diff --git a/pf2rnaseq/factorization.py b/pf2rnaseq/factorization.py index 0aff1b5..f26b8da 100644 --- a/pf2rnaseq/factorization.py +++ b/pf2rnaseq/factorization.py @@ -6,6 +6,7 @@ import scipy.sparse as sps from pacmap import PaCMAP from parafac2.parafac2 import parafac2_nd, store_pf2 +from scipy.optimize import minimize from scipy.stats import gmean from sklearn.decomposition import PCA from sklearn.linear_model import LinearRegression @@ -17,14 +18,16 @@ def correct_conditions(X: anndata.AnnData): """Correct the conditions factors by overall read depth. Ensures that weighting is not affected by cell count difference""" sgIndex = X.obs["condition_unique_idxs"] - #sgIndex = X.obs["condition_unique_idxs"].cat.codes + # sgIndex = X.obs["condition_unique_idxs"].cat.codes counts = np.zeros((np.amax(sgIndex) + 1, 1)) min_val = np.min(X.uns["Pf2_A"]) if min_val < 0: # Add the absolute value of the minimum (plus a small epsilon) to make all values positive X.uns["Pf2_A"] = X.uns["Pf2_A"] + abs(min_val) + 1e-10 - print(f"Warning: Found negative values in Pf2_A (min: {min_val:.6f}). Added {abs(min_val) + 1e-10:.6f} to all values.") - + print( + f"Warning: Found negative values in Pf2_A (min: {min_val:.6f}). Added {abs(min_val) + 1e-10:.6f} to all values." + ) + cond_mean = gmean(X.uns["Pf2_A"], axis=1) x_count = X.X.sum(axis=1) @@ -50,13 +53,11 @@ def pf2( ): cupy.cuda.Device(0).use() pf_out, R2X = parafac2_nd( - X, rank=rank, random_state=random_state, tol=tolerance, n_iter_max=500, - ) X = store_pf2(X, pf_out) @@ -197,3 +198,151 @@ def fms_diff_ranks( ) return df + + +def deconvolution_cytokine( + A: np.ndarray, + alpha: float = 0.1, + max_iter: int = 5000, + random_state: int = 1, +) -> tuple[np.ndarray, np.ndarray]: + """ + Decompose cytokine factor matrix: A ≈ W @ H + + This decomposes observed cytokine effects into: + 1. Direct primary effects (H) + 2. Induced effects via other cytokines (W) + + Parameters + ---------- + A : np.ndarray + Input matrix (n_cytokines, n_components) + Example: (91 cytokines, 100 Parafac2 components) + alpha : float + Regularization strength + max_iter : int + Maximum optimization iterations + random_state : int + Random seed + + Returns + ------- + W : np.ndarray + Cytokine interaction matrix (n_cytokines, n_cytokines) + W[i, j] = total contribution of cytokine j to observed effect of i + Diagonal W[i,i] = direct effect of cytokine i + H : np.ndarray + Effect basis matrix (n_cytokines, n_components) + H[:, j] = cytokine effects for component j without indirect contributions + """ + n_cytokines, n_components = A.shape + + np.random.seed(random_state) + + # W initialized as identity, H is original A + W_init = np.eye(n_cytokines) + H_init = A.copy() + + x0 = np.concatenate([W_init.ravel(), H_init.ravel()]) + + print("Cytokine deconvolution:") + print(f" A shape: {A.shape} (cytokines × components)") + print(f" W shape: ({n_cytokines}, {n_cytokines}) (cytokine interactions)") + print(f" H shape: ({n_cytokines}, {n_components}) (effect basis)") + + w_size = n_cytokines * n_cytokines + iteration_counter = [0] + best_loss = [np.inf] + + def objective(x): + W = x[:w_size].reshape(n_cytokines, n_cytokines) + H = x[w_size:].reshape(n_cytokines, n_components) + + # Reconstruction:A ≈ W @ H + + reconstruction = W @ H + mse = np.sum((A - reconstruction) ** 2) + + # Regularization: L1 penalty on both W and H + # Exclude diagonal of W from L1 penalty + l1_W = alpha * np.sum(np.abs(W)) - alpha * np.diag(np.abs(W)).sum() + l1_H = alpha * np.sum(np.abs(H)) + + total_loss = mse + l1_W + l1_H + + iteration_counter[0] += 1 + if total_loss < best_loss[0]: + best_loss[0] = total_loss + + if iteration_counter[0] % 10 == 0: + print( + f" Iter {iteration_counter[0]}: Loss={total_loss:.4f} " + f"(MSE={mse:.4f}, L1_W={l1_W:.4f}, L1_H={l1_H:.4f})" + ) + + return total_loss + + def gradient(x): + W = x[:w_size].reshape(n_cytokines, n_cytokines) + H = x[w_size:].reshape(n_cytokines, n_components) + + # ===== Gradient w.r.t. W ===== + # 1. Reconstruction term: ∂/∂W [||A - WH||²] = 2(error @ H^T), L1 penalty: ∂/∂W [α||W||₁] = α * sign(W) + grad_W = 2 * ((W @ H - A) @ H.T) + alpha * np.sign(W) - np.diag(alpha * np.sign(np.diag(W))) + + # ===== Gradient w.r.t. H ===== + # 1. Reconstruction term: ∂/∂H [||A - WH||²] = 2(W^T @ error), L1 penalty: ∂/∂H [α||H||₁] = α * sign(H) + grad_H = 2 * (W.T @ (W @ H - A)) + alpha * np.sign(H) + + return np.concatenate([grad_W.ravel(), grad_H.ravel()]) + + print("\nStarting optimization...") + + result = minimize( + fun=objective, + x0=x0, + method="L-BFGS-B", + jac=gradient, + options={"maxiter": max_iter, "disp": True}, + ) + + W = result.x[:w_size].reshape(n_cytokines, n_cytokines) + H = result.x[w_size:].reshape(n_cytokines, n_components) + + # Evaluate + + A_recon = W @ H + + recon_error = np.linalg.norm(A - A_recon, "fro") + rel_error = recon_error / np.linalg.norm(A, "fro") + + # Statistics for W + w_sparsity = np.sum(np.abs(W) < 1e-3) / W.size + w_mean = np.abs(W).mean() + w_max = np.abs(W).max() + + # Statistics for H + h_sparsity = np.sum(np.abs(H) < 1e-3) / H.size + h_mean = np.abs(H).mean() + h_max = np.abs(H).max() + + print("\nOptimization complete:") + print(f" Success: {result.success}") + print(f" Iterations: {result.nit}") + print(f" Relative reconstruction error: {rel_error:.4%}") + + print("\n W (cytokine interactions):") + print(f" Shape: {W.shape}") + print(f" Sparsity: {w_sparsity:.2%} (near-zero elements)") + print(f" Mean |W|: {w_mean:.4f}") + print(f" Max |W|: {w_max:.4f}") + print(f" Non-zeros: {np.sum(np.abs(W) > 1e-3)}/{W.size}") + + print("\n H (effect patterns):") + print(f" Shape: {H.shape}") + print(f" Sparsity: {h_sparsity:.2%} (near-zero elements)") + print(f" Mean |H|: {h_mean:.4f}") + print(f" Max |H|: {h_max:.4f}") + print(f" Non-zeros: {np.sum(np.abs(H) > 1e-3)}/{H.size}") + + return W, H diff --git a/pf2rnaseq/figures/commonFuncs/plotFactors.py b/pf2rnaseq/figures/commonFuncs/plotFactors.py index ccacc0d..259b287 100644 --- a/pf2rnaseq/figures/commonFuncs/plotFactors.py +++ b/pf2rnaseq/figures/commonFuncs/plotFactors.py @@ -28,9 +28,9 @@ def plot_condition_factors( X = np.log10(X) X -= np.median(X, axis=0) - X /= np.std(X, axis=0) + X /= np.std(X, axis=0) + 1e-3 + ind = reorder_table(X + 1e-3) - ind = reorder_table(X) X = X[ind] yt = yt.iloc[ind] @@ -67,7 +67,7 @@ def plot_condition_factors( ) ) # add a little legend - ax.legend(handles=legend_elements, bbox_to_anchor=(0, 1.3)) + # ax.legend(handles=legend_elements, bbox_to_anchor=(0, 1.3)) xticks = np.arange(1, X.shape[1] + 1) @@ -584,7 +584,6 @@ def plot_comp_weights( # Add legend for color coding (only if lowest are included) if include_lowest: - legend_elements = [ Patch(facecolor="darkred", label=f"Top {top_n} Highest"), Patch(facecolor="darkblue", label=f"Top {top_n} Lowest"), diff --git a/pf2rnaseq/figures/figureHeiserCompPac.py b/pf2rnaseq/figures/figureHeiserCompPac.py index 97d697d..fb9885b 100644 --- a/pf2rnaseq/figures/figureHeiserCompPac.py +++ b/pf2rnaseq/figures/figureHeiserCompPac.py @@ -2,7 +2,6 @@ Weighted projections per component in PaCMAP and boxplot of cell types """ - import numpy as np from ..factorization import correct_conditions, pf2 diff --git a/pf2rnaseq/figures/figureParseFactorsDeconv.py b/pf2rnaseq/figures/figureParseFactorsDeconv.py new file mode 100644 index 0000000..29c2332 --- /dev/null +++ b/pf2rnaseq/figures/figureParseFactorsDeconv.py @@ -0,0 +1,86 @@ +""" +Parse data: Plotting factors +""" + +import numpy as np +import pandas as pd +import seaborn as sns +from anndata import read_h5ad +from matplotlib import pyplot as plt + +from ..factorization import correct_conditions, deconvolution_cytokine +from .common import getSetup, subplotLabel +from .commonFuncs.plotFactors import ( + plot_condition_factors, +) + + +def samples_only(X) -> pd.DataFrame: + """Obtain samples once only with corresponding observations""" + samples = X.obs + df_samples = samples.drop_duplicates(subset="condition_unique_idxs") + df_samples = df_samples.sort_values("condition_unique_idxs") + return df_samples + + +def makeFigure(): + """Get a list of the axis objects and create a figure.""" + # Get list of axis objects + ax, f = getSetup((22, 15), (1, 3)) + + # Add subplot labels + subplotLabel(ax) + + # Load data + X = read_h5ad("/home/nicoleb/ParsePf2_100_D11_filt.h5ad") + X.uns["Pf2_A"] = correct_conditions(X) + + W, H = deconvolution_cytokine(X.uns["Pf2_A"], alpha=1e-1, max_iter=5000) + + # Get cytokine names in correct order + samples_df = samples_only(X) + + # Create deconvolved version for plotting + X_deconv = X.copy() + X_deconv.uns["Pf2_A"] = H # Use primary effects only + + plot_condition_factors( + X_deconv, + ax[0], + samples_df["cytokine"], + groupConditions=True, + cond="cytokine", + log_scale=False, + ) + ax[0].set_title("Deconvolved matrix (H)", fontsize=12, fontweight="bold") + + plot_condition_factors( + X, + ax[1], + samples_df["cytokine"], + groupConditions=True, + cond="cytokine", + log_scale=False, + ) + ax[1].set_title("Original Effects (A)", fontsize=12, fontweight="bold") + + cytokine_names = samples_df["cytokine"].values + + # Plot 2: W heatmap (primary effects) + sns.heatmap( + W, + ax=ax[2], + cmap="YlOrRd", + robust=True, + square=True, + cbar_kws={"label": "Signaling Strength"}, + xticklabels=cytokine_names, + yticklabels=cytokine_names, + ) + ax[2].set_title("Cytokine Signaling (W)", fontsize=12, fontweight="bold") + ax[2].set_xlabel("Inducing Cytokine →", fontsize=10) + ax[2].set_ylabel("← Induced Cytokine", fontsize=10) + plt.setp(ax[2].get_xticklabels(), rotation=90, ha="center", fontsize=6) + plt.setp(ax[2].get_yticklabels(), rotation=0, fontsize=6) + + return f diff --git a/pf2rnaseq/imports.py b/pf2rnaseq/imports.py index 9eaaf81..2928ba4 100644 --- a/pf2rnaseq/imports.py +++ b/pf2rnaseq/imports.py @@ -92,9 +92,8 @@ def import_Parse(geneThreshold=0.1, doublet=False) -> anndata.AnnData: X = anndata.read_h5ad("/home/nicoleb/Pf2-scRNAseq-1/pf2rnaseq/Parse_Donor11.h5ad") if doublet: doubletDF = pd.read_csv( - path_here / "pf2rnaseq/Data/DN11Doublets.csv.gz", - index_col=0 - ) + path_here / "pf2rnaseq/Data/DN11Doublets.csv.gz", index_col=0 + ) X.obs = X.obs.join(doubletDF.reindex(X.obs.index)) singlet_mask = X.obs["doublet"] == 0 X = X[singlet_mask, :].copy() diff --git a/pf2rnaseq/top_bot_genes_export.py b/pf2rnaseq/top_bot_genes_export.py index bf2434d..9e198eb 100644 --- a/pf2rnaseq/top_bot_genes_export.py +++ b/pf2rnaseq/top_bot_genes_export.py @@ -2,7 +2,6 @@ exports csv of top 30 and bottom 30 genes per component """ - import numpy as np import pandas as pd from anndata import read_h5ad diff --git a/pyproject.toml b/pyproject.toml index b2454b6..e03b379 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,12 +18,12 @@ dependencies = [ "anndata>=0.10.3", "datashader>=0.18", "gseapy>=1.1", - "scanpy @ git+https://github.com/scverse/scanpy.git@c2a7a4b7ec3203121a8d75aa05fbeb602ceecbd4", + "scanpy>=1.10", "pacmap>=0.8", "leidenalg>=0.10.1", "tqdm>=4.66.1", "tlviz>=0.1.1", - "statsmodels>=0.14.1", + "statsmodels>=0.14.4", "dask[dataframe]>=2025", "ipykernel>=6.29.5", "parafac2 @ git+https://github.com/meyer-lab/parafac2.git", @@ 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