diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index f806237d..9f1d70d9 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -16,7 +16,7 @@ jobs: strategy: matrix: os: ["ubuntu-latest", "windows-latest", "macos-latest"] - python-version: ["3.8", "3.11"] + python-version: ["3.9", "3.11"] runs-on: "${{matrix.os}}" steps: @@ -30,12 +30,6 @@ jobs: run: | python -m pip install --upgrade pip cryptography pip install wheel numpy Cython biom-format - - name: Install highspy via dev wheel - if: matrix.os == 'macos-latest' - run: | - PYTHON_VERSION=${{ matrix.python-version }} - PYTHON_VERSION=${PYTHON_VERSION//./} - pip install .github/wheels/highspy-1.5.3-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-macosx_10_9_x86_64.whl - name: Install MICOM run: pip install -e . - name: install CPLEX @@ -52,7 +46,7 @@ jobs: pip install pytest pytest-cov pytest --cov=micom --cov-report=xml - name: Upload to codecov - if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.9 + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11' uses: codecov/codecov-action@v4.3.0 release: diff --git a/docs/source/_static/association.html b/docs/source/_static/association.html new file mode 100644 index 00000000..0aa28576 --- /dev/null +++ b/docs/source/_static/association.html @@ -0,0 +1,223 @@ + + + + + + Associations + + + + + + + + + +
+
+

+ Association with the phenotype +

+
+ +
+

+ Each bar denotes the log fold-change of the metabolite across the phenotype + with a significant FDR-corrected test (q<0.5). Hover over the bars + to see the p values, q values and test statistics. The downloadable CSV includes all + tests (even non-significant ones). + All fluxes are log-transformed and standardized so statistics are roughly comparable. +

+
+ Download association test results as CSV... +
+

+
+

+ The plot on the left shows a particular metabolite production flux by phenotype for significant + interactions. Those show individual metabolite associations with the phenotype. Hover over + individual points to see the metabolite annotations.
+ The plot on the right shows the overall cross-validation performance of + a regularized linear regression in explaining the phenotype. This uses a LASSO + regression with the regularization parameter learned from a 2-fold cross-validation. + The predictions themselves are all based on leave-one-out cross-validation. It can be + used as an estimate of the global assocaition of the phenotype with the flux state. +

+
+ Download production fluxes as CSV... +
+
+
+

Model performance

+ +

training accuracy = 1.0

+

cross-validation accuracy = 0.8 ± 0.0

+ +
+ +
+ + \ No newline at end of file diff --git a/docs/source/_static/association.png b/docs/source/_static/association.png new file mode 100644 index 00000000..dc664a6b Binary files /dev/null and b/docs/source/_static/association.png differ diff --git a/docs/source/_static/consumption.html b/docs/source/_static/consumption.html index 116d2fe3..bb72d71c 100644 --- a/docs/source/_static/consumption.html +++ b/docs/source/_static/consumption.html @@ -6,11 +6,9 @@ Sample metabolite utilization/production - - - - - + + + - - -
-
-

- Metabolites associated with the phenotype -

-
- -
-

- Each bar denotes the coefficient of the metabolite flux in a linear predictor - of the phenotype. All fluxes are log-transformed and standardized so - coefficients are roughly comparable. -

-
- Download coefficients as CSV... -
-
-
-

- The plot on the left shows a particular metabolite flux by phenotype. - The plot on the right shows the overall cross-validation performance of - the model in explaining the phenotype. -

-
- Download fluxes as CSV... -
-
-
-

Model performance

- -

training accuracy = 1.0

-

cross-validation accuracy = 0.75 ± 0.4330127018922193

- -
- -
- - \ No newline at end of file diff --git a/docs/source/_static/fit.png b/docs/source/_static/fit.png deleted file mode 100644 index 5771576c..00000000 Binary files a/docs/source/_static/fit.png and /dev/null differ diff --git a/docs/source/_static/growth_rates.html b/docs/source/_static/growth_rates.html index 46557d56..43b074e2 100644 --- a/docs/source/_static/growth_rates.html +++ b/docs/source/_static/growth_rates.html @@ -6,11 +6,9 @@ Growth rates - - - - - + + +

- Metabolites associated with the phenotype + Association with the phenotype

- Each bar denotes the coefficient of the metabolite flux in a linear predictor - of the phenotype. All fluxes are log-transformed and standardized so - coefficients are roughly comparable. + Each bar denotes the {{statistic}} of the metabolite across the phenotype + with a significant FDR-corrected test (q<{{q_threshold}}). Hover over the bars + to see the p values, q values and test statistics. The downloadable CSV includes all + tests (even non-significant ones). + All fluxes are log-transformed and standardized so statistics are roughly comparable.

-
- Download coefficients as CSV... +
+ Download association test results as CSV...
-
+

- The plot on the left shows a particular metabolite flux by phenotype. + The plot on the left shows a particular metabolite {{direction}} flux by phenotype for significant + interactions. Those show individual metabolite associations with the phenotype. Hover over + individual points to see the metabolite annotations.
The plot on the right shows the overall cross-validation performance of - the model in explaining the phenotype. + a regularized linear regression in explaining the phenotype. This uses a LASSO + regression with the regularization parameter learned from a 2-fold cross-validation. + The predictions themselves are all based on leave-one-out cross-validation. It can be + used as an estimate of the global assocaition of the phenotype with the flux state.

- Download fluxes as CSV... + Download {{direction}} fluxes as CSV...

@@ -59,12 +68,12 @@

Model performance

- - - - + + +