Expectation maximization algorithm to decompose complex mixtures of cfDNA into the tissues generating the cfDNA fragments. Required input is methylated cfDNA, either WGBS or Illumina 450K with downsampled read counts, and a reference panel of tissues to estimate their contribution to the cfDNA. CelFiE can estimate an arbitrary number of unknown or missing tissues from your reference that are truly in the cfDNA mixtures.
CelFiE expects the methylation data for a cfDNA individual or reference cell type is in the form of # of methylated reads, # of total reads. For example for one sample, the file could like like:
# CHR START END METH DEPTH
chr1 186917271 186917772 446.0 630.0
chr15 92708070 92708571 71.0 133.0
chr14 55296905 55297406 89.0 115.0
If using the provided TIMs (in data/reference_file_regions.txt) the chrom, start, end would be a 500bp region around the TIM. To obtain the summed reads per sample, the file sum_by_list.py is provided. First, all CpGs within the specified TIM region can be found using bedtools. Then, all reads can be summed using sum_by_list.py. If a region has no coverage, meaning that region is 'missing', sum_by_list.py will return 0 methylated, 0 depth, for that region.
bedtools intersect -a <sample file> -b <data/reference_file_regions.txt" > <output> "
python sum_by_list.py <data/reference_file_regions.txt" > <output> <output summed> 1 # run for one sample
All cfDNA and reference data should then be compiled into one input tab separated file for CelFiE, with one set of bed columns before the sample data, and one set before the reference data. For example, for 2 input individuals and 4 reference cell types would look like
chr1 186917271 186917772 446.0 630.0 230.0 304.0 chr1 186917271 186917772 1156.062 1196.0 3.968 224.0 1172.14 1234.0 782.018 852.0
chr15 92708070 92708571 71.0 133.0 68.0 94.0 chr1 23291168 23291669 341.02 352.0 2.994 87.0 332.996 359.0 295.99 314.0
chr14 55296905 55297406 89.0 115.0 74.0 83.0 chr1 48159309 48159810 168.987 173.0 1.0 39.0 182.98 196.0 94.00 119.0
To prepare CelFiE files from Bismark output, see prepare_bismark.sh
To install CelFiE, clone or fork this repository using git clone https://github.com/christacaggiano/celfie.git. All required packages can be installed using Anaconda using the environment file specified in celfie_conda_env.yml. Run conda env create -f celfie_conda_env.yml -n celfie_env
Full implementation of the EM model at EM/em.py. Code to generate simulations can be found in EM/simulations.
After preparing data as above, the EM script as follows:
python EM/em.py <input_file> <output_directory> <num of cfDNA samples> <max EM iterations> <num of unknown categories> <parallel job ID> <convergence> <num of random restarts per replicate>
python EM/em.py data/sample_data.txt EM/sample_output 15 1000 1 1 0.001 1
Currently, the estimated methylation proportions for the reference and the estimated cell type proportions are output in pickled python numpy arrays. These can be read back into python for further analysis by the following
import pickle as pkl
cell_proportions = pkl.load(open("EM/sample_output/1_alpha.pkl", "rb"))
methylation_proportions = pkl.load(open("EM/sample_output/1_gamma.pkl", "rb"))To run many parallel replicates on a SGE or UGE cluster configuration, see run_real_data.sh
qsub run_real_data.sh
Jupyter notebooks to reproduce figures and statistical analyses for the final version of this manuscript can be found in paper_figures directory.
For any questions with this code, please contact christa@g.ucla.edu
For more details on CelFiE, see:
Christa Caggiano, Barbara Celona, Fleur Garton, Joel Mefford, Brian Black, Catherine Lomen-Hoerth, Andrew Dahl, Noah Zaitlen, "Estimating the rate of cell type degeneration from epigenetic sequencing of cell-free DNA", BioRxiv, Jan 2020, DOI