I think the number of species is a significant factor when using different network inference methods. Also, is LASSO a good method especially for microbial community data?
I do not know much about this field but the authors of the tools listed below claim their superiority over the original LASSO:
CCLasso: https://github.com/huayingfang/CCLasso
MInt: https://cran.r-project.org/web/packages/MInt/index.html
SparCC: http://psbweb05.psb.ugent.be/conet/microbialnetworks/sparcc.php
This is a pretty complex issue, I don't think anyone really has the answer yet. The linked publication by Weiss and colleagues does a very thorough review of the behaviour of different methods and provide a nice flowchart (Figure 7, I think) on how to pick the best approach for your data.
SparCC was one of the programs tested and it came out very well.
Article Correlation detection strategies in microbial data sets vary...
The evaluation cited above does not include more recent tools, such as SPIEC-EASI or MInt. Also, it evaluated tools only on simulated data, not on biological data (i.e. on sequencing data from communities with known ecological interactions). To my knowledge, we still lack a comparative biological evaluation of microbial network inference tools. So I agree, it is too early to conclude. If you're interested, here is a list of network inference tools I encountered:
http://psbweb05.psb.ugent.be/conet/references.html#tools
I know about different methods used for ecological communities, that may be applied also to microbial communities, here are the main references:
http://onlinelibrary.wiley.com/doi/10.1002/ecy.1605/full
http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12363/abstract
http://www.pnas.org/content/106/33/13854.abstract
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