ggiecold-zz/eclair

Robust and scalable inference of cell lineages via consensus clustering. Features novel algorithms for the comparison of weighted graphs and unrooted trees.

eclair's Issues

Error on Toy data

Hi!

I tried running this toy data X_krumsiek11.txt, which stores 640 observations of 11 features. The data forms a clearly tree-like manifold that I would expect ECLAIR to resolve.

ECLAIR though seems not to be able to treat this. Any help on the terminal outputs below is appreciated.

Cheers,
Alex

Terminal output (1): Running the interactive interface produced the error at the very end of terminal output below.

(py27) Alexs-MacBook-Pro:comparison_eclair alexwolf$ python -m ECLAIR.Build_instance
*****************************************
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***             ECLAIR                ***
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*****************************************


ECLAIR: provide the path to the file holding the data to be analyzed:
./X_krumsiek11.txt                          

ECLAIR: how may rows count as header in this file? Enter '0' if the file is not adorned by any header:
1

ECLAIR: which column of the data-file holds the names, tags or IDs of its samples? Enter '0' for the 1st column, '1' for the second, etc.:
0

ECLAIR: does this data-set include some time information? [Y/n] 
n

ECLAIR: you may choose to exclude some columns as features. If this option does not apply, simply press 'Enter'. Otherwise, provide a list of numbers:


ECLAIR: please give an estimate of the number of samples in this data-set:
600

ECLAIR: please enter the number of trees that will be bagged into a forest (a value of '50' is recommended):
50

ECLAIR: how many points do you want to sample from the dataset? Please provide a fraction of the total number of cells:
0.8

ECLAIR: choose the clustering algorithm to be applied to each of 50 subsamples from your data-set.
Available methods: affinity propagation (1), DBSCAN (2), hierarchical clustering (3) & k-means (4)

4

ECLAIR: how many centroids to generate for each run of k-means clustering?
n

ECLAIR: invalid entry; please correct by providing a positive integer:
5

ECLAIR: the total number of consensus clusters defaults to the highest number of clusters encountered in each of the 50 independent runs of subsamplings and clusterings. Do you want to provide a value instead? [Y/n]
n



ECLAIR	 INFO	 2017-09-14 10:33:50: ready to proceed!




ERROR: Density_Sampling: density_sampling: 'desired_samples' has been assigned a value of 512, larger than 494, the number of samples whose local densities are high enough (i.e. excluded are the local densities in the lowest 0.01 percentile).

Terminal output (2): Running the file using the command-line interface produced the following the error, even though the file correctly formatted,

(py27) Alexs-MacBook-Pro:comparison_eclair alexwolf$ python -m ECLAIR.Build_instance X_krumsiek11.txt 
Traceback (most recent call last):
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/site-packages/ECLAIR/Build_instance/__main__.py", line 624, in <module>
    main()
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/site-packages/ECLAIR/Build_instance/__main__.py", line 582, in main
    opts, args = parse_options()
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/site-packages/ECLAIR/Build_instance/__main__.py", line 452, in parse_options
    type = 'list', help = ("Specifies which features to remove, "
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/optparse.py", line 1013, in add_option
    option = self.option_class(*args, **kwargs)
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/optparse.py", line 578, in __init__
    checker(self)
  File "/Users/alexwolf/miniconda3/envs/py27/lib/python2.7/optparse.py", line 661, in _check_type
    raise OptionError("invalid option type: %r" % self.type, self)
optparse.OptionError: option -e/--excluded_columns: invalid option type: 'list'

Reproducing Figure 3 and 4 from the original paper

Hi!

I wanted to reproduce Figure 3 and 4 from the Nucleic Acids Research paper but I'm struggling already at finding and providing the data in the correct form.

Is it possible to generate a minimal example where you reproduce these figures and provide the data files or links to the data files?

Thank you,
Alex

PS: Actually, I didn't find a single usage example that allows to quickly reproduce a tree from a given dataset.