last updates

README.md

@@ -97,10 +97,12 @@ import imaging_transcriptomics as imt
 my_data = np.ones(41)  # MUST be of size 41 
                        # (corresponds to the regions in left hemisphere of the DK atlas)
 
-analysis = imt.ImagingTranscriptomics(my_data, n_components=1)
-analysis.run()
+analysis = imt.ImagingTranscriptomics(my_data, method="pls", n_components=1,
+                                      regions="cort+sub")
+analysis.run(gsea=False)
 # If instead of running PLS you want to analysze the data with correlation you can run the analysis with:
-analysis.run(method="corr")
+analysis = imt.ImagingTranscriptomics(my_data, method="corr", 
+                                      regions="cort+sub")
 ```
 
 Once completed the results will be part of the `analysis` object and can be accessed with `analysis.gene_results`.

imaging_transcriptomics/pls.py

@@ -141,7 +141,7 @@ def print_table(self):
     def save_results(self, outdir=None):
         """Save the results of the PLS regression.
 
-        :param oudtir: output directory.
+        :param outdir: output directory.
         """
         assert isinstance(self.gene_results.results, PLSGenes)
         for i in range(self.n_components):

imaging_transcriptomics/reporting.py

@@ -14,53 +14,6 @@
 from .transcriptomics import ImagingTranscriptomics
 
 
-class PDF(FPDF):
-    """Class to generate a PDF report for the imaging-transcriptomics script"""
-
-    def header(self):
-        """The header will contain always the title."""
-        self.rect(10, 10, 190, 280)
-        self.line(10, 50, 200, 50)
-        self.set_font("Helvetica", "B", 14)
-        self.cell(
-            w=0, h=15, align="C",
-            txt="Imaging Transcriptomics Analysis Report", ln=True
-        )
-
-    def analysis_info(self, filename, date, filepath):
-        """Info on the analysis performed. Information included are the name of
-        the scan of the input, date of the analysis and the original path of the scan.
-
-        :param filename: name of the scan used for analysis.
-        :param date: date when the analysis was performed.
-        :param filepath: absolute path of the imaging scan used for analysis.
-        """
-        self.set_font("Courier", "", 10)
-        self.cell(w=100, h=8, align="L", txt=f"  Scan Name: {filename}")
-        self.cell(w=100, h=8, align="L", txt=f"  Date: {date}", ln=True)
-        self.cell(w=100, h=10, align="L", txt=f"  File Path: {filepath}")
-
-    def pls_regression(self, path_plots):
-        """Include the plots of the pls components.
-
-        :param path_plots: path where the .png plots are located.
-        """
-        self.ln(20)
-        self.set_font("Helvetica", "BU", 12)
-        self.cell(w=0, h=10, align="L", txt="-PLS Regression")
-        self.ln(10)
-        self.image(Path(path_plots) / "individual_variance.png", x=15, w=120)
-        self.image(Path(path_plots) / "cumulative_variance.png", x=15, w=120)
-
-    def reproducibility_line(self, cli_commands, version):
-        """Create a string with the command used from the command line to run the analysis.
-
-        :param cli_commands: commands given in the cli to run the analysis.
-        :param version: version of the software used to run the analysis.
-        """
-        pass
-
-
 @CheckPath
 def make_folder(path, folder_name: str):
     """Create a folder based on path and name to use.
@@ -121,40 +74,15 @@ def make_plots(path, limit_x, data_y):
     plt.bar(range(1, 16), 100 * data_y, color="sandybrown")
     for index, value in enumerate(data_y):
         plt.text(index + 0.5, 100 * value, "{:.1f}".format(100 * value))
-    plt.xlabel("PLS components")
+    plt.title("Individual variance explained by PLS components")
+    plt.xlabel("PLS component")
     plt.ylabel("Variance (%)")
     plt.savefig(path / "individual_variance.png", dpi=1200)
     plt.close()
     return
 
-
-def create_pdf(filepath, save_dir):
-    """Create a PDF report.
-
-    Creates a report for the imaging transcriptomics analysis with some plots and details of what was run by the user.
-
-    :param filepath: path of the image used for analysis.
-    :param save_dir: path where to save the report (same as where the plots are located).
-    """
-    if not isinstance(filepath, Path):
-        filepath = Path(filepath)
-    analysis_date = datetime.now().strftime("%d-%m-%Y")
-
-    report = PDF(orientation="P", unit="mm", format="A4")
-    report.add_page()
-    report.analysis_info(filename=filepath.name, date=analysis_date,
-                         filepath=filepath)
-    report.pls_regression(path_plots=save_dir)
-    report.output(save_dir / "Report.pdf", "F")
-
-
+"""
 def reorder_data(data):
-    """Reorder the data from the DK convention to the ENIGMA convention for
-    plotting.
-
-    :param np.ndarray data: Data in the Desikan-Killiany convention. This
-    array should contain at least all the ROIs of the left brain hemisphere.
-    """
     # Indexes to match the abagen (DK) order to the enigma
     enigma_index = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                              12, 13, 14, 15, 16, 17, 18, 19, 20,
@@ -196,12 +124,56 @@ def make_surface_plots(data, save_dir):
                   transparent_bg=True
                   )
     plot_subcortical(array_name=data_subcortical,)
-
-
-def make_pdf():
+"""
+
+
+def pls_components(data):
+    """Return a string with the number of components, its cumulative
+    variance and its p value from the analysis."""
+    if not isinstance(data, ImagingTranscriptomics):
+        raise TypeError("The data must be an ImagingTranscriptomics object.")
+    n_components = data.analysis.n_components
+    res_str = "PLS Component: R2 pval "
+    for i in range(n_components):
+        res_str += f"PLS {i+1}: " \
+                   f"{data.analysis.r2[i]:.3f}" \
+                   f" {data.analysis.p_val[i]:.4f}"
+    return res_str
+
+
+def make_pdf(transcriptomics_data, save_dir, name="report", scanname="", ):
+    if not isinstance(transcriptomics_data, ImagingTranscriptomics):
+        raise TypeError("The data must be an ImagingTranscriptomics object.")
+    save_dir = Path(save_dir)
     WIDTH = 210  # mm
     HEIGHT = 297  # mm
     MARGIN = 5  # mm
-    report = PDF(orientation="P", unit="mm", format="A4")
-
-    pass
+    report = FPDF(orientation="P", unit="mm", format="A4")
+    report.set_font("Arial", size=11)
+    report.add_page()
+    # Header
+    report.image(str(Path(__file__).parent / "resources" / "header.png"),
+                 x=0, y=0, w=WIDTH)
+    # Info on the analysis
+    analysis_date = datetime.now().strftime("%d-%m-%Y")
+    report.ln(25)
+    report.cell(WIDTH/2, txt=f"Scan name: {scanname}")
+    report.cell(WIDTH/2, txt=f"Analysis date:{analysis_date}")
+    # PLS plots
+    if transcriptomics_data.method == "pls":
+        report.ln(10)
+        make_plots(save_dir,
+                   transcriptomics_data.analysis.n_components,
+                   transcriptomics_data.analysis.components_var)
+        report.cell(WIDTH, txt="PLS Analysis")
+        report.image(str(save_dir / "individual_variance.png"),
+                     x=MARGIN, y=53, w=WIDTH/2)
+        report.image(str(save_dir / "cumulative_variance.png"),
+                     x=WIDTH/2 + MARGIN, y=53,  w=WIDTH/2)
+        report.ln(90)
+        report.cell(WIDTH, txt=pls_components(transcriptomics_data))
+    # MAKE GENE RESULTS TABLE
+    report.ln(80)
+    report.cell(WIDTH, txt="For the gene results refer to the spreadsheet in "
+                           "the report folder.")
+    report.output(str(save_dir / f"{name}.pdf"), "F")

imaging_transcriptomics/script/imagingtranscriptomics.py

@@ -97,6 +97,11 @@ def main():
                         gene_set=geneset,
                         n_cpu=n_cpu)
     # PLOTTING and PDF creation
+    imt.reporting.make_pdf(transcriptomics_data=transcriptomics,
+                           save_dir=Path(outdir) / f"Imt_{input_name}_"
+                                                   f"{transcriptomics.method}",
+                           name=str(input_name),
+                           scanname=str(infile.name))
 
 
 if __name__ == "__main__":