diff --git a/RNA_Seq_Cecere/RNA-seq.snakefile b/RNA_Seq_Cecere/RNA-seq.snakefile
index fd6421f59e7cd3fd2c727cbd3cd8da3876da0d7a..0ee820e0265319ce50b2971ead25243e39b7149b 100644
--- a/RNA_Seq_Cecere/RNA-seq.snakefile
+++ b/RNA_Seq_Cecere/RNA-seq.snakefile
@@ -1396,7 +1396,9 @@ rule plot_spikein_responses:
     output:
         response_plots = expand(OPJ(aligner, f"mapped_{genome}", "{{counter}}",
             f"all_on_{genome}", "{lib}_{rep}_spike_ins_{{orientation}}_TPM_response.pdf"), lib=LIBS, rep=REPS),
-        # spikein_slope_files = OPJ(aligner, f"mapped_{genome}", "{counter}", f"all_on_{genome}", "spike_ins_{orientation}_TPM_response_slope.txt"),
+        spikein_slope_files = OPJ(
+            aligner, f"mapped_{genome}", "{counter}", f"all_on_{genome}",
+            "spike_ins_{orientation}_TPM_response_slope_and_intercept.txt"),
     run:
         tpm_table = pd.read_table(input.tpm_file, index_col="gene")
         common = tpm_table.index.intersection(spikein_expected_counts.index)
@@ -1410,6 +1412,8 @@ rule plot_spikein_responses:
         #         plot_spikein_response, data, lib,
         #         title="spike-ins TPM response")
         # pp.close()
+        slopes = {}
+        intercepts = {}
         rel_sq_diffs_list = []
         for libname in data.columns[:-1]:
             filename = OPJ(
@@ -1433,6 +1437,14 @@ rule plot_spikein_responses:
                     # intercept supposed to be 0 for spikein response
                     data, "expected_counts", libname, y_min=1, fit_intercept=False, transform="log10")
             rel_sq_diffs_list.append(rel_squared_diffs)
+            slopes[libname] = regline_slope
+            intercepts[libname] = regline_intercept
+        reg_params = pd.concat(
+            [pd.Series(slopes), pd.Series(intercepts)],
+            axis=1)
+        reg_params.columns = ["slope", "intercept"]
+        reg_params.to_csv(output.spikein_slope_files, sep="\t")
+        # TODO (28/10/2022): The following looks like old work in progress:
         all_rel_sq_diffs = pd.concat(rel_sq_diffs_list, axis=1).dropna()
         # print(all_rel_sq_diffs)
         #all_rel_sq_diffs.assign(p_normal=normaltest(all_rel_sq_diffs, axis=1, nan_policy="omit").pvalue)