SCCAF 0.0.3

Single-Cell Clustering Assessment Framework

SCCAF: Single Cell Clustering Assessment Framework

Single Cell Clustering Assessment Framework (SCCAF) is a novel method for automated identification of putative cell types from single cell RNA-seq (scRNA-seq) data. By iteratively applying clustering and a machine learning approach to gene expression profiles of a given set of cells, SCCAF simultaneously identifies distinct cell groups and a weighted list of feature genes for each group. The feature genes, which are overexpressed in the particular cell group, jointly discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterised by the feature genes as markers.

Requirements

This package requires Python 3 and pip3 for installation, which will take care of dependencies.

Installation

You can install SCCAF with pip:

pip install sccaf

Alternatively, for the latest version, clone this repo and go into its directory, then execute pip3 install .:

git clone https://github.com/SCCAF/sccaf
cd sccaf
# you might want to create a virtualenv for SCCAF before installing
pip3 install .

if your python environment is configured for python 3, then you should be able to replace python3 for just python (although pip3 needs to be kept). In time this will be simplified by a simple pip call.

Usage within Python environment

Use with pre-clustered anndata object in the SCANPY package

The main method of SCCAF can be applied directly to an anndata (AnnData is the main data format used by Scanpy) object in Python.

Before applying SCCAF, please make sure the doublets have been excluded and the batch effect has been effectively regressed.

Assessment of the quality of a clustering

Given a clustering stored in an anndata object adata under the key louvain, we would like to understand the quality (discrimination between clusters) with SCCAF:

from SCCAF import SCCAF_assessment, plot_roc
import scanpy as sc

adata = sc.read("path-to-clusterised-and-umapped-anndata-file")
y_prob, y_pred, y_test, clf, cvsm, acc = SCCAF_assessment(adata.X, adata.obs['louvain'], n=100)

returned accuracy is in the acc variable.

The ROC curve can be plotted:

import matplotlib.pyplot as plt

plot_roc(y_prob, y_test, clf, cvsm=cvsm, acc=acc)
plt.show()

Higher accuracy indicate better discrimination. And the ROC curve shows the problematic clusters.

Optimize an over-clustering

Given an over-clustered result, SCCAF optimize the clustering by merging the cell clusters that cannot be discriminated by machine learning:

# The batch effect MUST be regressed before applying SCCAF
adata = sc.read("path-to-clusterised-and-umapped-anndata-file")

# An initial over-clustering needs to be assigned in consistent with the prefix for the optimization.
# i.e., the optimization prefix is `L2`, the starting point of the optimization of `%s_Round0`%prefix, which is `L2_Round0`.

sc.tl.louvain(adata, resolution=1.5, key_added='L2_Round0')
# i.e., we aim to achieve an accuracy >90% for the whole dataset, optimize based on the PCA space:
SCCAF_optimize_all(ad=adata, plot=False, min_acc=0.9, prefix = 'L2', use='pca')

in the above run, all changes will be left on the adata anndata object and no plots will be generated. If you want to see the plots (blocking the progress until you close them) then remove the plots=False.

Within the anndata object, assignments of cells to clusters will be left in adata.obs['<prefix>_Round<roundNumber>'].

Usage from the command line

We have added convenience methods to use from the command line argument in the shell. This facilitate as well the inclusion in workflow systems.

Optimisation and general purpose usage

Given an annData dataset with louvain clustering pre-calculated (and batch corrected if needed):

sccaf -i <ann-data-input-file> --optimise --skip-assessment -s louvain -a 0.89 -c 8 --produce-rounds-summary

this will leave the result in new file named output.h5, which could be set via -o. In the current setting this will produce a file named rounds.txt with the name of all optimisation rounds left in the output. This file is used for later parallelisation (among different machines) of an assessment process to determine the step to choose as final clustering.

To understand all options, simply execute sccaf --help.

Parallel run of assessments

Once the optimisation has taken place, an strategy to choose the round to be used as final result is to observe the distribution of accuracies for each on multiple iterations of the assessment process. How the process is distributed is a matter of implementation of the local HPC or cloud system. Essentially, the process that can be repeated, per each round, is:

round=<name-of-the-round-in-the-output>
sccaf-asses -i output.h5 -o results/sccaf_assess_$round.txt --slot-for-existing-clustering $round --iterations 20 --cores 8

running the above for a number of different rounds will leave files in the results folder.

Merging parallel runs to produce plot

Once all assessment runs are done, the merging and plotting step can be run:

sccaf-assess-merger -i results -r rounds.txt -o rounds-acc-comparison-plot.png

This will produce a result like this: