bence-szalai / data-preparation-for-ctd2 Goto Github PK

#for compunds where inhibitor,blocker,antagonist etc. are in the MoA columns
#we assume they are inhibitory compounds, so we will mark them with -1 in the meta_matrix
# for other compounds, we assume they are activators, we will mark them with +1
#this is probably not a perfect way to access inhibitory/acovatory state, but good for a first try
inhibitory_words=set(['inhibitor','blocker','antagonist','inihibitor']) #inihibitor is just a typo
for i in drug_metadata.index:
    if list(drug_metadata.index).index(i) % 100==0:
        print('Done for %i drugs' %list(drug_metadata.index).index(i))
    brd=drug_metadata.loc[i,'broad_id']
    if not pd.isnull(drug_metadata.loc[i,'moa']):
        moas=drug_metadata.loc[i,'moa'].split('|')
    else:
        moas=[]
    if not pd.isnull(drug_metadata.loc[i,'target']):
        s=1
        targets=drug_metadata.loc[i,'target'].split('|')
        if len(set((' '.join(moas)).split())&inhibitory_words)>0:
            s=-1
    else:
        targets=[]
    meta_matrix.loc[brd,moas]=1
    meta_matrix.loc[brd,targets]=s

Done for 0 drugs
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-29-ff5ed4cb33f5> in <module>()
     20         targets=[]
     21     meta_matrix.loc[brd,moas]=1
---> 22     meta_matrix.loc[brd,targets]=s

NameError: name 's' is not defined

for i in drug_metadata.index:
    if list(drug_metadata.index).index(i) % 100==0:
        print('Done for %i drugs' %list(drug_metadata.index).index(i))
    brd=drug_metadata.loc[i,'broad_id']
    if not pd.isnull(drug_metadata.loc[i,'moa']):
        moas=drug_metadata.loc[i,'moa'].split('|')
    else:
        moas=[]
    if not pd.isnull(drug_metadata.loc[i,'target']):
        s=1
        targets=drug_metadata.loc[i,'target'].split('|')
        if len(set((' '.join(moas)).split())&inhibitory_words)>0:
            s=-1
    else:
        s=0
        targets=[]
    meta_matrix.loc[brd,moas]=1
    meta_matrix.loc[brd,targets]=s

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-7-929255b434da> in <module>()
      4 #cid is the sample ides, while rid is the gene ids
      5 expression=parse('../data/GSE92742/GSE92742_Broad_LINCS_Level5_COMPZ.MODZ_n473647x12328.gctx',
----> 6                  cid=samples,rid=gene_ids.index).data_df.T[gene_ids.index]
      7 expression.head()
      8 #so here the rows are the samples where the given genes were knocked down

/home/ec2-user/anaconda3/lib/python3.5/site-packages/cmapPy/pandasGEXpress/parse.py in parse(file_path, convert_neg_666, rid, cid, ridx, cidx, row_meta_only, col_meta_only, make_multiindex)
     60     elif file_path.endswith(".gctx"):
     61         curr = parse_gctx.parse(file_path, convert_neg_666, rid, cid, ridx, cidx, row_meta_only, col_meta_only,
---> 62                                 make_multiindex)
     63     else:
     64         err_msg = "File to parse must be .gct or .gctx!"

/home/ec2-user/anaconda3/lib/python3.5/site-packages/cmapPy/pandasGEXpress/parse_gctx.py in parse(gctx_file_path, convert_neg_666, rid, cid, ridx, cidx, row_meta_only, col_meta_only, make_multiindex)
    101 
    102         # validate optional input ids & get indexes to subset by
--> 103         (sorted_ridx, sorted_cidx) = check_and_order_id_inputs(rid, ridx, cid, cidx, row_meta, col_meta)
    104 
    105         data_dset = gctx_file[data_node]

/home/ec2-user/anaconda3/lib/python3.5/site-packages/cmapPy/pandasGEXpress/parse_gctx.py in check_and_order_id_inputs(rid, ridx, cid, cidx, row_meta_df, col_meta_df)
    137     (col_type, col_ids) = check_id_idx_exclusivity(cid, cidx)
    138 
--> 139     row_ids = check_and_convert_ids(row_type, row_ids, row_meta_df)
    140     ordered_ridx = get_ordered_idx(row_type, row_ids, row_meta_df)
    141 

/home/ec2-user/anaconda3/lib/python3.5/site-packages/cmapPy/pandasGEXpress/parse_gctx.py in check_and_convert_ids(id_type, id_list, meta_df)
    173         if id_type == "id":
    174             id_list = convert_ids_to_meta_type(id_list, meta_df)
--> 175             check_id_validity(id_list, meta_df)
    176         else:
    177             check_idx_validity(id_list, meta_df)

/home/ec2-user/anaconda3/lib/python3.5/site-packages/cmapPy/pandasGEXpress/parse_gctx.py in check_id_validity(id_list, meta_df)
    189             mismatch_ids)
    190         logger.error(msg)
--> 191         raise Exception("parse_gctx check_id_validity " + msg)
    192 
    193 

Exception: parse_gctx check_id_validity some of the ids being used to subset the data are not present in the metadata for the file being parsed - mismatch_ids:  {'10610', '7874', '4776', '1019', '256364', '8678', '9695', '4851', '5547', '9897', '9833', '2017', '7077', '64080', '5211', '8440', '5699', '3775', '7867', '5289', '6804', '9275', '9134', '79143', '9961', '11044', '10606', '958', '2264', '23131', '56997', '11031', '9688', '22887', '25874', '965', '2065', '4172', '2817', '79071', '1870', '80212', '57048', '54623', '80347', '22934', '8553', '1052', '7020', '1027', '9375', '960', '9221', '16', '3157', '142', '54499', '6195', '50865', '29763', '10123', '9133', '55111', '11232', '1277', '79961', '29978', '4860', '51203', '10057', '50814', '9928', '65123', '8312', '1514', '4836', '6304', '26064', '7398', '9170', '4067', '9053', '388650', '2954', '25966', '6117', '4482', '5927', '9552', '1017', '4331', '23325', '4313', '9097', '3909', '5110', '64422', '29890', '3162', '5716', '1956', '65057', '7319', '4927', '90861', '9801', '843', '5831', '10810', '51382', '51097', '4609', '670', '27032', '26036', '84722', '8985', '3122', '22883', '26511', '10221', '54681', '10206', '30836', '94239', '27244', '147179', '994', '9650', '7750', '10298', '9455', '54386', '2956', '23588', '30001', '51116', '9903', '79170', '23536', '596', '780', '808', '51599', '7168', '10845', '27242', '23212', '5058', '2146', '60528', '332', '9702', '7159', '29916', '22827', '10494', '868', '11098', '7538', '55608', '2736', '9448', '3566', '79094', '3098', '11230', '8884', '4232', '25839', '25932', '23300', '10190', '55127', '54807', '5440', '2961', '55746', '1029', '8349', '3251', '4846', '5331', '23326', '79080', '4312', '5359', '7416', '9761', '6659', '355', '7849', '2058', '9813', '10904', '2852', '5641', '11014', '2597', '1861', '8727', '11073', '10682', '22796', '3300', '3303', '5154', '8573', '11168', '3597', '55011', '5373', '5613', '5607', '58497', '6347', '51021', '9641', '27336', '51742', 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