Fix deseq2

pegasus/pseudo/__init__.py

@@ -1 +1 @@
-from .convenient import markers, write_results_to_excel, volcano
+from .convenient import markers, write_results_to_excel, volcano, get_original_DE_result

pegasus/pseudo/convenient.py

@@ -5,13 +5,13 @@
 import logging
 logger = logging.getLogger(__name__)
 
-from pegasusio import UnimodalData, timer
+from pegasusio import MultimodalData, timer
 from typing import Union, Dict, Optional, Tuple
 
 
 
 def markers(
-    pseudobulk: UnimodalData,
+    pseudobulk: MultimodalData,
     head: int = None,
     de_key: str = "deseq2",
     alpha: float = 0.05,
@@ -23,11 +23,11 @@ def markers(
 
     Parameters
     ----------
-    pseudobulk: ``UnimodalData``
+    pseudobulk: ``MultimodalData``
         Pseudobulk data matrix with rows for cells and columns for genes.
 
     head: ``int``, optional, default: ``None``
-        List only top ``head`` genes. If ``None``, show any DE genes.
+        List only top ``head`` genes. If ``None``, show all DE genes.
 
     de_key: ``str``, optional, default, ``deseq2``
         Keyword of DE result stored in ``data.varm``.
@@ -39,8 +39,8 @@ def markers(
     -------
     results: ``Dict[str, pd.DataFrame]``
         A Python dictionary containing DE results. This dictionary contains two keywords: 'up' and 'down'.
-        'up' refers to up-regulated genes, which should have 'log2FoldChange' > 0.5.
-        'down' refers to down-regulated genes, which should have 'log2FoldChange' < 0.5.
+        'up' refers to up-regulated genes, which should have 'log2FoldChange' > 0.5. The genes are ranked by Wald test statistics.
+        'down' refers to down-regulated genes, which should have 'log2FoldChange' < 0.5. The genes are ranked by Wald test statistics.
 
     Examples
     --------
@@ -53,11 +53,11 @@ def markers(
     df = pd.DataFrame(data=pseudobulk.varm[de_key], index=pseudobulk.var_names)
     idx = df["padj"] <= alpha
 
-    idx_up = idx & (df["log2FoldChange"].values > 0.0)
-    df_up = df.loc[idx_up].sort_values(by="log2FoldChange", ascending=False, inplace=False)
+    idx_up = idx & (df["stat"].values > 0.0)
+    df_up = df.loc[idx_up].sort_values(by="stat", ascending=False, inplace=False)
     res_dict["up"] = pd.DataFrame(df_up if head is None else df_up.iloc[0:head])
-    idx_down = idx & (df["log2FoldChange"].values < 0.0)
-    df_down = df.loc[idx_down].sort_values(by="log2FoldChange", ascending=True, inplace=False)
+    idx_down = idx & (df["stat"].values < 0.0)
+    df_down = df.loc[idx_down].sort_values(by="stat", ascending=True, inplace=False)
     res_dict["down"] = pd.DataFrame(df_down if head is None else df_down.iloc[0:head])
 
     return res_dict
@@ -149,10 +149,11 @@ def add_worksheet(
 
 
 def volcano(
-    pseudobulk: UnimodalData,
+    pseudobulk: MultimodalData,
     de_key: str = "deseq2",
     qval_threshold: float = 0.05,
     log2fc_threshold: float = 1.0,
+    rank_by: str = "log2fc",
     top_n: int = 20,
     panel_size: Optional[Tuple[float, float]] = (6, 4),
     return_fig: Optional[bool] = False,
@@ -164,14 +165,16 @@ def volcano(
     Parameters
     -----------
 
-    pseudobulk: ``UnimodalData`` object.
+    pseudobulk: ``MultimodalData`` object.
         Pseudobulk data matrix.
     de_key: ``str``, optional, default: ``deseq2``
         The varm keyword for DE results. data.varm[de_key] should store the full DE result table.
     qval_threshold: ``float``, optional, default: 0.05.
         Selected FDR rate. A horizontal line indicating this rate will be shown in the figure.
     log2fc_threshold: ``float``, optional, default: 1.0
         Log2 fold change threshold to highlight biologically interesting genes. Two vertical lines representing negative and positive log2 fold change will be shown.
+    rank_by: ``str``, optional, default: ``log2fc``
+        Rank genes by ``rank_by`` metric in the DE results. By default, rank by log2 Fold Change (``"log2fc"``); change to ``"neglog10p"`` if you want to rank genes by p-values.
     top_n: ``int``, optional, default: ``20``
         Number of top DE genes to show names. Genes are ranked by Log2 fold change.
     panel_size: ``Tuple[float, float]``, optional, default: ``(6, 4)``
@@ -258,13 +261,27 @@ def volcano(
     texts = []
 
     idx = np.where(idxsig & (log2fc >= log2fc_threshold))[0]
-    posvec = np.argsort(log2fc[idx])[::-1][0:top_n]
+    if rank_by == "log2fc":
+        posvec = np.argsort(log2fc[idx])[::-1][0:top_n]
+    elif rank_by == "neglog10p":
+        posvec = np.argsort(neglog10p[idx])[::-1][0:top_n]
+    else:
+        import sys
+        logger.error(f"Invalid rank_by key! Must choose from ['log2fc', 'neglog10p']!")
+        sys.exit(-1)
     for pos in posvec:
         gid = idx[pos]
         texts.append(ax.text(log2fc[gid], neglog10p[gid], gene_names[gid], fontsize=5))
 
     idx = np.where(idxsig & (log2fc <= -log2fc_threshold))[0]
-    posvec = np.argsort(log2fc[idx])[0:top_n]
+    if rank_by == "log2fc":
+        posvec = np.argsort(log2fc[idx])[0:top_n]
+    elif rank_by == "neglog10p":
+        posvec = np.argsort(neglog10p[idx])[::-1][0:top_n]
+    else:
+        import sys
+        logger.error(f"Invalid rank_by key! Must choose from ['log2fc', 'neglog10p']!")
+        sys.exit(-1)
     for pos in posvec:
         gid = idx[pos]
         texts.append(ax.text(log2fc[gid], neglog10p[gid], gene_names[gid], fontsize=5))
@@ -273,3 +290,10 @@ def volcano(
     adjust_text(texts, arrowprops=dict(arrowstyle='-', color='k', lw=0.5))
 
     return fig if return_fig else None
+
+
+def get_original_DE_result(
+    data: MultimodalData,
+    de_key: str = "deseq2",
+) -> pd.DataFrame:
+    return pd.DataFrame(data.varm[de_key], index=data.var_names)

pegasus/tools/pseudobulk.py

@@ -33,7 +33,7 @@ def pseudobulk(
     data: Union[MultimodalData, UnimodalData],
     groupby: str,
     attrs: Optional[Union[List[str], str]] = None,
-    mat_key: str = "counts",
+    mat_key: Optional[str] = None,
     condition: Optional[str] = None,
 ) -> MultimodalData:
     """Generate Pseudo-bulk count matrices.
@@ -53,9 +53,9 @@ def pseudobulk(
         Notice that for a categorical attribute, each pseudo-bulk's value is the one of highest frequency among its cells,
         and for a numeric attribute, each pseudo-bulk's value is the mean among its cells.
 
-    mat_key: ``str``, optional, default: ``counts``
+    mat_key: ``str``, optional, default: ``None``
         Specify the single-cell count matrix used for aggregating pseudo-bulk counts:
-        If specified, use the count matrix with key ``mat_key`` from matrices of ``data``; otherwise, default is ``counts``.
+        If specified, use the count matrix with key ``mat_key`` from matrices of ``data``; otherwise, first look for key ``counts``, then for ``raw.X`` if not existing.
 
     condition: ``str``, optional, default: ``None``
         If set, additionally generate pseudo-bulk matrices per condition specified in ``data.obs[condition]``.
@@ -74,9 +74,21 @@ def pseudobulk(
     --------
     >>> pg.pseudobulk(data, groupby="Channel")
     """
+    if mat_key is None:
+        if "counts" in data._unidata.matrices:
+            mat_key = "counts"
+        elif "raw.X" in data._unidata.matrices:
+            mat_key = "raw.X"
+        else:
+            import sys
+            logger.error("No matrix with default key found in data! Please specify an explicit matrix key!")
+            sys.exit(-1)
     X = data.get_matrix(mat_key)
 
-    assert groupby in data.obs.columns, f"Sample key '{groupby}' must exist in data.obs!"
+    if groupby not in data.obs.columns:
+        import sys
+        logger.error(f"Sample key '{groupby}' must exist in data.obs!")
+        sys.exit(-1)
 
     sample_vec = (
         data.obs[groupby]
@@ -96,9 +108,10 @@ def pseudobulk(
         if isinstance(attrs, str):
             attrs = [attrs]
         for attr in attrs:
-            assert (
-                attr in data.obs.columns
-            ), f"Cell attribute key '{attr}' must exist in data.obs!"
+            if attr not in data.obs.columns:
+                import sys
+                logger.error(f"Cell attribute key '{attr}' must exist in data.obs!")
+                sys.exit(-1)
 
     for bulk in bulk_list:
         df_bulk = df_barcode.loc[df_barcode[groupby] == bulk]
@@ -124,9 +137,10 @@ def pseudobulk(
         df_feature["featureid"] = data.var["featureid"]
 
     if condition is not None:
-        assert (
-            condition in data.obs.columns
-        ), f"Condition key '{attr}' must exist in data.obs!"
+        if condition not in data.obs.columns:
+            import sys
+            logger.error(f"Condition key '{attr}' must exist in data.obs!")
+            sys.exit(-1)
 
         cluster_list = data.obs[condition].astype("category").cat.categories
         for cls in cluster_list:
@@ -138,7 +152,7 @@ def pseudobulk(
         barcode_metadata=df_pseudobulk,
         feature_metadata=df_feature,
         matrices=mat_dict,
-        genome=groupby,
+        genome=data.get_genome(),
         modality="pseudobulk",
         cur_matrix="counts",
     )
@@ -148,13 +162,14 @@ def pseudobulk(
 
 @timer(logger=logger)
 def deseq2(
-    pseudobulk: Union[MultimodalData, UnimodalData],
+    pseudobulk: MultimodalData,
     design: str,
     contrast: Tuple[str, str, str],
     backend: str = "pydeseq2",
     de_key: str = "deseq2",
     alpha: float = 0.05,
     compute_all: bool = False,
+    verbose: bool = True,
     n_jobs: int = -1,
 ) -> None:
     """Perform Differential Expression (DE) Analysis using DESeq2 on pseduobulk data.
@@ -185,6 +200,9 @@ def deseq2(
     compute_all: ``bool``, optional, default: ``False``
         If performing DE analysis on all count matrices. By default (``compute_all=False``), only apply DE analysis to the default count matrix ``counts``.
 
+    verbose: ``bool``, optional, default: ``True``
+        If showing DESeq2 status updates during fit. Only works when ``backend="pydeseq2"``.
+
     n_jobs: ``int``, optional, default: ``-1``
         Number of threads to use. If ``-1``, use all physical CPU cores. This only works when ``backend="pydeseq2"`.
 
@@ -204,19 +222,20 @@ def deseq2(
     mat_keys = ['counts'] if not compute_all else pseudobulk.list_keys()
     for mat_key in mat_keys:
         if backend == "pydeseq2":
-            _run_pydeseq2(pseudobulk=pseudobulk, mat_key=mat_key, design_factors=design, contrast=contrast, de_key=de_key, alpha=alpha, n_jobs=n_jobs)
+            _run_pydeseq2(pseudobulk=pseudobulk, mat_key=mat_key, design_factors=design, contrast=contrast, de_key=de_key, alpha=alpha, n_jobs=n_jobs, verbose=verbose)
         else:
             _run_rdeseq2(pseudobulk=pseudobulk, mat_key=mat_key, design=design, contrast=contrast, de_key=de_key, alpha=alpha)
 
 
 def _run_pydeseq2(
-    pseudobulk: Union[MultimodalData, UnimodalData],
+    pseudobulk: MultimodalData,
     mat_key: str,
     design_factors: Union[str, List[str]],
     contrast: Tuple[str, str, str],
     de_key: str,
     alpha: float,
     n_jobs: int,
+    verbose: bool,
 ) -> None:
     try:
         from pydeseq2.dds import DeseqDataSet
@@ -228,10 +247,16 @@ def _run_pydeseq2(
         sys.exit(-1)
 
     if isinstance(design_factors, str):
-        assert design_factors in pseudobulk.obs.columns, f"The design factor {design_factors} does not exist in data.obs!"
+        if design_factors not in pseudobulk.obs.columns:
+            import sys
+            logger.error(f"The design factor {design_factors} does not exist in data.obs!")
+            sys.exit(-1)
     else:
         for factor in design_factors:
-            assert factor in pseudobulk.obs.columns, f"The design factor {factor} does not exist in data.obs!"
+            if factor not in pseudobulk.obs.columns:
+                import sys
+                logger.error(f"The design factor {factor} does not exist in data.obs!")
+                sys.exit(-1)
 
     counts_df = pd.DataFrame(pseudobulk.get_matrix(mat_key), index=pseudobulk.obs_names, columns=pseudobulk.var_names)
     metadata = pseudobulk.obs
@@ -242,19 +267,17 @@ def _run_pydeseq2(
         counts=counts_df,
         metadata=metadata,
         design_factors=design_factors,
-        refit_cooks=True,
         inference=inference,
-        quiet=True,
+        quiet=not verbose,
     )
     dds.deseq2()
 
     stat_res = DeseqStats(
         dds,
         contrast=contrast,
-        cooks_filter=True,
         alpha=alpha,
         inference=inference,
-        quiet=True,
+        quiet=not verbose,
     )
     stat_res.summary()
     res_key = de_key if mat_key == "counts" else mat_key.removesuffix(".X") + "." + de_key
@@ -263,7 +286,7 @@ def _run_pydeseq2(
 
 
 def _run_rdeseq2(
-    pseudobulk: Union[MultimodalData, UnimodalData],
+    pseudobulk: MultimodalData,
     mat_key: str,
     design: str,
     contrast: Tuple[str, str, str],
@@ -293,7 +316,10 @@ def _run_rdeseq2(
         logger.error(text)
         sys.exit(-1)
 
-    assert design.strip().startswith("~"), f"Design '{design}' is not a valid R formula! Valid examples: '~var', '~var1+var2', '~var1+var2+var1:var2'."
+    if not design.strip().startswith("~"):
+        import sys
+        logger.error(f"Design '{design}' is not a valid R formula! Valid examples: '~var', '~var1+var2', '~var1+var2+var1:var2'.")
+        sys.exit(-1)
 
     import math
     to_dataframe = ro.r('function(x) data.frame(x)')