add TranscriptGenerator class, call in main function to sample transcripts

tsg/main.py

 import logging
 
+import numpy as np
 import pandas as pd
 
 LOG = logging.getLogger(__name__)
@@ -162,6 +163,115 @@ class Gtf:
     def pick_transcript(self, transcript_id: str) -> pd.DataFrame:
         return self.df.query(f"transcript_id == '{transcript_id}'")
 
+
+class TranscriptGenerator:
+    def __init__(
+        self,
+        transcript_id: str,
+        transcript_count: int,
+        transcript_df: pd.DataFrame,
+        prob_inclusion: float,
+    ):
+        assert len(transcript_df) > 0
+        assert transcript_count > 0
+        assert (prob_inclusion >= 0) and (prob_inclusion <= 1)
+
+        self.id = transcript_id
+        self.count = transcript_count
+        self.df = transcript_df
+        self.no_exons = len(transcript_df)
+        self.strand = self.df["strand"].unique().item()
+        self.prob_inclusion = prob_inclusion
+
+    def _get_inclusions(self) -> np.array:
+        """Generate inclusions array where each column corresponds to one sample and the number of columns corresponds to the number of samples.
+
+        Returns:
+            np.array: inclusions, where True means intron inclusion
+        """
+        inclusion_arr = np.random.rand(self.no_exons, self.count) < self.prob_inclusion
+        if self.strand == "+":
+            inclusion_arr[-1, :] = False
+        elif self.strand == "-":
+            inclusion_arr[-1, :] = False
+
+        return inclusion_arr
+
+    def _get_unique_inclusions(self) -> (list, np.array, np.array):
+        inclusion_arr = self._get_inclusions()
+        # Unique intron inclusion arrays and counts
+        inclusion_arr_unique, counts = np.unique(
+            inclusion_arr, axis=1, return_counts=True
+        )
+        # Name for each generated transcript
+        names = []
+        for i in range(inclusion_arr_unique.shape[1]):
+            if np.all(inclusion_arr_unique[:, i] == False, axis=0):
+                names.append(self.id)
+            else:
+                names.append(f"{self.id}_{i}")
+
+        return names, inclusion_arr_unique, counts
+
+    def _get_df(self, inclusions: np.array, transcript_id: str) -> pd.DataFrame:
+        """Take as input a dataframe filtered to one transcript and a boolean vector denoting intron inclusions.
+
+        Args:
+            inclusions (np.array): boolean vector denoting intron inclusion
+            transcript_id (str): transcript id
+
+        Returns:
+            pd.DataFrame: Derived dataframe
+        """
+        df_generated = self.df.copy()
+        if self.strand == "+":
+            origninal_end = df_generated["end"]
+            df_generated["end"] = np.where(
+                inclusions, df_generated["start"].shift(periods=-1, fill_value=-1) - 1, origninal_end
+            )
+        if self.strand == "-":
+            origninal_start = df_generated["start"]
+            df_generated["start"] = np.where(
+                inclusions, df_generated["end"].shift(periods=-1, fill_value=-1) + 1, origninal_start
+            )
+
+        original_id = df_generated["exon_id"]
+        df_generated["exon_id"] = np.where(
+            inclusions,
+            df_generated["exon_id"] + "_" + np.arange(len(df_generated)).astype(str),
+            original_id,
+        )
+
+        df_generated["transcript_id"] = transcript_id
+        return df_generated
+
+    def generate_transcripts(self, filename: str) -> None:
+        """Write transcripts to file.
+
+        Args:
+            filename (str): Output csv filename
+        """
+        ids, inclusions, counts = self._get_unique_inclusions()
+        with open(filename, "a") as fh:
+            for transcript_id, transcript_count in zip(ids, counts):
+                fh.write(f"{transcript_id},{transcript_count}\n")
+
+    def generate_annotations(self, filename: str) -> None:
+        ids, inclusions, counts = self._get_unique_inclusions()
+        n_unique = len(ids)
+
+        try:
+            df = pd.concat(
+                [self._get_df(inclusions[:, i], ids[i]) for i in range(n_unique)]
+            )
+            df = reverse_parse_free_text(df)
+
+            write_gtf(df, filename)
+            LOG.info(f"Transcript {self.id} sampled")
+        except ValueError:
+            LOG.error(f"Transcript {self.id} could not be sampled.")
+
+
 def sample_transcripts(
     input_transcripts_file: str,
     input_annotations_file: str,
@@ -174,3 +284,20 @@ def sample_transcripts(
     annotations = Gtf()
     annotations.read_file(input_annotations_file)
     annotations.parse_free_text()
+
+    # Set up output file, write header once and append data in loop
+    write_header(output_annotations_file)
+
+    for _, row in transcripts.iterrows():
+        transcript_id = row["id"]
+        transcript_count = row["count"]
+
+        transcript_df = annotations.pick_transcript(transcript_id)
+        transcripts = TranscriptGenerator(
+            transcript_id,
+            transcript_count,
+            transcript_df,
+            prob_inclusion=prob_inclusion,
+        )
+        transcripts.generate_annotations(output_annotations_file)
+        transcripts.generate_transcripts(output_transcripts_file)