From b526d416702e3ffcf4ef37863c3c1bf8a36d6b1a Mon Sep 17 00:00:00 2001
From: Malte Benedikt Kuehl <malte.kuehl@clin.au.dk>
Date: Sat, 13 Sep 2025 02:38:16 +0200
Subject: [PATCH 1/2] Working on pytximport support

---
 .gitignore                                    |     5 +
 docs/source/.DS_Store                         |   Bin 6148 -> 0 bytes
 docs/source/index.rst                         |     9 +-
 docs/source/refs.bib                          |    14 +-
 examples/plot_pytximport_example.py           |   164 +
 pydeseq2/dds.py                               |   122 +
 pydeseq2/default_inference.py                 |     6 +
 pydeseq2/ds.py                                |    25 +-
 pydeseq2/inference.py                         |     1 +
 pydeseq2/preprocessing.py                     |    79 +
 pydeseq2/utils.py                             |    34 +-
 pyproject.toml                                |     8 +-
 tests/data/pytximport/test_counts.csv         | 41128 ++++++++++++++++
 tests/data/pytximport/test_metadata.csv       |     5 +
 tests/data/pytximport/test_pytximport.h5ad    |   Bin 0 -> 20161264 bytes
 .../test_pytximport_length_scaled.h5ad        |   Bin 0 -> 20161264 bytes
 tests/test_pytximport.py                      |   240 +
 17 files changed, 41819 insertions(+), 21 deletions(-)
 delete mode 100644 docs/source/.DS_Store
 create mode 100644 examples/plot_pytximport_example.py
 create mode 100644 tests/data/pytximport/test_counts.csv
 create mode 100644 tests/data/pytximport/test_metadata.csv
 create mode 100644 tests/data/pytximport/test_pytximport.h5ad
 create mode 100644 tests/data/pytximport/test_pytximport_length_scaled.h5ad
 create mode 100644 tests/test_pytximport.py

diff --git a/.gitignore b/.gitignore
index c3b28032..79719dee 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,6 +70,8 @@ instance/
 
 # Sphinx documentation
 docs/build/
+docs/source/auto_examples/
+output_files/
 
 # PyBuilder
 target/
@@ -128,6 +130,9 @@ venv.bak/
 .dmypy.json
 dmypy.json
 
+# ruff
+.ruff_cache/
+
 # Pyre type checker
 .pyre/
 
diff --git a/docs/source/.DS_Store b/docs/source/.DS_Store
deleted file mode 100644
index 4e51ff10c6752e39c8019426a3542dad954e1ac3..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/docs/source/index.rst b/docs/source/index.rst
index 4de749ec..d80592c1 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -17,7 +17,14 @@ from scratch, you may experience some differences in terms of retrieved values o
 Currently, available features broadly correspond to the default settings of DESeq2 (v1.34.0) for single-factor and
 multi-factor analysis (with categorical or continuous factors) using Wald tests, with an
 optional `apeGLM <https://academic.oup.com/bioinformatics/article/35/12/2084/5159452>`_ LFC shrinkage step
-:cite:p:`zhu2019heavy`. We plan to implement more in the near future. In case there is a feature you would particularly
+:cite:p:`zhu2019heavy`. 
+
+PyDESeq2 also supports `pytximport <https://bioconductor.org/packages/release/bioc/html/tximport.html>`_-derived 
+normalization factors :cite:p:`kuehlGeneCountEstimation2024b`, enabling accurate differential expression analysis 
+with transcript-level quantification data from tools like Salmon, Kallisto, or RSEM. When explicitly enabled,
+this feature accounts for gene length differences between samples due to differential isoform usage.
+
+We plan to implement more features in the near future. In case there is a feature you would particularly
 like to be implemented, feel free to open an issue on GitHub.
 
 
diff --git a/docs/source/refs.bib b/docs/source/refs.bib
index a579d2d8..25f158fc 100644
--- a/docs/source/refs.bib
+++ b/docs/source/refs.bib
@@ -30,4 +30,16 @@ @article{zhu2019heavy
   year={2019},
   doi={10.1093/bioinformatics/bty895},
   publisher={Oxford University Press}
-}
\ No newline at end of file
+}
+
+@article{kuehlGeneCountEstimation2024b,
+  title = {Gene Count Estimation with Pytximport Enables Reproducible Analysis of Bulk {{RNA}} Sequencing Data in {{Python}}},
+  author = {Kuehl, Malte and Wong, Milagros N and Wanner, Nicola and Bonn, Stefan and Puelles, Victor G},
+  year = {2024},
+  journal = {Bioinformatics},
+  volume = {40},
+  number = {12},
+  pages = {btae700},
+  doi = {10.1093/bioinformatics/btae700},
+  publisher = {Oxford University Press}
+}
diff --git a/examples/plot_pytximport_example.py b/examples/plot_pytximport_example.py
new file mode 100644
index 00000000..a7735d74
--- /dev/null
+++ b/examples/plot_pytximport_example.py
@@ -0,0 +1,164 @@
+"""
+pytximport Integration Example
+==============================
+
+This example demonstrates how to use PyDESeq2 with pytximport-derived data
+for differential expression analysis with transcript-level quantification data.
+
+pytximport enables the import of transcript-level abundance estimates from
+popular RNA-seq quantification tools like Salmon, Kallisto, or RSEM into
+PyDESeq2, while accounting for gene length differences between samples
+due to differential isoform usage.
+"""
+
+import anndata as ad
+import matplotlib.pyplot as plt
+import numpy as np
+
+from pydeseq2.dds import DeseqDataSet
+from pydeseq2.ds import DeseqStats
+
+# %%
+# Read AnnData object
+# -------------------
+#
+# Make sure that you have rounded the counts in .X to integers, as DESeq2 requires
+# integer counts and that pytximport was used with counts_from_abundance=None
+# (raw counts) to generate the AnnData object.
+
+adata = ad.read_h5ad("../tests/data/pytximport/test_pytximport.h5ad")
+
+# %%
+# Standard usage without pytximport
+# ---------------------------------
+#
+# By default, PyDESeq2 uses standard size factor normalization.
+
+dds_standard = DeseqDataSet(adata=adata, design="~condition")
+dds_standard.fit_size_factors()
+print(f"Standard normalization: {'normalization_factors' not in dds_standard.obsm}")
+
+# %%
+# Explicit pytximport mode
+# ------------------------
+#
+# To use pytximport normalization factors, explicitly set from_pytximport=True.
+
+dds_explicit = DeseqDataSet(adata=adata, design="~condition", from_pytximport=True)
+dds_explicit.fit_size_factors()
+print(f"Pytximport normalization: {'normalization_factors' in dds_explicit.obsm}")
+
+# %%
+# Size factor estimation with normalization factors
+# -------------------------------------------------
+#
+# When pytximport data is used, PyDESeq2 computes normalization factors
+# that account for both library size and gene length differences.
+
+dds_explicit.fit_size_factors()
+
+print(f"Size factors computed: {'size_factors' in dds_explicit.obs}")
+print(f"Normalization factors computed: {'normalization_factors' in dds_explicit.obsm}")
+
+# Check normalization factors properties
+norm_factors = dds_explicit.obsm["normalization_factors"]
+size_factors = dds_explicit.obs["size_factors"]
+
+print(f"Normalization factors shape: {norm_factors.shape}")
+print(f"Size factors shape: {size_factors.shape}")
+print(f"Size factors: {size_factors.values}")
+print(f"Norm factors range: {norm_factors.min():.3f} - {norm_factors.max():.3f}")
+
+# %%
+# Plot distribution of normalization factors
+# ------------------------------------------
+#
+# Normalization factors should vary around 1, reflecting gene length
+# differences between samples.
+plt.figure(figsize=(8, 5))
+plt.hist(
+    norm_factors.flatten(),
+    bins=list(np.arange(0.5, 1.6, 0.1).astype(float)),
+    color="skyblue",
+    edgecolor="black",
+    alpha=0.7,
+)
+plt.axvline(x=1, color="red", linestyle="--", linewidth=2, label="x = 1")
+plt.title("Distribution of Normalization Factors")
+plt.xlabel("Normalization Factor")
+plt.ylabel("Frequency")
+plt.legend()
+plt.tight_layout()
+plt.show()
+
+# %%
+# Full differential expression analysis
+# -------------------------------------
+#
+# The complete DESeq2 pipeline works seamlessly with pytximport data.
+
+# Run the full DESeq2 pipeline
+dds_explicit.deseq2()
+
+# Perform statistical testing
+stat_res = DeseqStats(dds_explicit, contrast=["condition", "ko", "wt"])
+stat_res.summary()
+results_df = stat_res.results_df
+
+print("Differential expression results:")
+print(f"Total genes analyzed: {len(results_df)}")
+print(f"Significant genes (padj < 0.05): {sum(results_df['padj'] < 0.05)}")
+
+# Display top results
+print("\nTop 5 most significant genes:")
+top_results = results_df.sort_values("padj").head()
+print(top_results[["baseMean", "log2FoldChange", "pvalue", "padj"]])
+stat_res.plot_MA()
+
+# %%
+# Differential expression analysis without normalization factors
+# --------------------------------------------------------------
+#
+# Compare results with standard DESeq2 normalization (without length correction).
+
+# Read standard AnnData (generated with counts_from_abundance="length_scaled_tpm")
+adata_standard = ad.read_h5ad(
+    "../tests/data/pytximport/test_pytximport_length_scaled.h5ad"
+)
+
+dds_standard = DeseqDataSet(adata=adata_standard, design="~condition")
+dds_standard.deseq2()
+
+stat_res_standard = DeseqStats(dds_standard, contrast=["condition", "ko", "wt"])
+stat_res_standard.summary()
+results_standard = stat_res_standard.results_df
+print("\nTop 5 most significant genes:")
+top_results = results_standard.sort_values("padj").head()
+print(top_results[["baseMean", "log2FoldChange", "pvalue", "padj"]])
+stat_res_standard.plot_MA()
+
+# %%
+# Comparison with standard normalization
+# --------------------------------------
+#
+# Compare results with standard DESeq2 normalization (without length correction).
+pytximport_sig = sum(results_df["padj"] < 0.05)
+standard_sig = sum(results_standard["padj"] < 0.05)
+
+print("\nComparison of significant genes (padj < 0.05):")
+print(f"pytximport normalization: {pytximport_sig}")
+print(f"Standard normalization: {standard_sig}")
+
+# %%
+# Variance Stabilizing Transformation (VST)
+# -----------------------------------------
+#
+# VST also works with pytximport normalization factors.
+
+dds_vst = DeseqDataSet(adata=adata, design="~condition", from_pytximport=True)
+dds_vst.vst()
+
+vst_counts = dds_vst.layers["vst_counts"]
+print("\nVST transformation completed")
+print(f"VST counts shape: {vst_counts.shape}")
+print(f"VST counts range: {vst_counts.min():.3f} - {vst_counts.max():.3f}")
diff --git a/pydeseq2/dds.py b/pydeseq2/dds.py
index cfff35a3..6a8e90c7 100644
--- a/pydeseq2/dds.py
+++ b/pydeseq2/dds.py
@@ -16,6 +16,7 @@
 from pydeseq2.inference import Inference
 from pydeseq2.preprocessing import deseq2_norm_fit
 from pydeseq2.preprocessing import deseq2_norm_transform
+from pydeseq2.preprocessing import estimate_norm_factors
 from pydeseq2.utils import dispersion_trend
 from pydeseq2.utils import make_scatter
 from pydeseq2.utils import mean_absolute_deviation
@@ -135,6 +136,14 @@ class DeseqDataSet(ad.AnnData):
         longer necessary after they are used during deseq2 run, such as Cook's
         distances. (default: False)
 
+    from_pytximport : bool
+        Whether the data comes from pytximport and should use length-corrected
+        normalization factors instead of simple size factors. Must be explicitly
+        set to True to enable pytximport support. When enabled, will validate the
+        presence of "length" in obsm and "counts_from_abundance" in uns. Counts
+        must have been generated with ``counts_from_abundance=None``.
+        (default: ``False``)
+
     Attributes
     ----------
     X
@@ -225,6 +234,7 @@ def __init__(
         inference: Inference | None = None,
         quiet: bool = False,
         low_memory: bool = False,
+        from_pytximport: bool = False,
     ) -> None:
         # Initialize the AnnData part
         if adata is not None:
@@ -318,6 +328,11 @@ def __init__(
         self.control_genes = control_genes
         self.logmeans = None
         self.filtered_genes = None
+        self.from_pytximport = from_pytximport
+
+        if self.from_pytximport:
+            print("Detected pytximport data with length offsets.")
+            self._validate_pytximport_data()
 
         if inference:
             if n_cpus:
@@ -645,6 +660,11 @@ def fit_size_factors(
         else:
             _control_mask = np.ones(self.X.shape[1], dtype=bool)
 
+        if fit_type not in [None, "ratio"] and self.from_pytximport:
+            raise ValueError(
+                "When from_pytximport=True, fit_type must be 'ratio' (default)."
+            )
+
         if fit_type == "iterative":
             self._fit_iterate_size_factors()
 
@@ -682,8 +702,56 @@ def sizeFactor(x):
                 UserWarning,
                 stacklevel=2,
             )
+            if self.from_pytximport:
+                raise ValueError(
+                    "When from_pytximport=True, size factors cannot be computed "
+                    "iteratively. Please check your input data."
+                )
+
             self._fit_iterate_size_factors()
 
+        elif self.from_pytximport:
+            if not self.quiet:
+                print(
+                    "Using pytximport length offsets for normalization factors...",
+                    file=sys.stderr,
+                )
+
+            # Create normalization matrix from length matrix
+            # Divide each gene's length by the geometric mean across samples
+            length_matrix = self.obsm["length"]
+
+            with np.errstate(divide="ignore"):
+                # Gene-wise geometric means
+                log_geom_means = np.mean(np.log(length_matrix), axis=0)
+                norm_matrix = length_matrix / np.exp(log_geom_means)
+
+            # Compute normalization factors using the DESeq2 algorithm
+            (
+                counts_adj,
+                self.layers["normed_counts"],
+                norm_factors,
+                logmeans,
+                filtered_genes,
+            ) = estimate_norm_factors(self.X, norm_matrix, _control_mask)
+
+            self.obsm["normalization_factors"] = norm_factors
+            self.logmeans = logmeans
+            self.filtered_genes = filtered_genes
+
+            # Calculate the size factors on the normed counts
+            _, self.obs["size_factors"] = deseq2_norm_transform(
+                counts_adj, logmeans, filtered_genes
+            )
+
+            # Store sample-wise geometric mean of norm factors as size_factors
+            # This maintains compatibility with existing code while incorporating
+            # the gene-length correction
+            # with np.errstate(divide="ignore"):
+            #     log_norm_factors = np.log(norm_factors)
+            #     log_geom_mean_per_sample = np.mean(log_norm_factors, axis=1)
+            #     self.obs["size_factors"] = np.exp(log_geom_mean_per_sample)
+
         else:
             self.logmeans, self.filtered_genes = deseq2_norm_fit(self.X)
             _control_mask &= self.filtered_genes
@@ -744,6 +812,12 @@ def fit_genewise_dispersions(self, vst=False) -> None:
                 min_mu=self.min_mu,
             )
         else:
+            # Use normalization factors if available
+            norm_factors = (
+                self.obsm["normalization_factors"][:, self.non_zero_idx]
+                if "normalization_factors" in self.obsm
+                else None
+            )
             _, mu_hat_, _, _ = self.inference.irls(
                 counts=self.X[:, self.non_zero_idx],
                 size_factors=size_factors,
@@ -751,6 +825,7 @@ def fit_genewise_dispersions(self, vst=False) -> None:
                 disp=self.var.loc[self.var["non_zero"], "_MoM_dispersions"].values,
                 min_mu=self.min_mu,
                 beta_tol=self.beta_tol,
+                normalization_factors=norm_factors,
             )
 
         mu_param_name = "_vst_mu_hat" if vst else "_mu_hat"
@@ -939,6 +1014,12 @@ def fit_LFC(self) -> None:
         if not self.quiet:
             print("Fitting LFCs...", file=sys.stderr)
         start = time.time()
+        # Use normalization factors if available
+        norm_factors = (
+            self.obsm["normalization_factors"][:, self.non_zero_idx]
+            if "normalization_factors" in self.obsm
+            else None
+        )
         mle_lfcs_, mu_, hat_diagonals_, converged_ = self.inference.irls(
             counts=self.X[:, self.non_zero_idx],
             size_factors=self.obs["size_factors"].values,
@@ -946,6 +1027,7 @@ def fit_LFC(self) -> None:
             disp=self.var.loc[self.var["non_zero"], "dispersions"].values,
             min_mu=self.min_mu,
             beta_tol=self.beta_tol,
+            normalization_factors=norm_factors,
         )
         end = time.time()
 
@@ -1548,3 +1630,43 @@ def _check_full_rank_design(self):
                 UserWarning,
                 stacklevel=2,
             )
+
+    def _validate_pytximport_data(self):
+        """Validate that the data has the necessary pytximport information."""
+        if "length" not in self.obsm_keys():
+            raise ValueError(
+                "from_pytximport=True but 'length' not found in obsm. "
+                "Make sure your AnnData object was created from pytximport output."
+            )
+
+        if "counts_from_abundance" not in self.uns_keys():
+            raise ValueError(
+                "from_pytximport=True but 'counts_from_abundance' not found in uns. "
+                "Make sure your AnnData object was created from pytximport output."
+            )
+
+        # Check that counts_from_abundance is None (meaning raw counts were used)
+        if self.uns["counts_from_abundance"] is not None:
+            raise ValueError(
+                "Normalization factors can only be used with raw counts. "
+                f"Got counts_from_abundance={self.uns['counts_from_abundance']}. "
+                "Please use pytximport with counts_from_abundance=None."
+            )
+
+        # Validate length matrix dimensions
+        length_matrix = self.obsm["length"]
+        if length_matrix.shape != (self.n_obs, self.n_vars):
+            raise ValueError(
+                f"Length matrix shape {length_matrix.shape} does not match "
+                f"count matrix shape {(self.n_obs, self.n_vars)}"
+            )
+
+        # Check for non-positive lengths
+        if np.any(length_matrix <= 0):
+            raise ValueError(
+                "Length matrix contains non-positive values. "
+                "All gene lengths must be positive."
+            )
+
+        if not self.quiet:
+            print("Detected pytximport data with length offsets.", file=sys.stderr)
diff --git a/pydeseq2/default_inference.py b/pydeseq2/default_inference.py
index 9bd67bad..ed3733ca 100644
--- a/pydeseq2/default_inference.py
+++ b/pydeseq2/default_inference.py
@@ -92,6 +92,7 @@ def irls(  # noqa: D102
         max_beta: float = 30,
         optimizer: Literal["BFGS", "L-BFGS-B"] = "L-BFGS-B",
         maxiter: int = 250,
+        normalization_factors: np.ndarray | None = None,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
         with parallel_backend(self._backend, inner_max_num_threads=1):
             res = Parallel(
@@ -110,6 +111,11 @@ def irls(  # noqa: D102
                     max_beta=max_beta,
                     optimizer=optimizer,
                     maxiter=maxiter,
+                    normalization_factors=(
+                        normalization_factors[:, i]
+                        if normalization_factors is not None
+                        else None
+                    ),
                 )
                 for i in range(counts.shape[1])
             )
diff --git a/pydeseq2/ds.py b/pydeseq2/ds.py
index 3419607f..03780f42 100644
--- a/pydeseq2/ds.py
+++ b/pydeseq2/ds.py
@@ -316,11 +316,20 @@ def run_wald_test(self) -> None:
                     file=sys.stderr,
                 )
 
-        mu = (
-            np.exp(self.design_matrix @ self.LFC.T)
-            .multiply(self.dds.obs["size_factors"], 0)
-            .values
-        )
+        # Use normalization factors if available, otherwise use size factors
+        if "normalization_factors" in self.dds.obsm:
+            # For normalization factors: mu = exp(X @ beta) * norm_factors
+            # Both matrices are (samples, genes)
+            exp_design_lfc = np.exp(self.design_matrix @ self.LFC.T).values
+            mu = exp_design_lfc * self.dds.obsm["normalization_factors"]
+        else:
+            # Standard case: mu = exp(X @ beta) * size_factors
+            # Result is (samples, genes), size_factors is (samples,)
+            mu = (
+                np.exp(self.design_matrix @ self.LFC.T)
+                .multiply(self.dds.obs["size_factors"], 0)
+                .values
+            )
 
         # Set regularization factors.
         if self.prior_LFC_var is not None:
@@ -384,7 +393,11 @@ def lfc_shrink(self, coeff: str, adapt: bool = True) -> None:
 
         design_matrix = self.design_matrix.values
         size = 1.0 / self.dds.var["dispersions"].values
-        offset = np.log(self.dds.obs["size_factors"]).values
+
+        if "normalization_factors" in self.dds.obsm:
+            offset = np.log(self.dds.obsm["normalization_factors"])
+        else:
+            offset = np.log(self.dds.obs["size_factors"].values)
 
         # Set priors
         prior_no_shrink_scale = 15
diff --git a/pydeseq2/inference.py b/pydeseq2/inference.py
index 40bff73b..e79085fc 100644
--- a/pydeseq2/inference.py
+++ b/pydeseq2/inference.py
@@ -55,6 +55,7 @@ def irls(
         max_beta: float = 30,
         optimizer: Literal["BFGS", "L-BFGS-B"] = "L-BFGS-B",
         maxiter: int = 250,
+        normalization_factors: np.ndarray | None = None,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
         r"""Fit a NB GLM wit log-link to predict counts from the design matrix.
 
diff --git a/pydeseq2/preprocessing.py b/pydeseq2/preprocessing.py
index 0c0a5a34..d2dc2cac 100644
--- a/pydeseq2/preprocessing.py
+++ b/pydeseq2/preprocessing.py
@@ -100,3 +100,82 @@ def deseq2_norm_transform(
     size_factors = np.exp(log_medians)
     deseq2_counts = counts / size_factors[:, None]
     return deseq2_counts, size_factors
+
+
+def estimate_norm_factors(
+    counts: pd.DataFrame | np.ndarray,
+    norm_matrix: np.ndarray,
+    control_mask: np.ndarray | None = None,
+) -> tuple[
+    pd.DataFrame | np.ndarray,
+    pd.DataFrame | np.ndarray,
+    np.ndarray,
+    np.ndarray,
+    np.ndarray,
+]:
+    """Estimate normalization factors from counts and normalization matrix.
+
+    Implements the DESeq2 estimateNormFactors algorithm for pytximport integration.
+    This function computes normalization factors that account for both library size
+    and gene length differences.
+
+    Parameters
+    ----------
+    counts : pandas.DataFrame or ndarray
+        Raw counts. One column per gene, one row per sample.
+
+    norm_matrix : ndarray
+        Normalization matrix. Should have the same dimensions as counts.
+
+    control_mask : ndarray, optional
+        Boolean mask indicating which genes to use as control genes for size
+        factor estimation. If None, all genes are used.
+
+    Returns
+    -------
+    counts_adj : pandas.DataFrame or ndarray
+        Counts adjusted by the normalization matrix.
+
+    normed_counts : pandas.DataFrame or ndarray
+        Normalized counts using the computed normalization factors.
+
+    norm_factors : ndarray
+        Computed normalization factors that account for both library size
+        and gene length differences.
+
+    logmeans : ndarray
+        Gene-wise mean log counts from the adjusted counts.
+
+    filtered_genes : ndarray
+        Genes whose log means are different from -∞ in the adjusted counts.
+    """
+    # Divide counts by normalization matrix
+    counts_adj = counts / norm_matrix
+
+    # Compute size factors from adjusted counts using standard DESeq2 method
+    logmeans, filtered_genes = deseq2_norm_fit(counts_adj)
+
+    # Apply control gene mask if provided
+    if control_mask is not None:
+        filtered_genes = filtered_genes & control_mask
+
+    _, size_factors = deseq2_norm_transform(counts_adj, logmeans, filtered_genes)
+
+    # Compute normalization factors: (norm_matrix.T * size_factors).T
+    # Broadcasting: norm_matrix (samples x genes) * size_factors (samples,)
+    norm_factors = norm_matrix * size_factors[:, None]
+
+    # Normalize to geometric mean of 1 (row-wise geometric mean)
+    with np.errstate(divide="ignore"):  # ignore division by zero warnings
+        log_norm_factors = np.log(norm_factors)
+        log_geom_means = np.mean(log_norm_factors, axis=1, keepdims=True)
+        norm_factors = norm_factors / np.exp(log_geom_means)
+
+    # Compute normalized counts
+    normed_counts = counts / norm_factors
+
+    # TODO: Check whether we actually have to calculate logmeans and filtered_genes again
+    # from the normed counts, or if we can just return the previous ones.
+    logmeans, filtered_genes = deseq2_norm_fit(normed_counts)
+
+    return counts_adj, normed_counts, norm_factors, logmeans, filtered_genes
diff --git a/pydeseq2/utils.py b/pydeseq2/utils.py
index c538c137..7aa20382 100644
--- a/pydeseq2/utils.py
+++ b/pydeseq2/utils.py
@@ -283,6 +283,7 @@ def irls_solver(
     max_beta: float = 30,
     optimizer: Literal["BFGS", "L-BFGS-B"] = "L-BFGS-B",
     maxiter: int = 250,
+    normalization_factors: np.ndarray | None = None,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray, bool]:
     r"""Fit a NB GLM wit log-link to predict counts from the design matrix.
 
@@ -327,6 +328,11 @@ def irls_solver(
         Maximum number of IRLS iterations to perform before switching to L-BFGS-B.
         (default: ``250``).
 
+    normalization_factors : ndarray, optional
+        Gene-specific normalization factors (samples x genes). If provided, these
+        will be used instead of size_factors for computing mu. This enables support
+        for pytximport length-corrected normalization. (default: ``None``).
+
     Returns
     -------
     beta: ndarray
@@ -347,28 +353,36 @@ def irls_solver(
     num_vars = design_matrix.shape[1]
     X = design_matrix
 
+    # Choose appropriate normalization factors
+    if normalization_factors is not None:
+        # Use gene-specific normalization factors (1D array for single gene)
+        norm_factors = normalization_factors
+    else:
+        # Use standard size factors (1D array)
+        norm_factors = size_factors
+
     # if full rank, estimate initial betas for IRLS below
     if np.linalg.matrix_rank(X) == num_vars:
         Q, R = np.linalg.qr(X)
-        y = np.log(counts / size_factors + 0.1)
+        y = np.log(counts / norm_factors + 0.1)
         beta_init = solve(R, Q.T @ y)
         beta = beta_init
     else:  # Initialise intercept with log base mean
         beta_init = np.zeros(num_vars)
-        beta_init[0] = np.log(counts / size_factors).mean()
+        beta_init[0] = np.log(counts / norm_factors).mean()
         beta = beta_init
 
     dev = 1000.0
     dev_ratio = 1.0
 
     ridge_factor = np.diag(np.repeat(1e-6, num_vars))
-    mu = np.maximum(size_factors * np.exp(X @ beta), min_mu)
+    mu = np.maximum(norm_factors * np.exp(X @ beta), min_mu)
 
     converged = True
     i = 0
     while dev_ratio > beta_tol:
         W = mu / (1.0 + mu * disp)
-        z = np.log(mu / size_factors) + (counts - mu) / mu
+        z = np.log(mu / norm_factors) + (counts - mu) / mu
         H = (X.T * W) @ X + ridge_factor
         beta_hat = solve(H, X.T @ (W * z), assume_a="pos")
         i += 1
@@ -377,11 +391,11 @@ def irls_solver(
             # If IRLS starts diverging, use L-BFGS-B
             def f(beta: np.ndarray) -> float:
                 # closure to minimize
-                mu_ = np.maximum(size_factors * np.exp(X @ beta), min_mu)
+                mu_ = np.maximum(norm_factors * np.exp(X @ beta), min_mu)
                 return nb_nll(counts, mu_, disp) + 0.5 * (ridge_factor @ beta**2).sum()
 
             def df(beta: np.ndarray) -> np.ndarray:
-                mu_ = np.maximum(size_factors * np.exp(X @ beta), min_mu)
+                mu_ = np.maximum(norm_factors * np.exp(X @ beta), min_mu)
                 return (
                     -X.T @ counts
                     + ((1 / disp + counts) * mu_ / (1 / disp + mu_)) @ X
@@ -401,7 +415,7 @@ def df(beta: np.ndarray) -> np.ndarray:
             )
 
             beta = res.x
-            mu = np.maximum(size_factors * np.exp(X @ beta), min_mu)
+            mu = np.maximum(norm_factors * np.exp(X @ beta), min_mu)
             converged = res.success
 
             if not res.success and num_vars <= 2:
@@ -411,11 +425,11 @@ def df(beta: np.ndarray) -> np.ndarray:
                     X,
                     disp,
                 )
-                mu = np.maximum(size_factors * np.exp(X @ beta), min_mu)
+                mu = np.maximum(norm_factors * np.exp(X @ beta), min_mu)
             break
 
         beta = beta_hat
-        mu = np.maximum(size_factors * np.exp(X @ beta), min_mu)
+        mu = np.maximum(norm_factors * np.exp(X @ beta), min_mu)
         # Compute deviation
         old_dev = dev
         # Replaced deviation with -2 * nll, as in the R code
@@ -436,7 +450,7 @@ def df(beta: np.ndarray) -> np.ndarray:
 
     # Return an UNthresholded mu (as in the R code)
     # Previous quantities are estimated with a threshold though
-    mu = size_factors * np.exp(X @ beta)
+    mu = norm_factors * np.exp(X @ beta)
     return beta, mu, H, converged
 
 
diff --git a/pyproject.toml b/pyproject.toml
index eecde408..ef43aa22 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,6 +3,8 @@ line-length = 89
 
 [tool.ruff]
 line-length = 89
+
+[tool.ruff.lint]
 select = [
     "F",  # Errors detected by Pyflakes
     "E",  # Error detected by Pycodestyle
@@ -29,13 +31,13 @@ ignore = [
     "D213",
 ]
 
-[tool.ruff.isort]
+[tool.ruff.lint.isort]
 force-single-line = true
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "docs/*" = ["I"]
 "tests/*" = ["D"]
 # Ignore errors linked to sphinx gallery syntax
diff --git a/tests/data/pytximport/test_counts.csv b/tests/data/pytximport/test_counts.csv
new file mode 100644
index 00000000..099b7e7d
--- /dev/null
+++ b/tests/data/pytximport/test_counts.csv
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diff --git a/tests/data/pytximport/test_metadata.csv b/tests/data/pytximport/test_metadata.csv
new file mode 100644
index 00000000..e1da0301
--- /dev/null
+++ b/tests/data/pytximport/test_metadata.csv
@@ -0,0 +1,5 @@
+,condition
+SRR16504309_wt,wt
+SRR16504310_wt,wt
+SRR16504311_ko,ko
+SRR16504312_ko,ko
diff --git a/tests/data/pytximport/test_pytximport.h5ad b/tests/data/pytximport/test_pytximport.h5ad
new file mode 100644
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diff --git a/tests/data/pytximport/test_pytximport_length_scaled.h5ad b/tests/data/pytximport/test_pytximport_length_scaled.h5ad
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diff --git a/tests/test_pytximport.py b/tests/test_pytximport.py
new file mode 100644
index 00000000..6696fe98
--- /dev/null
+++ b/tests/test_pytximport.py
@@ -0,0 +1,240 @@
+import os
+from pathlib import Path
+
+import anndata as ad
+import numpy as np
+import pandas as pd
+import pytest
+
+import tests
+from pydeseq2.dds import DeseqDataSet
+from pydeseq2.ds import DeseqStats
+
+
+@pytest.fixture
+def pytximport_adata():
+    """Load test pytximport AnnData object."""
+    test_path = str(Path(os.path.realpath(tests.__file__)).parent.resolve())
+    adata_path = os.path.join(test_path, "data/pytximport/test_pytximport.h5ad")
+    return ad.read_h5ad(adata_path)
+
+
+@pytest.fixture
+def standard_adata():
+    """Create standard AnnData object without pytximport fields."""
+    test_path = str(Path(os.path.realpath(tests.__file__)).parent.resolve())
+
+    counts_path = os.path.join(test_path, "data/pytximport/test_counts.csv")
+    metadata_path = os.path.join(test_path, "data/pytximport/test_metadata.csv")
+
+    counts_df = pd.read_csv(counts_path, index_col=0)
+    metadata_df = pd.read_csv(metadata_path, index_col=0)
+
+    adata = ad.AnnData(X=counts_df.T.values.round(), obs=metadata_df)
+    adata.var_names = counts_df.index
+    adata.obs_names = counts_df.columns
+
+    return adata
+
+
+class TestPytximportDetection:
+    """Test pytximport data detection and validation."""
+
+    def test_explicit_flag(self, pytximport_adata):
+        """Test explicit from_pytximport=True flag."""
+        dds = DeseqDataSet(
+            adata=pytximport_adata, design="~condition", from_pytximport=True
+        )
+
+        # Should have detected pytximport data during initialization
+        assert dds.from_pytximport is True
+
+        dds.fit_size_factors()
+        assert "normalization_factors" in dds.obsm
+        assert dds.obsm["normalization_factors"].shape == (4, 41127)
+
+    def test_no_pytximport_data(self, standard_adata):
+        """Test standard DESeq2 without pytximport data."""
+        dds = DeseqDataSet(adata=standard_adata, design="~condition")
+
+        dds.fit_size_factors()
+        assert "normalization_factors" not in dds.obsm
+        assert "size_factors" in dds.obs
+
+
+class TestPytximportValidation:
+    """Test pytximport data validation."""
+
+    def test_missing_length_matrix(self, standard_adata):
+        """Test validation when length matrix is missing."""
+        with pytest.raises(ValueError, match="'length' not found in obsm"):
+            DeseqDataSet(adata=standard_adata, design="~condition", from_pytximport=True)
+
+    def test_missing_counts_from_abundance(self, pytximport_adata):
+        """Test validation when counts_from_abundance is missing."""
+        # Remove the counts_from_abundance key
+        del pytximport_adata.uns["counts_from_abundance"]
+
+        with pytest.raises(ValueError, match="'counts_from_abundance' not found in uns"):
+            DeseqDataSet(
+                adata=pytximport_adata, design="~condition", from_pytximport=True
+            )
+
+    def test_invalid_counts_from_abundance(self, pytximport_adata):
+        """Test validation when counts are not raw counts."""
+        pytximport_adata.uns["counts_from_abundance"] = "scaledTPM"
+
+        with pytest.raises(ValueError, match="only be used with raw counts"):
+            DeseqDataSet(
+                adata=pytximport_adata, design="~condition", from_pytximport=True
+            )
+
+    def test_length_matrix_shape(self, pytximport_adata):
+        """Test validation of length matrix shape."""
+        # Create a copy to avoid modifying the fixture
+        test_adata = pytximport_adata.copy()
+
+        # Test wrong number of columns (genes) - this should pass AnnData validation
+        # but fail our pytximport validation
+        test_adata.obsm["length"] = np.random.rand(4, 30)  # Wrong n_vars
+
+        with pytest.raises(ValueError, match="Length matrix shape"):
+            DeseqDataSet(adata=test_adata, design="~condition", from_pytximport=True)
+
+    def test_negative_lengths(self, pytximport_adata):
+        """Test validation of non-positive gene lengths."""
+        # Add some negative lengths
+        length_matrix = pytximport_adata.obsm["length"].copy()
+        length_matrix[0, 0] = -1.0
+        pytximport_adata.obsm["length"] = length_matrix
+
+        with pytest.raises(ValueError, match="non-positive values"):
+            DeseqDataSet(
+                adata=pytximport_adata, design="~condition", from_pytximport=True
+            )
+
+
+class TestNormalizationFactors:
+    """Test normalization factor computation and usage."""
+
+    def test_normalization_factor_computation(self, pytximport_adata):
+        """Test that normalization factors are computed correctly."""
+        dds = DeseqDataSet(
+            adata=pytximport_adata,
+            design="~condition",
+            from_pytximport=True,
+        )
+        dds.fit_size_factors()
+
+        # Check normalization factors properties
+        norm_factors = dds.obsm["normalization_factors"]
+        assert norm_factors.shape == (4, 41127)
+        assert np.all(norm_factors > 0)
+
+        # Check that they're different from simple size factors
+        # (due to gene-length correction)
+        size_factors = dds.obs["size_factors"].values
+        norm_factor_means = norm_factors.mean(axis=1)
+
+        # Check that normalization factors vary across genes (not constant)
+        norm_factor_std = np.std(norm_factors, axis=1)
+        assert np.all(norm_factor_std > 0)  # Should have variation across genes
+
+        # Should have some difference from size factors
+        max_diff = np.max(np.abs(norm_factor_means - size_factors))
+        assert max_diff > 0.001  # At least some difference
+
+    def test_normalized_counts(self, pytximport_adata):
+        """Test that normalized counts are computed correctly."""
+        dds = DeseqDataSet(
+            adata=pytximport_adata,
+            design="~condition",
+            from_pytximport=True,
+        )
+        dds.fit_size_factors()
+
+        # Check normalized counts
+        normed_counts = dds.layers["normed_counts"]
+        assert normed_counts.shape == (4, 41127)
+        assert np.all(normed_counts >= 0)
+
+        # Normalized counts should be different from simple division
+        simple_normed = dds.X / dds.obs["size_factors"].values[:, None]
+        assert not np.allclose(normed_counts, simple_normed, rtol=0.01)
+
+    def test_comparison_with_standard_deseq2(self, pytximport_adata, standard_adata):
+        """Compare pytximport and standard DESeq2 normalization."""
+        # pytximport normalization
+        dds_pyxtm = DeseqDataSet(
+            adata=pytximport_adata,
+            design="~condition",
+            from_pytximport=True,
+        )
+        dds_pyxtm.fit_size_factors()
+
+        # Standard normalization
+        dds_std = DeseqDataSet(adata=standard_adata, design="~condition")
+        dds_std.fit_size_factors()
+
+        # The main difference should be in having normalization factors vs not
+        assert "normalization_factors" in dds_pyxtm.obsm
+        assert "normalization_factors" not in dds_std.obsm
+
+        # Both should have size factors
+        assert "size_factors" in dds_pyxtm.obs
+        assert "size_factors" in dds_std.obs
+
+        # Normalization factors should exist and have correct properties
+        norm_factors = dds_pyxtm.obsm["normalization_factors"]
+        assert norm_factors.shape == (4, 41127)
+        assert np.all(norm_factors > 0)
+
+
+class TestDownstreamAnalysis:
+    """Test that downstream analysis works with normalization factors."""
+
+    def test_deseq2_pipeline_with_normalization_factors(self, pytximport_adata):
+        """Test full DESeq2 pipeline with pytximport normalization factors."""
+        dds = DeseqDataSet(
+            adata=pytximport_adata, design="~condition", from_pytximport=True
+        )
+
+        # Run full DESeq2 pipeline - may have convergence warnings with small data
+        with pytest.warns(
+            UserWarning, match="As the residual degrees of freedom is less than 3"
+        ):
+            dds.deseq2()
+
+        # Check that all expected results are computed
+        assert "LFC" in dds.varm
+        assert "dispersions" in dds.var
+        assert "size_factors" in dds.obs
+        assert "normalization_factors" in dds.obsm
+
+        # Test differential expression analysis
+        stat_res = DeseqStats(dds, contrast=["condition", "ko", "wt"])
+        stat_res.summary()
+
+        # Check results
+        assert hasattr(stat_res, "results_df")
+        assert "log2FoldChange" in stat_res.results_df.columns
+        assert "padj" in stat_res.results_df.columns
+
+        # Results should be reasonable
+        results_df = stat_res.results_df
+        assert results_df.shape[0] == 41127  # All genes
+        assert not results_df["log2FoldChange"].isna().all()
+
+    def test_vst_with_normalization_factors(self, pytximport_adata):
+        """Test VST transformation with pytximport normalization factors."""
+        dds = DeseqDataSet(
+            adata=pytximport_adata, design="~condition", from_pytximport=True
+        )
+
+        dds.vst()
+
+        # Check VST results
+        assert "vst_counts" in dds.layers
+        vst_counts = dds.layers["vst_counts"]
+        assert vst_counts.shape == (4, 41127)
+        assert np.all(np.isfinite(vst_counts))

From 5c6b3cd95697a9ea61596e7bbb6b090abe1b4892 Mon Sep 17 00:00:00 2001
From: Malte Kuehl <malte.kuehl@kh-biotechnology.com>
Date: Sat, 20 Sep 2025 10:07:02 +0200
Subject: [PATCH 2/2] Update pytximport link in documentation

---
 docs/source/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index d80592c1..f228556d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -19,7 +19,7 @@ multi-factor analysis (with categorical or continuous factors) using Wald tests,
 optional `apeGLM <https://academic.oup.com/bioinformatics/article/35/12/2084/5159452>`_ LFC shrinkage step
 :cite:p:`zhu2019heavy`. 
 
-PyDESeq2 also supports `pytximport <https://bioconductor.org/packages/release/bioc/html/tximport.html>`_-derived 
+PyDESeq2 also supports `pytximport <https://pytximport.complextissue.com/en/stable/>`_-derived 
 normalization factors :cite:p:`kuehlGeneCountEstimation2024b`, enabling accurate differential expression analysis 
 with transcript-level quantification data from tools like Salmon, Kallisto, or RSEM. When explicitly enabled,
 this feature accounts for gene length differences between samples due to differential isoform usage.