Body: <p>I'm Sebastian, working on my MSc thesis, aiming to create a knowledge graph of the wastewater treatment microbiome. We're using <a href="https://github.com/stefpeschel/NetCoMi" rel="nofollow noreferrer">Netcomi</a> for constructing Microbial Association Networks (MANs) from <a href="https://www.ebi.ac.uk/metagenomics" rel="nofollow noreferrer">MGnify database</a> metagenomics and metatranscriptomics data. Specifically, we are using the <a href="https://academic.oup.com/bioinformatics/article/31/19/3172/211784?login=true" rel="nofollow noreferrer">CCLasso</a> method. However, we are facing these challenges and have some questions:</p>
  <ul>
  <li><strong>Insufficient Samples</strong>: Many MGnify studies lack enough samples for MANs inference, leading us to combine data from various studies. Is combining samples from different studies a viable strategy for MANs inference in this context?</li>
  <li><strong>Data Heterogeneity:</strong> The data exhibits variability in sequencing technologies (e.g., Illumina Hiseq, Nextseq, Nanopore, etc), experiment types (metagenomics, assembly, or metatranscriptomics), sampling countries, and other variables. How best can we account for the data's heterogeneity in our analyses using Netcomi?</li>
  </ul>
  <p>Your expertise and advice would be incredibly valuable. Thanks for your time and assistance.</p>
  <p>Sebastian</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-15T12:11:07.137Z
Comments.Text: Can you give more details on what you are looking at in MGnify? I've not used it, I use SRS directly, so why not just download the FASTQ files and process them? Also what specific question are you seeking to address, i.e. whats your hypothesis? Basically something isn't right when you say "combined data" (it's a bad idea). What do you mean by that exactly? What are you combining? To address the downvote, the question is badly explained, but it's a valid investigation and I can explain the methodology and the rationale in the answer.
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-15T19:53:58.080Z
Comments.Text: Just to reiterate, I can probably answer this, but I'd need to know more detail and will be free to respond last thing Monday/early Tuesday.
Comments.UserDisplayName: M__
Tags: <networks><microbial-genomics>
Title: Looking for guidance on Microbial Association Networks (MANs) Inference with samples from different metagenomics/metatranscriptomics studies
ThreadText: # Looking for guidance on Microbial Association Networks (MANs) Inference with samples from different metagenomics/metatranscriptomics studies
  
  (Asked by: username_0 on 2024-03-14)
  
  I'm username_0, working on my MSc thesis, aiming to create a knowledge graph of the wastewater treatment microbiome. We're using [Netcomi](https://github.com/stefpeschel/NetCoMi) for constructing Microbial Association Networks (MANs) from [MGnify database](https://www.ebi.ac.uk/metagenomics) metagenomics and metatranscriptomics data. Specifically, we are using the [CCLasso](https://academic.oup.com/bioinformatics/article/31/19/3172/211784?login=true) method. However, we are facing these challenges and have some questions:
  
  * **Insufficient Samples**: Many MGnify studies lack enough samples for MANs inference, leading us to combine data from various studies. Is combining samples from different studies a viable strategy for MANs inference in this context?
  * **Data Heterogeneity:** The data exhibits variability in sequencing technologies (e.g., Illumina Hiseq, Nextseq, Nanopore, etc), experiment types (metagenomics, assembly, or metatranscriptomics), sampling countries, and other variables. How best can we account for the data's heterogeneity in our analyses using Netcomi?
  
  Your expertise and advice would be incredibly valuable. Thanks for your time and assistance.
  
  username_0
  
  **Tags:** networks, microbial-genomics
  
  > **Comments:**
  > **username_1**: Can you give more details on what you are looking at in MGnify? I've not used it, I use SRS directly, so why not just download the FASTQ files and process them? Also what specific question are you seeking to address, i.e. whats your hypothesis? Basically something isn't right when you say "combined data" (it's a bad idea). What do you mean by that exactly? What are you combining? To address the downvote, the question is badly explained, but it's a valid investigation and I can explain the methodology and the rationale in the answer.  
  > **username_1**: Just to reiterate, I can probably answer this, but I'd need to know more detail and will be free to respond last thing Monday/early Tuesday.  
  
  ---
  
  ## 0 answers
  
  