<｜begin▁of▁sentence｜>### Instruction:
In the realm of bioinformatics, we deal with complex networks of biological data that sometimes can be overwhelming in their entirety. Especially when we're trying to analyze the interactions within protein-protein interaction networks or the intricate web of gene regulatory networks, it's crucial to distill the essence of these structures to make sense of the underlying biological processes. For this, subgraph sampling comes in handy, as it allows us to focus on a manageable, representative part of the whole.

Now, let's dive into a practical scenario. Imagine we have mapped out a gene interaction network that's detailed to a fault for our current analysis. We'd like to zoom in on a specific part of this network to understand the diffusion of, say, a genetic signal or the spread of a regulatory effect within a subset of 10 genes.

To do this, we'd typically utilize a tool like the DiffusionSampler from the littleballoffur library, which is adept at simulating diffusion processes to extract such a subgraph. In this instance, our full network is stored as a sparse6 file named 'littleballoffur10.sparse6'.

What I need to do is to apply the DiffusionSampler to read from our specific 'littleballoffur10.sparse6' file. The goal is to generate a sampled subgraph that encompasses 10 nodes. This would effectively mimic a diffusion process in the gene network and help us focus our analysis on a portion that might be more insightful for our research question.

Could you guide me through employing the DiffusionSampler in littleballoffur to extract a 10-node subgraph from the 'littleballoffur10.sparse6' graph format, simulating a diffusion process for our bioinformatics analysis?
### Response:
import networkx as nx
from littleballoffur import DiffusionSampler
G = nx.read_sparse6('littleballoffur10.sparse6')
sampler = DiffusionSampler(number_of_nodes=10, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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