diff --git a/README.md b/README.md index 8a2739c..a64473a 100644 --- a/README.md +++ b/README.md @@ -37,9 +37,9 @@ which can be used to estimate microbe-metabolite conditional probabilities that ``` mmvec paired-omics \ - --microbe-file examples/cf/otus_nt.biom \ - --metabolite-file examples/cf/lcms_nt.biom \ - --summary-dir summary + --microbe-file examples/cf/otus_nt.biom \ + --metabolite-file examples/cf/lcms_nt.biom \ + --summary-dir summary ``` While this is running, you can open up another session and run `tensorboard --logdir .` for diagnosis, see FAQs below for more details. @@ -67,24 +67,24 @@ the qiime2 plugin, run the following commands to import an example dataset ``` qiime tools import \ - --input-path data/otus_nt.biom \ - --output-path otus_nt.qza \ - --type FeatureTable[Frequency] + --input-path data/otus_nt.biom \ + --output-path otus_nt.qza \ + --type FeatureTable[Frequency] qiime tools import \ - --input-path data/lcms_nt.biom \ - --output-path lcms_nt.qza \ - --type FeatureTable[Frequency] + --input-path data/lcms_nt.biom \ + --output-path lcms_nt.qza \ + --type FeatureTable[Frequency] ``` Then you can run mmvec ``` qiime mmvec paired-omics \ - --i-microbes otus_nt.qza \ - --i-metabolites lcms_nt.qza \ - --p-learning-rate 1e-3 \ - --o-conditionals ranks.qza \ - --o-conditional-biplot biplot.qza + --i-microbes otus_nt.qza \ + --i-metabolites lcms_nt.qza \ + --p-learning-rate 1e-3 \ + --o-conditionals ranks.qza \ + --o-conditional-biplot biplot.qza ``` In the results, there are two files, namely `results/conditional_biplot.qza` and `results/conditionals.qza`. The conditional biplot is a biplot representation the @@ -98,8 +98,8 @@ created as follows ``` qiime metadata tabulate \ - --m-input-file results/conditionals.qza \ - --o-visualization conditionals-viz.qzv + --m-input-file results/conditionals.qza \ + --o-visualization conditionals-viz.qzv ``` @@ -107,10 +107,10 @@ Then you can run the following to generate a emperor biplot. ``` qiime emperor biplot \ - --i-biplot conditional_biplot.qza \ - --m-sample-metadata-file data/metabolite-metadata.txt \ - --m-feature-metadata-file data/microbe-metadata.txt \ - --o-visualization emperor.qzv + --i-biplot conditional_biplot.qza \ + --m-sample-metadata-file data/metabolite-metadata.txt \ + --m-feature-metadata-file data/microbe-metadata.txt \ + --o-visualization emperor.qzv ``` @@ -293,6 +293,10 @@ qiime tools import --input-path conditionals.tsv --output-path ranks.qza --type qiime tools import --input-path ordination.txt --output-path biplot.qza --type "PCoAResults % Properties('biplot')" ``` +**Q** : Can MMvec handle small sample studies? + +**A** : We have ran MMvec with studies as few as 19 samples. However running MMvec in these small sample regimes requires careful tuning of `--latent-dimension` in addition to the `--input-prior` and `--output-prior` commands. The [desert biocrust experiment](https://github.com/biocore/mmvec/tree/master/examples/soils) maybe a good dataset to refer to when analyzing these sorts of datasets. + Credits to Lisa Marotz ([@lisa55asil](https://github.com/lisa55asil)), Yoshiki Vazquez-Baeza ([@ElDeveloper](https://github.com/ElDeveloper)), Julia Gauglitz ([@jgauglitz](https://github.com/jgauglitz)) and Nickolas Bokulich ([@nbokulich](https://github.com/nbokulich)) for their README contributions. # Citation diff --git a/mmvec/q2/_method.py b/mmvec/q2/_method.py index d9ec687..46236f3 100644 --- a/mmvec/q2/_method.py +++ b/mmvec/q2/_method.py @@ -50,6 +50,7 @@ def paired_omics(microbes: biom.Table, model = MMvec( latent_dim=latent_dim, u_scale=input_prior, v_scale=output_prior, + batch_size=batch_size, learning_rate=learning_rate) model(session, train_microbes_coo, train_metabolites_df.values, diff --git a/scripts/mmvec b/scripts/mmvec index 6e1ed2f..76a8c53 100644 --- a/scripts/mmvec +++ b/scripts/mmvec @@ -165,6 +165,7 @@ def paired_omics(microbe_file, metabolite_file, learning_rate = learning_rate, beta_1=beta1, beta_2=beta2, device_name=device_name, + batch_size=batch_size, clipnorm=clipnorm, save_path=sname) model(session,