diff --git a/src/find_interaction_cluster/community_calibration.py b/src/find_interaction_cluster/community_calibration.py
index eb7cc0e120af6d28192189ddadb53b7f70c58d47..d387f63b034dba5e0ecdd6f5b37bd56e9add2297 100644
--- a/src/find_interaction_cluster/community_calibration.py
+++ b/src/find_interaction_cluster/community_calibration.py
@@ -41,7 +41,7 @@ def write_input(arr_interaction: np.array, outfile: Path, use_weight: bool):
 
 def get_out_name(weight: int, global_weight: int, inflation: float,
                  project: str = "", same_gene=True, feature: str = "exon",
-                 use_weight: bool = False):
+                 use_weight: bool = False, cell_line: str = "ALL"):
     """
     return the output file where the communities are stored.
 
@@ -56,19 +56,22 @@ def get_out_name(weight: int, global_weight: int, inflation: float,
     same gene (True) or not (False) (default False)
     :param feature: The feature we want to analyse (default 'exon')
     :param use_weight: Say if we want to write the weight into the result file.
+    :param cell_line: Interactions are recovered only from project made \
+     on this cell line.
     :return: The file containing communities,
     the input of hiMCL and the output
     """
     w = "weigthed" if use_weight else "unweigthed"
+    cell = f"_{cell_line}" if cell_line != "ALL" else ""
     if global_weight != 0:
         project = f"global-weight-{global_weight}"
     output = ConfigGraph.community_calibration_folder / "community_files" / \
         f"{project}_weight-{weight}_same_gene-{same_gene}_{feature}_" \
-        f"{inflation}_{w}.txt"
+        f"{inflation}{cell}_{w}.txt"
     input_hip = output.parent / "hipMCL_files" / \
             output.name.replace(".txt", "_input.txt")
     output_hip = input_hip.parent / input_hip.name.replace("_input.txt",
-                                                           "output.txt")
+                                                           "_output.txt")
     output.parent.mkdir(exist_ok=True, parents=True)
     input_hip.parent.mkdir(exist_ok=True, parents=True)
     return output, input_hip, output_hip
@@ -77,7 +80,7 @@ def get_out_name(weight: int, global_weight: int, inflation: float,
 
 def get_figname(weight: int, global_weight: int,
                 project: str = "", same_gene=True, feature: str = "exon",
-                use_weight: bool = False):
+                use_weight: bool = False, cell_line: str = "ALL"):
     """
     return the output file where the communities are stored.
 
@@ -91,18 +94,22 @@ def get_figname(weight: int, global_weight: int,
     same gene (True) or not (False) (default False)
     :param feature: The feature we want to analyse (default 'exon')
     :param use_weight: Say if we want to write the weight into the result file.
+    :param cell_line: Interactions are recovered only from project made \
+     on this cell line.
     :return:
     """
     w = "weigthed" if use_weight else "unweigthed"
+    cell = f"_{cell_line}" if cell_line != "ALL" else ""
     if global_weight != 0:
         project = f"global-weight-{global_weight}"
     return ConfigGraph.community_calibration_folder / \
-        f"{project}_weight-{weight}_same_gene-{same_gene}_{feature}_{w}.pdf"
+        f"{project}_weight-{weight}_same_gene-{same_gene}_{feature}" \
+        f"{cell}_{w}.pdf"
 
 
 def community_finder(weight: int, global_weight: int, inflation: float,
                      project: str = "", same_gene=True, feature: str = "exon",
-                     use_weight: bool = False):
+                     use_weight: bool = False, cell_line: str = "ALL"):
     """
     Find communities inside co-localisation between exons found in \
     a ChIA-PET project.
@@ -118,18 +125,19 @@ def community_finder(weight: int, global_weight: int, inflation: float,
     same gene (True) or not (False) (default False)
     :param use_weight: Say if we want to write the weight into the result file.
     :param feature: The feature we want to analyse (default 'exon')
+    :param cell_line: The cell line chosen
     """
     inflation = round(inflation, 2)
     logging.info(f"Working with inflation {inflation}")
     outfile, in_hipmcl, out_hipmcl = get_out_name(
         weight, global_weight, inflation, project, same_gene, feature,
-        use_weight)
+        use_weight, cell_line)
     if outfile.is_file():
         return pd.read_csv(outfile, sep="\t")
     cnx = sqlite3.connect(ConfigGraph.db_file)
     interaction = get_project_colocalisation(cnx, project, weight,
                                              global_weight, same_gene, True,
-                                             level=feature)
+                                             level=feature, cell=cell_line)
     write_input(interaction, in_hipmcl, use_weight)
     graph = create_graph(interaction)
     df, dic_community = find_communities(graph, project, in_hipmcl, out_hipmcl,
@@ -262,18 +270,19 @@ def create_community_size_fig(df_infl: pd.DataFrame, fig_name: Path) -> None:
 def make_calibration(weight: int, global_weight: int, istart: float = 1.1,
                      istop: float = 2.5, istep: float = 0.1, project: str = "",
                      same_gene=True, feature: str = "exon",
-                     use_weight: bool = False, logging_level: str = "INFO"):
+                     use_weight: bool = False, cell_line: str = "ALL",
+                     logging_level: str = "INFO"):
     logging_def(ConfigGraph.output_folder, __file__, logging_level)
     inflations = np.arange(istart, istop + istep, istep)
     list_df = [
         community_finder(weight, global_weight, i, project, same_gene, feature,
-                         use_weight=use_weight)
+                         use_weight=use_weight, cell_line=cell_line)
         for i in inflations
     ]
     df_infl = create_dataframe(list_df, inflations)
     df_size = create_community_size_dataframe(list_df, inflations)
     figname = get_figname(weight, global_weight, project, same_gene, feature,
-                          use_weight)
+                          use_weight, cell_line)
     create_scatter(df_infl, figname)
     create_community_size_fig(df_size, figname.parent /
                               figname.name.replace(".pdf", "_sizes.pdf"))