From a03fedcc222f2b43cc60bf1d0cfc3d873c20e8f0 Mon Sep 17 00:00:00 2001
From: BeGeiger <benedikt-geiger@gmx.de>
Date: Wed, 3 Jan 2024 06:57:39 -0500
Subject: [PATCH 1/5] update dependencies

---
 poetry.lock | 148 +++++++++++++++++++++++++++++-----------------------
 1 file changed, 83 insertions(+), 65 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 7214f96..a94baf8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -701,70 +701,88 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 
 [[package]]
 name = "pillow"
-version = "10.1.0"
+version = "10.2.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
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+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7c8f97e8e7a9009bcacbe3766a36175056c12f9a44e6e6f2d5caad06dcfbf03b"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a"},
+    {file = "pillow-10.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868"},
+    {file = "pillow-10.2.0.tar.gz", hash = "sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e"},
 ]
 
 [package.extras]
 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
@@ -867,13 +885,13 @@ testing = ["covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytes
 
 [[package]]
 name = "pytest"
-version = "7.4.3"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-7.4.3-py3-none-any.whl", hash = "sha256:0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac"},
-    {file = "pytest-7.4.3.tar.gz", hash = "sha256:d989d136982de4e3b29dabcc838ad581c64e8ed52c11fbe86ddebd9da0818cd5"},
+    {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
+    {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
 ]
 
 [package.dependencies]
@@ -1062,17 +1080,17 @@ test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeo
 
 [[package]]
 name = "seaborn"
-version = "0.13.0"
+version = "0.13.1"
 description = "Statistical data visualization"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "seaborn-0.13.0-py3-none-any.whl", hash = "sha256:70d740828c48de0f402bb17234e475eda687e3c65f4383ea25d0cc4728f7772e"},
-    {file = "seaborn-0.13.0.tar.gz", hash = "sha256:0e76abd2ec291c655b516703c6a022f0fd5afed26c8e714e8baef48150f73598"},
+    {file = "seaborn-0.13.1-py3-none-any.whl", hash = "sha256:6baa69b6d1169ae59037971491c450c0b73332b42bd4b23570b62a546bc61cb8"},
+    {file = "seaborn-0.13.1.tar.gz", hash = "sha256:bfad65e9c5989e5e1897e61bdbd2f22e62455940ca76fd49eca3ed69345b9179"},
 ]
 
 [package.dependencies]
-matplotlib = ">=3.3,<3.6.1 || >3.6.1"
+matplotlib = ">=3.4,<3.6.1 || >3.6.1"
 numpy = ">=1.20,<1.24.0 || >1.24.0"
 pandas = ">=1.2"
 
@@ -1083,13 +1101,13 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "setuptools"
-version = "69.0.2"
+version = "69.0.3"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-69.0.2-py3-none-any.whl", hash = "sha256:1e8fdff6797d3865f37397be788a4e3cba233608e9b509382a2777d25ebde7f2"},
-    {file = "setuptools-69.0.2.tar.gz", hash = "sha256:735896e78a4742605974de002ac60562d286fa8051a7e2299445e8e8fbb01aa6"},
+    {file = "setuptools-69.0.3-py3-none-any.whl", hash = "sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05"},
+    {file = "setuptools-69.0.3.tar.gz", hash = "sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78"},
 ]
 
 [package.extras]

From 7c5ac5db95eb8aa36b484259381482d0fdb67828 Mon Sep 17 00:00:00 2001
From: BeGeiger <benedikt-geiger@gmx.de>
Date: Wed, 3 Jan 2024 06:57:53 -0500
Subject: [PATCH 2/5] improve doc

---
 src/salamander/nmf_framework/corrnmf.py       | 36 ++++++++-----------
 src/salamander/nmf_framework/corrnmf_det.py   | 10 +++---
 src/salamander/nmf_framework/klnmf.py         |  8 +++--
 src/salamander/nmf_framework/nmf.py           | 21 ++++++-----
 src/salamander/nmf_framework/signature_nmf.py | 19 ++++++----
 5 files changed, 46 insertions(+), 48 deletions(-)

diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py
index 8c1aa0a..f0258f5 100644
--- a/src/salamander/nmf_framework/corrnmf.py
+++ b/src/salamander/nmf_framework/corrnmf.py
@@ -16,11 +16,10 @@
 class CorrNMF(SignatureNMF):
     r"""
     The abstract class CorrNMF unifies the structure of deterministic and
-    stochastic correlated NMF (CorrNMF) with and without given signatures.
-    Both variants of CorrNMF have an identical generative model and objective function.
-    The model parameters are the sample biases \alpha, variance \sigma^2,
-    signature matrix W and the auxiliary parameters p.
-    The latent variables are the signature embeddings L and the sample embeddings U.
+    stochastic algorithms to fit the parameters of correlated NMF (CorrNMF).
+
+    The model parameters are the signature and sample biases, the variance, and the
+    signature matrix. The latent variables are the signature and sample embeddings.
 
     Overview:
 
@@ -48,12 +47,10 @@ class CorrNMF(SignatureNMF):
             update a single sample embedding u
 
         - fit:
-            Run CorrNMF for a given mutation count data. Every
-            fit method should also implement a version that allows fixing
-            arbitrary many a priori known signatures.
+            Run CorrNMF for a given mutation count data.
 
 
-    The following attributes are implemented in the abstract class CNMF:
+    The following attributes are implemented in CorrNMF:
 
         - signatures: pd.DataFrame
             The signature matrix including mutation type names and signature names
@@ -73,10 +70,7 @@ class CorrNMF(SignatureNMF):
             The number of parameters fitted in CorrNMF
 
         - objective: str
-            "minimize" or "maximize". Whether the NMF algorithm maximizes
-            or minimizes the objective function. Some algorithms maximize a likelihood,
-            others minimize a distance. The distinction is useful for filtering NMF
-            runs based on the fitted objective function value.
+            "minimize" or "maximize". CorrNMF maximizes the objective function.
 
         - corr_signatures: pd.DataFrame
             The signature correlation matrix induced by the signature embeddings
@@ -85,7 +79,7 @@ class CorrNMF(SignatureNMF):
             The sample correlation matrix induced by the sample embeddings
 
 
-    The following methods are implemented in the abstract class CorrNMF:
+    The following methods are implemented in CorrNMF:
 
         - objective_function:
             The evidence lower bound (ELBO) of the log-likelihood.
@@ -103,12 +97,12 @@ class CorrNMF(SignatureNMF):
             Initialize all model parameters and latent variables depending on the
             initialization method chosen
 
-        - _get_embedding_annotations:
-            A helper function to concatenate signature and sample names
+        - _get_embedding_data:
+            A helper function for the embedding plot that returns the signature
+            and sample embeddings
 
-        - plot_embeddings:
-            Plot signature or sample embeddings in 2D using PCA, tSNE or UMAP.
-            The respective plotting functions are implemented in the plot.py module
+        - _get_default_embedding_annotations:
+            A helper function for the embedding plot that returns the signature names
 
     More specific docstrings are written for the respective attributes and methods.
     """
@@ -207,10 +201,8 @@ def _n_parameters(self):
         """
         There are n_features * n_signatures parameters corresponding to
         the signature matrix, each embedding corresponds to dim_embeddings parameters,
-        and each signature & sample has a bias.
+        and each signature & sample has a real valued bias.
         Finally, the model variance is a single positive real number.
-
-        Note: We do not include the number of auxiliary parameters p.
         """
         n_parameters_signatures = self.n_features * self.n_signatures
         n_parameters_embeddings = self.dim_embeddings * (
diff --git a/src/salamander/nmf_framework/corrnmf_det.py b/src/salamander/nmf_framework/corrnmf_det.py
index f45b185..2ad6a2b 100644
--- a/src/salamander/nmf_framework/corrnmf_det.py
+++ b/src/salamander/nmf_framework/corrnmf_det.py
@@ -20,17 +20,16 @@ class CorrNMFDet(CorrNMF):
     "Bayesian Nonnegative Matrix Factorization with Stochastic Variational
     Inference" by Paisley et al.
 
-    The following methods are implemented to match the structure
-    of the abstract class CorrNMF:
+    The following methods are implemented to match the structure of CorrNMF:
 
         - _update_alpha:
-            update the sample exposure biases \alpha
+            update the sample biases \alpha
 
         - _update_beta:
             update the signature biases \beta
 
         - _update_sigma_sq:
-            update the variance \sigma^2 assumed in the generative model
+            update the variance \sigma^2
 
         - _update_W:
             update the signature matrix W
@@ -47,8 +46,7 @@ class CorrNMFDet(CorrNMF):
     The following method is implemented to match the structure of SignatureNMF:
 
         - fit:
-            Perform CorrNMF for the given mutation count data or
-            for given signatures and mutation count data
+            Inference of the CorrNMF parameters for a given mutation count data
     """
 
     def _update_alpha(self, given_sample_biases=None):
diff --git a/src/salamander/nmf_framework/klnmf.py b/src/salamander/nmf_framework/klnmf.py
index 57eabc1..0ce4fc1 100644
--- a/src/salamander/nmf_framework/klnmf.py
+++ b/src/salamander/nmf_framework/klnmf.py
@@ -11,9 +11,11 @@
 class KLNMF(NMF):
     """
     Decompose a mutation count matrix X into the product of a signature
-    matrix W and an exposure matrix H by minimizing the generalized
-    Kullback-Leibler (KL) divergence under the constraint of having
-    normalized signatures.
+    matrix W and an exposure matrix H by minimizing the weighted
+    generalized Kullback-Leibler (KL) divergence under the constraint of
+    having normalized signatures.
+    The implementation supports a sparstiy-inducing l_half penalty of the
+    exposures.
 
     Parameters
     ----------
diff --git a/src/salamander/nmf_framework/nmf.py b/src/salamander/nmf_framework/nmf.py
index f5dc04d..b0c3d4a 100644
--- a/src/salamander/nmf_framework/nmf.py
+++ b/src/salamander/nmf_framework/nmf.py
@@ -69,7 +69,7 @@ class NMF(SignatureNMF):
             The exposure matrix including the signature names and sample names
 
         - _n_parameters:
-            The number of parameters fitted
+            The number of parameters of models with signature and exposure matrices
 
         - corr_signatures: pd.DataFrame
             The signature correlation matrix induced by their sample exposures
@@ -80,18 +80,17 @@ class NMF(SignatureNMF):
 
     The following methods are implemented in the abstract class NMF:
 
-        - initialize:
-            Initialize all model parameters and latent variables depending on the
-            initialization method chosen
+        - _initialize:
+            Initialize all model parameters
 
-        - _get_embedding_annotations:
-            A helper function to get the sample names for the embedding plots
+        - _get_embedding_data:
+            A helper function for the embedding plot. Models with signature and
+            exposure matrices use the exposures as the lower-dimensional
+            representation of the samples
 
-        - plot_embeddings:
-            Plot signature or sample embeddings in 2D using PCA, tSNE or UMAP.
-            The respective plotting functions are implemented in the plot.py module.
-
-    More details are explained in the respective attributes and methods.
+        - _get_default_embedding_annotations:
+            A helper function for the embedding plot. By default, no samples are
+            annotated
     """
 
     def __init__(
diff --git a/src/salamander/nmf_framework/signature_nmf.py b/src/salamander/nmf_framework/signature_nmf.py
index 9a96158..54e13c5 100644
--- a/src/salamander/nmf_framework/signature_nmf.py
+++ b/src/salamander/nmf_framework/signature_nmf.py
@@ -73,8 +73,11 @@ class SignatureNMF(ABC):
             fit method should also implement a version that allows fixing
             arbitrary many a priori known signatures.
 
-        - plot_embeddings:
-            Plot the sample (and potentially the signature) embeddings in 2D.
+        - _get_embedding_data:
+            A helper function for the embedding plot
+
+        - _get_default_embedding_annotations:
+            A helper function for the embedding plot
 
 
     The following attributes and methods are implemented in SignatureNMF:
@@ -90,9 +93,11 @@ class SignatureNMF(ABC):
         - bic: float
             The value of the Bayesian Information Criterion (BIC)
 
-        - _setup_parameters_fitting:
-            Perform parameter checks and add the input mutation counts matrix
-            as an attributes
+        - _setup_data_parameters:
+            Perform parameter checks on the input data and add attributes
+
+        - plot_history:
+            Plot the history of the objective function values after fitting the model
 
         - plot_signatures:
             Plot the signatures using the signatures_plot function implemented in
@@ -102,7 +107,9 @@ class SignatureNMF(ABC):
             Plot the correlation of either the signatures or exposures
             using the corr_plot function implemented in the plot module
 
-    More specific docstrings are written for the respective attributes and methods.
+        - plot_embeddings:
+            Plot the sample (and potentially the signature) embeddings in 2D
+            using PCA, tSNE or UMAP
     """
 
     def __init__(

From 194f47b7e694a443fd6f0c1c78d21f1064d2f850 Mon Sep 17 00:00:00 2001
From: BeGeiger <benedikt-geiger@gmx.de>
Date: Wed, 3 Jan 2024 06:58:38 -0500
Subject: [PATCH 3/5] improve order

---
 src/salamander/nmf_framework/corrnmf.py | 36 ++++++++++++-------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py
index f0258f5..5c698aa 100644
--- a/src/salamander/nmf_framework/corrnmf.py
+++ b/src/salamander/nmf_framework/corrnmf.py
@@ -196,24 +196,6 @@ def exposures(self) -> pd.DataFrame:
         )
         return exposures
 
-    @property
-    def _n_parameters(self):
-        """
-        There are n_features * n_signatures parameters corresponding to
-        the signature matrix, each embedding corresponds to dim_embeddings parameters,
-        and each signature & sample has a real valued bias.
-        Finally, the model variance is a single positive real number.
-        """
-        n_parameters_signatures = self.n_features * self.n_signatures
-        n_parameters_embeddings = self.dim_embeddings * (
-            self.n_signatures + self.n_samples
-        )
-        n_parameters_biases = self.n_samples + self.n_signatures
-        n_parameters_exposures = n_parameters_embeddings + n_parameters_biases
-        n_parameters = n_parameters_signatures + n_parameters_exposures + 1
-
-        return n_parameters
-
     @property
     def reconstruction_error(self):
         return kl_divergence(self.X, self.W, self.exposures.values)
@@ -282,6 +264,24 @@ def _surrogate_objective_function(self, penalize_sample_embeddings=True) -> floa
     def loglikelihood(self):
         return self.objective_function()
 
+    @property
+    def _n_parameters(self):
+        """
+        There are n_features * n_signatures parameters corresponding to
+        the signature matrix, each embedding corresponds to dim_embeddings parameters,
+        and each signature & sample has a real valued bias.
+        Finally, the model variance is a single positive real number.
+        """
+        n_parameters_signatures = self.n_features * self.n_signatures
+        n_parameters_embeddings = self.dim_embeddings * (
+            self.n_signatures + self.n_samples
+        )
+        n_parameters_biases = self.n_samples + self.n_signatures
+        n_parameters_exposures = n_parameters_embeddings + n_parameters_biases
+        n_parameters = n_parameters_signatures + n_parameters_exposures + 1
+
+        return n_parameters
+
     @abstractmethod
     def _update_alpha(self):
         pass

From 259d1b8a68c2648bf411150b577e08335d3b3063 Mon Sep 17 00:00:00 2001
From: BeGeiger <benedikt-geiger@gmx.de>
Date: Wed, 3 Jan 2024 11:04:03 -0500
Subject: [PATCH 4/5] fix given parameters

the model variance of CorrNMF models can now also be fixed during model training
---
 src/salamander/nmf_framework/corrnmf.py       | 23 +++++++++++---
 src/salamander/nmf_framework/corrnmf_det.py   | 18 +++++++----
 .../nmf_framework/multimodal_corrnmf.py       | 30 ++++++++++++-------
 tests/test_corrnmf.py                         |  5 ++++
 tests/test_multimodal_corrnmf.py              | 10 ++++++-
 5 files changed, 64 insertions(+), 22 deletions(-)

diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py
index 5c698aa..f399f84 100644
--- a/src/salamander/nmf_framework/corrnmf.py
+++ b/src/salamander/nmf_framework/corrnmf.py
@@ -355,6 +355,7 @@ def _check_given_parameters(
         given_signature_embeddings,
         given_sample_biases,
         given_sample_embeddings,
+        given_variance,
     ):
         if given_signatures is not None:
             self._check_given_signatures(given_signatures)
@@ -381,6 +382,11 @@ def _check_given_parameters(
                 given_sample_embeddings, self.n_samples, "given_sample_embeddings"
             )
 
+        if given_variance is not None:
+            type_checker("given_variance", given_variance, [float, int])
+            if given_variance <= 0.0:
+                raise ValueError("The variance has to be a positive real number.")
+
     def _initialize(
         self,
         given_signatures=None,
@@ -388,11 +394,12 @@ def _initialize(
         given_signature_embeddings=None,
         given_sample_biases=None,
         given_sample_embeddings=None,
+        given_variance=None,
         init_kwargs=None,
     ):
         """
         Initialize the signature matrix W, sample biases alpha, signature biases beta,
-        the squared variance, and the signature and sample embeddings.
+        the variance, and the signature and sample embeddings.
         The signatures or signature embeddings can also be provided by the user.
 
         Parameters
@@ -417,6 +424,9 @@ def _initialize(
         given_sample_embeddings : np.ndarray, default=None
             A priori known sample embeddings of shape (dim_embeddings, n_samples).
 
+        given_variance : float, default=None
+            A priori known model variance of the embeddings.
+
         init_kwargs : dict
             Any further keyword arguments to pass to the initialization method.
             This includes, for example, a possible 'seed' keyword argument
@@ -428,6 +438,7 @@ def _initialize(
             given_signature_embeddings,
             given_sample_biases,
             given_sample_embeddings,
+            given_variance,
         )
 
         if given_signatures is not None:
@@ -439,7 +450,11 @@ def _initialize(
         self.W, _, self.signature_names = initialize(
             self.X, self.n_signatures, self.init_method, given_signatures, **init_kwargs
         )
-        self.sigma_sq = 1.0
+
+        if given_variance is None:
+            self.sigma_sq = 1.0
+        else:
+            self.sigma_sq = float(given_variance)
 
         if given_signature_biases is None:
             self.beta = np.zeros(self.n_signatures)
@@ -449,7 +464,7 @@ def _initialize(
         if given_signature_embeddings is None:
             self.L = np.random.multivariate_normal(
                 np.zeros(self.dim_embeddings),
-                np.identity(self.dim_embeddings),
+                self.sigma_sq * np.identity(self.dim_embeddings),
                 size=self.n_signatures,
             ).T
         else:
@@ -463,7 +478,7 @@ def _initialize(
         if given_sample_embeddings is None:
             self.U = np.random.multivariate_normal(
                 np.zeros(self.dim_embeddings),
-                np.identity(self.dim_embeddings),
+                self.sigma_sq * np.identity(self.dim_embeddings),
                 size=self.n_samples,
             ).T
         else:
diff --git a/src/salamander/nmf_framework/corrnmf_det.py b/src/salamander/nmf_framework/corrnmf_det.py
index 2ad6a2b..3eda6f9 100644
--- a/src/salamander/nmf_framework/corrnmf_det.py
+++ b/src/salamander/nmf_framework/corrnmf_det.py
@@ -59,10 +59,11 @@ def _update_beta(self, p, given_signature_biases=None):
                 self.X, p, self.alpha, self.L, self.U
             )
 
-    def _update_sigma_sq(self):
-        embeddings = np.concatenate([self.L, self.U], axis=1)
-        self.sigma_sq = np.mean(embeddings**2)
-        self.sigma_sq = np.clip(self.sigma_sq, EPSILON, None)
+    def _update_sigma_sq(self, given_variance=None):
+        if given_variance is None:
+            embeddings = np.concatenate([self.L, self.U], axis=1)
+            self.sigma_sq = np.mean(embeddings**2)
+            self.sigma_sq = np.clip(self.sigma_sq, EPSILON, None)
 
     def _update_W(self):
         self.W = update_W(
@@ -191,6 +192,7 @@ def fit(
         given_signature_embeddings=None,
         given_sample_biases=None,
         given_sample_embeddings=None,
+        given_variance=None,
         init_kwargs=None,
         history=False,
         verbose=0,
@@ -217,9 +219,12 @@ def fit(
             Known sample biases of shape (n_samples,) that will be fixed
             during model fitting.
 
-        given_sample_embeddings: np.ndarray, default=None
+        given_sample_embeddings : np.ndarray, default=None
             Known sample embeddings that will be fixed during model fitting.
 
+        given_variance : float, default=None
+            Known model variance that will be fixed during model fitting.
+
         init_kwargs: dict
             Any further keywords arguments to be passed to the initialization method.
             This includes, for example, a possible 'seed' keyword argument
@@ -244,6 +249,7 @@ def fit(
             given_signature_embeddings=given_signature_embeddings,
             given_sample_biases=given_sample_biases,
             given_sample_embeddings=given_sample_embeddings,
+            given_variance=given_variance,
             init_kwargs=init_kwargs,
         )
         of_values = [self.objective_function()]
@@ -260,7 +266,7 @@ def fit(
             p = self._update_p()
             self._update_beta(p, given_signature_biases)
             self._update_LU(p, given_signature_embeddings, given_sample_embeddings)
-            self._update_sigma_sq()
+            self._update_sigma_sq(given_variance)
 
             if self.n_given_signatures < self.n_signatures:
                 self._update_W()
diff --git a/src/salamander/nmf_framework/multimodal_corrnmf.py b/src/salamander/nmf_framework/multimodal_corrnmf.py
index 3b90573..cdf2f56 100755
--- a/src/salamander/nmf_framework/multimodal_corrnmf.py
+++ b/src/salamander/nmf_framework/multimodal_corrnmf.py
@@ -187,14 +187,15 @@ def _update_betas(self, ps, given_signature_biases):
         for model, p, given_sig_biases in zip(self.models, ps, given_signature_biases):
             model._update_beta(p, given_sig_biases)
 
-    def _update_sigma_sq(self):
-        Ls = np.concatenate([model.L for model in self.models], axis=1)
-        embeddings = np.concatenate([Ls, self.models[0].U], axis=1)
-        sigma_sq = np.mean(embeddings**2)
-        sigma_sq = np.clip(sigma_sq, EPSILON, None)
+    def _update_sigma_sq(self, given_variance):
+        if given_variance is None:
+            Ls = np.concatenate([model.L for model in self.models], axis=1)
+            embeddings = np.concatenate([Ls, self.models[0].U], axis=1)
+            sigma_sq = np.mean(embeddings**2)
+            sigma_sq = np.clip(sigma_sq, EPSILON, None)
 
-        for model in self.models:
-            model.sigma_sq = sigma_sq
+            for model in self.models:
+                model.sigma_sq = sigma_sq
 
     def _update_Ws(self):
         for model in self.models:
@@ -351,11 +352,17 @@ def _initialize(
         given_signature_embeddings=None,
         given_sample_biases=None,
         given_sample_embeddings=None,
+        given_variance=None,
         init_kwargs=None,
     ):
         if given_signatures is None:
             given_signatures = [None for _ in range(self.n_modalities)]
 
+        if given_variance is None:
+            variance = 1.0
+        else:
+            variance = given_variance
+
         if given_signature_biases is None:
             given_signature_biases = [None for _ in range(self.n_modalities)]
 
@@ -368,7 +375,7 @@ def _initialize(
         if given_sample_embeddings is None:
             U = np.random.multivariate_normal(
                 np.zeros(self.dim_embeddings),
-                np.identity(self.dim_embeddings),
+                variance * np.identity(self.dim_embeddings),
                 size=self.n_samples,
             ).T
         else:
@@ -395,6 +402,7 @@ def _initialize(
                 given_signature_embeddings=given_sig_embs,
                 given_sample_biases=given_sam_biases,
                 given_sample_embeddings=U,
+                given_variance=variance,
                 init_kwargs=init_kwargs,
             )
             model.signature_names = np.concatenate(
@@ -410,7 +418,6 @@ def _initialize(
             given_signature_biases,
             given_signature_embeddings,
             given_sample_biases,
-            given_sample_embeddings,
         )
 
     def fit(
@@ -421,6 +428,7 @@ def fit(
         given_signature_embeddings=None,
         given_sample_biases=None,
         given_sample_embeddings=None,
+        given_variance=None,
         init_kwargs=None,
         history=False,
         verbose=0,
@@ -430,13 +438,13 @@ def fit(
             given_signature_biases,
             given_signature_embeddings,
             given_sample_biases,
-            given_sample_embeddings,
         ) = self._initialize(
             given_signatures=given_signatures,
             given_signature_biases=given_signature_biases,
             given_signature_embeddings=given_signature_embeddings,
             given_sample_biases=given_sample_biases,
             given_sample_embeddings=given_sample_embeddings,
+            given_variance=given_variance,
             init_kwargs=init_kwargs,
         )
         of_values = [self.objective_function()]
@@ -453,7 +461,7 @@ def fit(
             ps = self._update_ps()
             self._update_betas(ps, given_signature_biases)
             self._update_LsU(ps, given_signature_embeddings, given_sample_embeddings)
-            self._update_sigma_sq()
+            self._update_sigma_sq(given_variance)
             self._update_Ws()
 
             if n_iteration % self.conv_test_freq == 0:
diff --git a/tests/test_corrnmf.py b/tests/test_corrnmf.py
index 2dad2f4..481c9d7 100644
--- a/tests/test_corrnmf.py
+++ b/tests/test_corrnmf.py
@@ -214,3 +214,8 @@ def test_given_sample_embeddings(self, model, counts):
         )
         model.fit(counts, given_sample_embeddings=given_sample_embeddings)
         assert np.allclose(given_sample_embeddings, model.U)
+
+    def test_given_variance(self, model, counts):
+        given_variance = 3
+        model.fit(counts, given_variance=given_variance)
+        assert np.allclose(given_variance, model.sigma_sq)
diff --git a/tests/test_multimodal_corrnmf.py b/tests/test_multimodal_corrnmf.py
index 6d08611..185a0d7 100644
--- a/tests/test_multimodal_corrnmf.py
+++ b/tests/test_multimodal_corrnmf.py
@@ -239,7 +239,8 @@ def test_update_U(self, multi_model_init, _auxs, U_updated):
             assert np.allclose(model.U, U_updated)
 
     def test_update_sigma_sq(self, multi_model_init, sigma_sq_updated):
-        multi_model_init._update_sigma_sq()
+        given_variance = None
+        multi_model_init._update_sigma_sq(given_variance)
 
         for model in multi_model_init.models:
             assert np.allclose(model.sigma_sq, sigma_sq_updated)
@@ -313,3 +314,10 @@ def test_given_sample_embeddings(self, multi_model, counts):
 
         for model in multi_model.models:
             assert np.allclose(given_sample_embeddings, model.U)
+
+    def test_given_variance(self, multi_model, counts):
+        given_variance = 3
+        multi_model.fit(counts, given_variance=given_variance)
+
+        for model in multi_model.models:
+            assert np.allclose(given_variance, model.sigma_sq)

From d4bd71f4f50971d049cfd3085a8ab5d7e323757c Mon Sep 17 00:00:00 2001
From: BeGeiger <benedikt-geiger@gmx.de>
Date: Wed, 3 Jan 2024 11:06:58 -0500
Subject: [PATCH 5/5] bump version

---
 CHANGELOG.md               | 8 ++++++--
 pyproject.toml             | 2 +-
 src/salamander/__init__.py | 2 +-
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7f67f4b..788e7a6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ---
 ---
 
+## 0.3.2 - 2024-01
+### Fixed
+  - Support fixing the model variance of (multimodal) CorrNMF models during training
+
 ## 0.3.1 - 2023-12
 ### Fixed
   - Improve signature, history and embedding plots
@@ -16,7 +20,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
   - Support a sample-weigted KL-divergence loss in KL-NMF
   - Support a sample-weighted sparsity regularization in KL-NMF
-  - Support fixing signature and sample biases in (multimodal) CorrNMF during inference
+  - Support fixing signature and sample biases of (multimodal) CorrNMF models during training
 
 ## 0.2.1 - 2023-10
 ### Fixed
@@ -25,7 +29,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## 0.2.0 - 2023-10
 ### Added
-  - Support fixing arbitrary many a priori known signatures during inference
+  - Support fixing arbitrary many a priori known signatures during model training
   - Improved performance with just-in-time compiled update rules
 
 ## 0.1.0 - 2023-10
diff --git a/pyproject.toml b/pyproject.toml
index 98a23a9..1872680 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "salamander-learn"
-version = "0.3.1"
+version = "0.3.2"
 description = "Salamander is a non-negative matrix factorization framework for signature analysis"
 license = "MIT"
 authors = ["Benedikt Geiger"]
diff --git a/src/salamander/__init__.py b/src/salamander/__init__.py
index 8198b35..59d4511 100644
--- a/src/salamander/__init__.py
+++ b/src/salamander/__init__.py
@@ -8,7 +8,7 @@
 from .nmf_framework.mvnmf import MvNMF
 from .plot import set_salamander_style
 
-__version__ = "0.3.1"
+__version__ = "0.3.2"
 __all__ = ["CorrNMFDet", "KLNMF", "MvNMF", "MultimodalCorrNMF"]