CelFiE with normal lung tissue samples about celfie HOT 5 OPEN

Hi Dhanya and Fayaz, Sorry for the late reply. CelFiE is really designed to be used to fit multiple samples simultaneously. When you fit one sample at once, which seems to be the case with the data that Fayaz provided (I don't know about Dhanya's data), the EM will tend to learn a "custom" unknown for that sample. This makes sense intuitively, because if I was blindly trying to describe one sample without knowing much about the reference (which is an assumption of the model- that our reference tissues aren't perfect since both ENCODE and BLUEPRINT are noisy), then the best I could do to describe a sample is to just describe what I have in front of me.

I have found that CelFiE performs best with more than 10 samples fit at once (see figure 3 of our preprint).

Let me know if that helps to clear things up.

from celfie.

Hi Dhanya,

I apologize for the late reply. I ended up using meth_atlas for deconvolution: https://github.com/nloyfer/meth_atlas

Hope this helps.

Thanks, Fayaz

from celfie.

Dear Fayaz

I was facing the same issue when I tried using celfie for my dataset. With no unknowns, I get >90% as placenta contribution in non-pregnant samples.

I was wondering if you were able to solve your issue and if you tried any other tool for your samples. Appreciate your time!

Thank you
Dhanya

from celfie.

Hi Dhanya and Fayaz, Sorry for the late reply. CelFiE is really designed to be used to fit multiple samples simultaneously. When you fit one sample at once, which seems to be the case with the data that Fayaz provided (I don't know about Dhanya's data), the EM will tend to learn a "custom" unknown for that sample. This makes sense intuitively, because if I was blindly trying to describe one sample without knowing much about the reference (which is an assumption of the model- that our reference tissues aren't perfect since both ENCODE and BLUEPRINT are noisy), then the best I could do to describe a sample is to just describe what I have in front of me.

I have found that CelFiE performs best with more than 10 samples fit at once (see figure 3 of our preprint).

Let me know if that helps to clear things up.

Hi Christina,

Thank you for your reply. We have 16 samples (COVID19, cfDNA, WGBS) and I tried CelFie but, I'm getting the same results as I did with the individual sample i.e. it predicts that most of the cfDNA originates from the "placenta."

I am attaching the results here.

I am also attaching the input data.

One question I had was: are the reference TIMs you provide on hg38? or hg19? Maybe that's causing the issue?

Any help will be appreciated.

Thanks, fs

covid19_samples_reference_file_tims.txt

covid19_cell_proportions.xlsx

from celfie.

Hi Dhanya and Fayaz, Sorry for the late reply. CelFiE is really designed to be used to fit multiple samples simultaneously. When you fit one sample at once, which seems to be the case with the data that Fayaz provided (I don't know about Dhanya's data), the EM will tend to learn a "custom" unknown for that sample. This makes sense intuitively, because if I was blindly trying to describe one sample without knowing much about the reference (which is an assumption of the model- that our reference tissues aren't perfect since both ENCODE and BLUEPRINT are noisy), then the best I could do to describe a sample is to just describe what I have in front of me.
I have found that CelFiE performs best with more than 10 samples fit at once (see figure 3 of our preprint).
Let me know if that helps to clear things up.

Hi Christina,

Thank you for your reply. We have 16 samples (COVID19, cfDNA, WGBS) and I tried CelFie but, I'm getting the same results as I did with the individual sample i.e. it predicts that most of the cfDNA originates from the "placenta."

I am attaching the results here.

I am also attaching the input data.

One question I had was: are the reference TIMs you provide on hg38? or hg19? Maybe that's causing the issue?

Any help will be appreciated.

Thanks, fs

covid19_samples_reference_file_tims.txt

covid19_cell_proportions.xlsx

Hi Christina,

For your reference, this is the command that I used. There is a warning about division by zero at some point in the script but this could be due to no coverage. I changed the number of unknowns from 0 to 20 compared to the previous run.

Again, any help will be appreciated.

Thanks, fs

python /data/NHLBI_BCB/Sean_MethylSeq/10-tissue_of_origin_methylation_project/celfie/EM/em.py \

/data/NHLBI_BCB/Sean_MethylSeq/14_MKJ5249/02_methylseq_analysis_pipeline/02_tissue_of_origin_prediction/04_deconvolution_with_celfie/covid19_samples_reference_file_tims.txt
/data/NHLBI_BCB/Sean_MethylSeq/14_MKJ5249/02_methylseq_analysis_pipeline/02_tissue_of_origin_prediction/04_deconvolution_with_celfie
16
1000
20
1
0.001
100
writing to /data/NHLBI_BCB/Sean_MethylSeq/14_MKJ5249/02_methylseq_analysis_pipeline/02_tissue_of_origin_prediction/04_deconvolution_with_celfie/
finshed reading /data/NHLBI_BCB/Sean_MethylSeq/14_MKJ5249/02_methylseq_analysis_pipeline/02_tissue_of_origin_prediction/04_deconvolution_with_celfie/covid19_samples_reference_file_tims.txt

beginning generation of /data/NHLBI_BCB/Sean_MethylSeq/14_MKJ5249/02_methylseq_analysis_pipeline/02_tissue_of_origin_prediction/04_deconvolution_with_celfie/1_alpha.pkl

/data/NHLBI_BCB/Sean_MethylSeq/10-tissue_of_origin_methylation_project/celfie/EM/em.py:159: RuntimeWarning: invalid value encountered in true_divide
add_pseduocounts(1, np.nan_to_num(y/y_depths), y, y_depths)
/data/NHLBI_BCB/Sean_MethylSeq/10-tissue_of_origin_methylation_project/celfie/EM/em.py:160: RuntimeWarning: invalid value encountered in true_divide
add_pseduocounts(0, np.nan_to_num(y/y_depths), y, y_depths)

from celfie.