reports. (6), 34841.
6: Tsuda, K., et al. (