anticor-features 0.2.1

Anti-correlation based feature selection for single cell datasets

README

What is this repository for?

Anti-correlated genes as a method of feature selection

• Unsupervised feature selection for single cell omics (or anything else!) that passes the null-dataset test

How do I get set up?

python3 -m pip install anticor_features

You can also install using the setup.py script in the distribution like so: python3 setup.py install

This should take less than one minute or even seconds if dependecies were already installed.

How do I run use this package?

from anticor_features.anticor_features import get_anti_cor_genes

## Then feed in the expression matrix, with cells in columns, genes in rows
## and the feature names (all_features)
## and the species code (in gProfiler format, linked below)

anti_cor_table = get_anti_cor_genes(in_mat,
                                    all_features,
                                    species="hsapiens")

A list of the gProfiler accepted species codes is listed here: https://biit.cs.ut.ee/gprofiler/page/organism-list

The above call yields a pandas data frame that will give you the collected summary statistics, and let you filter based on the features annotated as "selected" in that column

>>> print(anti_cor_table.head())
     gene  pre_remove_feature  pre_remove_pathway  ...       FDR  num_sig_pos_cor  selected
0    Xkr4               False               False  ...       NaN              NaN       NaN
1     Rp1               False               False  ...       NaN              NaN       NaN
2   Sox17               False               False  ...  0.001883           3406.0      True
3  Mrpl15               False                True  ...       NaN              NaN       NaN
4  Lypla1               False                True  ...       NaN              NaN       NaN

The NaNs are produced where the gene was not assayed for anti-correlations either from pre-filtering (the default is to remove genes in pathways related to mitochondria, ribosomes, and hemoglobin).

If you want to customize which GO terms are removed, or specify specific genes to exclude, you can do that with the pre_remove_features and pre_remove_pathways arguments

anti_cor_table = get_anti_cor_genes(in_mat,
                                    all_features,
                                    species="hsapiens",
                                    pre_remove_features = ["ACTB","MT-COX1"])

Scanpy (or anything from python where you have a matrix)

• If you're using scanpy, then you can use the same basic syntax as above. The only thing worth noting is that our downsampling function assumes that the genes are in rows, and cells are in columns, which is flipped from AnnData's formatting, that's why we use the have the transpose() functions below:

If you follow along Scanpy's tutorial, then the only thing different would be swapping out:

[16]: sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
[17]: sc.pl.highly_variable_genes(adata)
[18]: adata.raw = adata
[19]: adata = adata[:, adata.var.highly_variable]

for

from anticor_features.anticor_features import get_anti_cor_genes

anti_cor_table = get_anti_cor_genes(adata.X.T,
                                    adata.var.index.tolist(),
                                    species="hsapiens")

selected_table = anti_cor_table[anti_cor_table["selected"]==True]
print(selected_table)

## And you can save the anti-correlation dataframe into the adata object as well:
import pandas as pd
adata.var = pd.concat([adata.var,anti_cor_table], axis=1)

## And then we subset the data to only include the selected features!
adata.raw = adata
adata = adata[:, selected_table.index]

## Note that the downstream clusters and marker genes will be slightly different!

It should take ~ 1-2 minute(s) for the feature selection depending on your internet connection, the speed of the gprofiler server (if looking up pathways like ribosomes, mitochondria, etc to remove), but will also scale a bit with the complexity of the dataset. For example, the tabula muris dataset used in the manuscript took ~ 60 minutes, in part because nearly every gene was expressed at appreciable values in a subset of the cells.

• An important note if you're working in a cluster environment ** Anti-correlated genes are selected out of memory - to do this, the pipeline needs a hard-disk area to work in. On a stand-alone computer it'll automatically find the system temp drive, but this might not be the behavior you want on a cluster, or if you're analyzing several datasets simultaneously because they would overwrite each other. ** In those cases, you should supply the additional argument scratch_dir=</local/path/to/dataset/directory>. This ensures that each dataset will be analyzed properly and there won't be conflicts in terms of where files get written.

Command line interface

You can also use this tool at the command line, if you have either a .tsv or an hdf5 file, with the matrix under the key "infile"

python3 -m anticor_features.anticor_features -i exprs.tsv -species mmusculus

or something similar. This outputs the pandas table to a tsv in the same folder as the input file

See the help section for more detailed usage of the command line interface:

python3 -m anticor_features.anticor_features -h

License

This package is available via the AGPLv3 license.

Who do I talk to?

• Repo owner/admin: [e-mail]