Not dropping "duplicates" in join_all_counts.

This ignored the index, and index should be unique if the joining is
made on a careful selection of biotypes.

Degradome-seq/Degradome-seq.snakefile

@@ -362,7 +362,7 @@ rule join_all_counts:
             counts_dir,
             f"all_on_{genome}", "alltypes_{orientation}_counts.txt"),
     run:
-        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables)).drop_duplicates()
+        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables))
         assert len(counts_data.index.unique()) == len(counts_data.index), "Some genes appear several times in the counts table."
         counts_data.index.names = ["gene"]
         counts_data.to_csv(output.counts_table, sep="\t")

PRO-seq/PRO-seq.snakefile

@@ -932,7 +932,7 @@ rule join_all_counts:
     wildcard_constraints:
         biotype = "|".join(JOINED_BIOTYPES)
     run:
-        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables)).drop_duplicates()
+        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables))
         assert len(counts_data.index.unique()) == len(counts_data.index), "Some genes appear several times in the counts table."
         counts_data.index.names = ["gene"]
         counts_data.to_csv(output.counts_table, sep="\t")

RNA_Seq_Cecere/RNA-seq.snakefile

@@ -1539,7 +1539,7 @@ rule join_all_counts:
     wildcard_constraints:
         biotype = "|".join(JOINED_BIOTYPES)
     run:
-        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables)).drop_duplicates()
+        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables))
         assert len(counts_data.index.unique()) == len(counts_data.index), "Some genes appear several times in the counts table."
         counts_data.index.names = ["gene"]
         counts_data.to_csv(output.counts_table, sep="\t")

Ribo-seq/Ribo-seq.snakefile

@@ -674,7 +674,7 @@ rule trim:
         nb_trimmed =  OPJ(data_dir, "trimmed", "{lib}_{rep}_nb_trimmed.txt"),
     #threads: 2
     message:
-        "Trimming adaptor from raw data, deduplicating reads, removing random 5' {trim5}-mers and 3' {trim3}-mers for {wildcards.lib}_{wildcards.rep}."
+        "Trimming adaptor from raw data, removing random 5' {trim5}-mers and 3' {trim3}-mers for {wildcards.lib}_{wildcards.rep}."
     benchmark:
         OPJ(log_dir, "trim", "{lib}_{rep}_benchmark.txt")
     log:
@@ -1253,7 +1253,10 @@ rule join_all_counts:
         counts_table = OPJ(counts_dir,
         "all_{read_type}_on_%s" % genome, "alltypes_{orientation}_counts.txt"),
     run:
-        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables)).drop_duplicates()
+        # !! drop_duplicates() is wrong: doesn't consider the index (at least in pandas < 1.0.0),
+        # so it eliminates rows having the same data pattern. For instance [0, 0, ...] (wich eliminates a lot of entries)
+        # counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables)).drop_duplicates()
+        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables))
         assert len(counts_data.index.unique()) == len(counts_data.index), "Some genes appear several times in the counts table."
         counts_data.index.names = ["gene"]
         counts_data.to_csv(output.counts_table, sep="\t")

small_RNA-seq/small_RNA-seq.snakefile

@@ -1279,7 +1279,7 @@ rule join_all_feature_counts:
         ## debug:
         print("Input:", ", ".join(input))
         ##
-        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables)).drop_duplicates()
+        counts_data = pd.concat((pd.read_table(table, index_col="gene") for table in input.counts_tables))
         assert len(counts_data.index.unique()) == len(counts_data.index), "Some genes appear several times in the counts table."
         counts_data.index.names = ["gene"]
         counts_data.to_csv(output.counts_table, sep="\t")