chronODE: A framework to integrate time-series multi-omics data based on ordinary differential equations combined with machine learning
Beatrice Borsari, Mor Frank, Eve S. Wattenberg, Susanna X. Liu, Xuezhu Yu, Mark Gerstein
bioRxiv preprint
An ODE-based pipeline for interpolating values and derivatives from time-series data, and machine learning models that predicted gene expression from linked chromatin features.
These Nextflow pipelines are written in Nextflow DSL2.
R libraries:
data.table
optparse
Python libraries:
argparse
numpy
pandas
scipy
Usage: nextflow run chronode.nf [options]
Parameters:
--infile Input matrix (should already be fully transformed)
--size Chunk size (affects speed but not final output). We recommend ~10% of the file length, depending on available resources.
--out Prefix for output files
--dir Output directory
--timesfile File of timepoints
Nextflow offers a number of other optional parameters, including --help and -with-trace that may be useful for debugging if errors occur.
The tab-separated main input file needs one row per gene/regulatory element, one index column, and a column for each time point in the original data:
cCRE_id E10.5 E11.5 E12.5 E13.5 E14.5 E15.5 E16.5 PN
EM10D0043278 0.112209163706003 0.263615771996068 0.101058746899128 0.00109066358305005 -0.0733457337036519 -0.155362226485118 -0.130227596077729 -0.103008112890712
EM10D0046746 0.158520542990051 0.207235934578883 0.0882546996858981 0.0342933724159356 -0.0391589358988853 -0.136999257336671 -0.328550015608365 -0.181150203360637
The time course must be specified using a .csv file listing all time points in numeric form on one line:
10.5,11.5,12.5,13.5,14.5,15.5,16.5,21
The parameters output will be tab-separated and have a row for each element and have columns for the k and b parameters fitted to the data, mean squared error (MSE), the range that the data were shifted into for the fitting, and a rescaled value of b based on the original range.
cCRE_id k b MSE range rescaled_b
EM10D2246738 0.120000858009469 239089.428129566 0.0557348329407984 0-1 127612.186705749
EM10D2246742 1.03068354441858 0.886385177421958 0.0237505021001379 0-1 2.88606678854373
The derivatives, fitted values, and rescaled fitted values output files will be tab-separated and have a row for each element and a column for each interpolated timepoint:
cCRE_id 10.5 11.5 12.5 13.5 14.5 15.5 16.5 21.0
EM10D1138540 1.51000458715981 1.43871659514363 1.34337943231759 1.23596323549907 1.13599611554697 1.05821095446409 1.00569653913472 0.937419990935623
EM10D1138541 1.70323032149406 1.40624378601762 1.20345995048573 1.06499817485729 0.970455811708974 0.905901834291343 0.861824070290061 0.783982973335906
nextflow run chronode.nf \
--infile example_input.tsv \
--out forebrain_test \
--size 3 \
--dir example_output/ \
--timesfile mouse.timecourse.csv
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