How to add my own LR pair?
Closed this issue · 4 comments
Great jobs! I try to use my own collected LR pair, but I encounter the following error. How can i solve it? And the format of ids_arr is the same as tutotrial format.Thanks.
InvalidIndexError Traceback (most recent call last)
Cell In[42], line 38
35 print(len(lrs))
37 # Running the analysis #
---> 38 st.tl.cci.run(data, ids_arr,
39 min_spots = 20, #Filter out any LR pairs with no scores for less than min_spots
40 distance=0, # None defaults to spot+immediate neighbours; distance=0 for within-spot mode
41 n_pairs=10000, # Number of random pairs to generate; low as example, recommend ~10,000
42 n_cpus=30, # Number of CPUs for parallel. If None, detects & use all available.
43 )
File ~/miniconda3/envs/stlearn/lib/python3.8/site-packages/stlearn/tools/microenv/cci/analysis.py:337, in run(adata, lrs, min_spots, distance, n_pairs, n_cpus, use_label, adj_method, pval_adj_cutoff, min_expr, save_bg, neg_binom, verbose)
333 return
335 """ Permutation methods generating background per spot, & test lrs in spot.
336 """
--> 337 perform_spot_testing(
338 adata,
339 lr_scores,
340 lrs,
341 n_pairs,
342 neighbours,
343 het_vals,
344 min_expr,
345 adj_method,
346 pval_adj_cutoff,
347 verbose,
348 save_bg=save_bg,
349 neg_binom=neg_binom,
350 )
File ~/miniconda3/envs/stlearn/lib/python3.8/site-packages/stlearn/tools/microenv/cci/permutation.py:59, in perform_spot_testing(adata, lr_scores, lrs, n_pairs, neighbours, het_vals, min_expr, adj_method, pval_adj_cutoff, verbose, save_bg, neg_binom, quantiles)
57 ####### Quantiles to select similar gene to LRs to gen. rand-pairs #######
58 lr_expr = adata[:, lr_genes].to_df()
---> 59 lr_feats = get_lr_features(adata, lr_expr, lrs, quantiles)
60 l_quants = lr_feats.loc[
61 lrs, [col for col in lr_feats.columns if "L_" in col]
62 ].values
63 r_quants = lr_feats.loc[
64 lrs, [col for col in lr_feats.columns if "R_" in col]
65 ].values
File ~/miniconda3/envs/stlearn/lib/python3.8/site-packages/stlearn/tools/microenv/cci/perm_utils.py:325, in get_lr_features(adata, lr_expr, lrs, quantiles)
321 lr_cols = [f"L_{quant}" for quant in quantiles] + [
322 f"R_{quant}" for quant in quantiles
323 ]
324 quant_df = pd.DataFrame(lr_quants, columns=lr_cols, index=lrs)
--> 325 lr_features = pd.concat((lr_features, quant_df), axis=1)
326 adata.uns["lrfeatures"] = lr_features
328 return lr_features
File ~/miniconda3/envs/stlearn/lib/python3.8/site-packages/pandas/core/reshape/concat.py:385, in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
370 copy = False
372 op = _Concatenator(
373 objs,
374 axis=axis,
(...)
382 sort=sort,
383 )
--> 385 return op.get_result()
File ~/miniconda3/envs/stlearn/lib/python3.8/site-packages/pandas/core/reshape/concat.py:612, in _Concatenator.get_result(self)
610 obj_labels = obj.axes[1 - ax]
611 if not new_labels.equals(obj_labels):
--> 612 indexers[ax] = obj_labels.get_indexer(new_labels)
614 mgrs_indexers.append((obj._mgr, indexers))
616 new_data = concatenate_managers(
617 mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
618 )
File ~/miniconda3/envs/stlearn/lib/python3.8/site-packages/pandas/core/indexes/base.py:3731, in Index.get_indexer(self, target, method, limit, tolerance)
3728 self._check_indexing_method(method, limit, tolerance)
3730 if not self._index_as_unique:
-> 3731 raise InvalidIndexError(self._requires_unique_msg)
3733 if len(target) == 0:
3734 return np.array([], dtype=np.intp)
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
Make sure again the format like index, header name should be the same as https://github.com/BiomedicalMachineLearning/stLearn/blob/master/stlearn/tools/microenv/cci/databases/connectomeDB2020_lit.txt
If it still doesn't work, can you send me a header of your csv file (5 lines)? I can check it
Thanks for your reply.My code and input file are as follows:
df = pd.read_csv('data.csv')
ids_arr =df['x']
ids_arr = ids_arr.astype(object)
data.csv
Can you try this ids_arr=ids_arr.values?
Also you could try to clean all NaN or infinity values in your array
Thanks for your reply! It works now. I will close this issue.