PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (doi:10.7910/DVN/HNUUSJ)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition

doi:10.7910/DVN/HNUUSJ

Harvard Dataverse

2023-12-15

1

Durham, Timothy J; Libbrecht, Maxwell W; Howbert, J Jeffry; Bilmes, Jeff; Noble, William, 2023, "PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition", https://doi.org/10.7910/DVN/HNUUSJ, Harvard Dataverse, V1, UNF:6:uHgP1kOF1LJxepJcrn6AGQ== [fileUNF]

PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition

doi:10.7910/DVN/HNUUSJ

Durham, Timothy J (University of Washington, Department of Genome Sciences)

Libbrecht, Maxwell W (University of Washington, Department of Genome Sciences)

Howbert, J Jeffry (University of Washington, Department of Genome Sciences)

Bilmes, Jeff (University of Washington, Department of Electrical Engineering)

Noble, William (University of Washington, Department of Genome Sciences, Department of Computer Science)

Python

Apache Spark

https://bitbucket.org/noblelab/predictd

R01 ES024917

U41 HG007000

U41 HG007000

U41 HG007000

U41 HG007000

Harvard Dataverse

Noble, William

Noble, William

2023-12-06

https://doi.org/10.7910/DVN/HNUUSJ

Computer and Information Science, Medicine, Health and Life Sciences

This dataset contains training data and the trained model parameters for PREDICTD, a 3D tensor decomposition-based model of the NIH Epigenomics Roadmap project data. The NIH Epigenomics Roadmap consortium was focused on mapping the genomic locations of many different epigenomic marks (for example histone modifications) in many different cell types. However, due to the large number of experiments that it would take to exhaustively produce such maps for all epigenomic marks and all cell types, many possible experiments were missing. The PREDICTD model used 3D tensor decomposition and reconstruction to predict the results of these missing experiments. The training data and model parameters are preserved to allow others to more easily replicate or follow up on the results in the PREDICTD publication (Durham, et al. 2018.).

Serialized Apache Spark Resilient Distributed Dataset (RDD)

Genomics Data Files (e.g. BED)

Serialized Python data structures (pickle format)

Text files

This dataset is paired with the computer code used to train the 3D tensor decomposition model. The code is hosted in the following BitBucket repository: https://bitbucket.org/noblelab/predictd.

• Number of cases: 1031

• No. of variables per record: 3

• Type of File: text/tab-separated-values

UNF:6:uHgP1kOF1LJxepJcrn6AGQ==

• http://egg2.wustl.edu/roadmap/data/byFileType/signal/consolidated/macs2signal/pval/E001-H3K4me1.pval.signal.bigwig - http://egg2.wustl.edu/roadmap/data/byFileType/signal/consolidated/macs2signal/pval/E001-H3K4me1.pval.signal.bigwig
• ES-I3_Cell_Line - ES-I3_Cell_Line
• H3K4me1 - H3K4me1

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