Adaptations for SDRF file creation from MetabolLights studies mention…

…ed in bigbio/proteomics-sample-metadata#678

Home.py

@@ -13,6 +13,9 @@
 from PIL import Image
 import base64
 import io
+
+max_samples = 350
+
 st.set_page_config(
     page_title="lesSDRF",
     layout="wide",
@@ -105,12 +108,12 @@ def load_data():
 Upload your intermediate SDRF file here:""")
 
 upload_df = st.file_uploader(
-    "Upload intermediate SDRF file", type=["tsv"], accept_multiple_files=False, help='Upload a previously saved SDRF file. It should be in tsv format and should not contain more than 250 samples'
+    "Upload intermediate SDRF file", type=["tsv"], accept_multiple_files=False, help=f'Upload a previously saved SDRF file. It should be in tsv format and should not contain more than {max_samples} samples'
 )
 if upload_df is not None:
     template_df = pd.read_csv(upload_df, sep='\t')
-    if template_df.shape[0]>250:
-        st.error('Too many samples, please upload a maximum of 250 samples')
+    if template_df.shape[0]>max_samples:
+        st.error(f'Too many samples, please upload a maximum of {max_samples}')
     else:
         st.write(template_df)
         st.session_state["template_df"] = template_df
@@ -150,8 +153,8 @@ def load_data():
         #remove trailing and leading spaces
         uploaded_names = [name.strip() for name in uploaded_names]
         filenames.append(uploaded_names)
-    if len(filenames[0]) > 250:
-        st.error('Too many samples, please upload a maximum of 250 samples')
+    if len(filenames[0]) > max_samples:
+        st.error(f'Too many samples, please upload a maximum of {max_samples} samples')
     else:
         st.write(f"Added filenames: {filenames[0]}")
         ## Store filenames in the dataframe

pages/1_1. Mapping_local_metadata.py

@@ -53,7 +53,7 @@ def get_base64_image(image):
     """If you have a local metadata file available, you can use this file to map the data to the required SDRF information. """
 )
 st.markdown(
-    """**Important:** you can upload the file in csv, tsv or xlsx format.  
+    """**Important:** you can upload the file in csv, tsv, txt (tab-separated), or xlsx format.  
 The order of your raw file names should match the order in which you inputted them in the previous step"""
 )
 
@@ -77,14 +77,16 @@ def get_base64_image(image):
                  *The experimental metadata has been generated using lesSDRF and is available through ProteomeXchange with the dataset identifier [PXDxxxxxxx]*""")
 # Ask the user to upload their own metadata file and to map it to the columns of the template file
 metadata_sheet = st.file_uploader(
-    "Upload your local metadata file (.csv, .tsv or .xls)", type=["csv", "tsv", "xlsx"]
+    "Upload your local metadata file (.csv, .tsv, .txt (tab-separated), or .xls)", type=["csv", "tsv", "txt", "xlsx"]
 )
 if metadata_sheet is not None:
     file_extension = metadata_sheet.name.split(".")[-1]
     if file_extension == "csv":
         metadata_df = pd.read_csv(metadata_sheet)
     elif file_extension == "tsv":
         metadata_df = pd.read_csv(metadata_sheet, sep="\t")
+    elif file_extension == "txt":
+        metadata_df = pd.read_csv(metadata_sheet, sep="\t")
     elif file_extension == "xlsx":
         metadata_df = pd.read_excel(metadata_sheet)
     st.write("Your metadata file:")