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Benchmarking the impact of data transformation, pre-processing and choice of method in the computational deconvolution of transcriptomics data

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Organization
Abstract
Many computational methods to infer proportions of individual cell types from bulk transcriptomics data have been developed (= computational deconvolution). Attempts comparing these methods revealed that the choice of reference signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing and methodology on the results is still lacking. Using single-cell RNA-sequencing (scRNA-seq) data from human pancreas and PBMCs, we artificially generated hundreds of pseudo-bulk mixtures with varying number of cells and cell types in known proportions, allowing the evaluation of the combined impact on the deconvolution results. Among the methods to perform deconvolution of bulk RNA-seq data we included MuSiC, a method designed to infer the cell type composition of bulk data using scRNA-seq data as reference. Moreover, since most methods require an additional reference matrix containing cell-type specific expression values, we assessed the effect of removing cell types from the reference that were actually present in the mixtures. Further in-depth analyses are currently ongoing.
Keywords
computational deconvolution, transcriptomics

Citation

Please use this url to cite or link to this publication:

MLA
Avila Cobos, Francisco, et al. “Benchmarking the Impact of Data Transformation, Pre-Processing and Choice of Method in the Computational Deconvolution of Transcriptomics Data.” ISMB/ECCB 2019, Abstracts, 2019.
APA
Avila Cobos, F., Alquicira-Hernandez, J., Vandesompele, J., Powell, J., Mestdagh, P., & De Preter, K. (2019). Benchmarking the impact of data transformation, pre-processing and choice of method in the computational deconvolution of transcriptomics data. In ISMB/ECCB 2019, Abstracts. Basel (Switzerland).
Chicago author-date
Avila Cobos, Francisco, José Alquicira-Hernandez, Jo Vandesompele, Joseph Powell, Pieter Mestdagh, and Katleen De Preter. 2019. “Benchmarking the Impact of Data Transformation, Pre-Processing and Choice of Method in the Computational Deconvolution of Transcriptomics Data.” In ISMB/ECCB 2019, Abstracts.
Chicago author-date (all authors)
Avila Cobos, Francisco, José Alquicira-Hernandez, Jo Vandesompele, Joseph Powell, Pieter Mestdagh, and Katleen De Preter. 2019. “Benchmarking the Impact of Data Transformation, Pre-Processing and Choice of Method in the Computational Deconvolution of Transcriptomics Data.” In ISMB/ECCB 2019, Abstracts.
Vancouver
1.
Avila Cobos F, Alquicira-Hernandez J, Vandesompele J, Powell J, Mestdagh P, De Preter K. Benchmarking the impact of data transformation, pre-processing and choice of method in the computational deconvolution of transcriptomics data. In: ISMB/ECCB 2019, Abstracts. 2019.
IEEE
[1]
F. Avila Cobos, J. Alquicira-Hernandez, J. Vandesompele, J. Powell, P. Mestdagh, and K. De Preter, “Benchmarking the impact of data transformation, pre-processing and choice of method in the computational deconvolution of transcriptomics data,” in ISMB/ECCB 2019, Abstracts, Basel (Switzerland), 2019.
@inproceedings{8644571,
  abstract     = {Many computational methods to infer proportions of individual cell types from bulk
transcriptomics data have been developed (= computational deconvolution). Attempts
comparing these methods revealed that the choice of reference signatures is far more
important than the method itself. However, a thorough evaluation of the combined
impact of data transformation, pre-processing and methodology on the results is still
lacking.
Using single-cell RNA-sequencing (scRNA-seq) data from human pancreas and
PBMCs, we artificially generated hundreds of pseudo-bulk mixtures with varying
number of cells and cell types in known proportions, allowing the evaluation of the
combined impact on the deconvolution results. Among the methods to perform
deconvolution of bulk RNA-seq data we included MuSiC, a method designed to infer
the cell type composition of bulk data using scRNA-seq data as reference. Moreover,
since most methods require an additional reference matrix containing cell-type
specific expression values, we assessed the effect of removing cell types from the
reference that were actually present in the mixtures. Further in-depth analyses are
currently ongoing.},
  author       = {Avila Cobos, Francisco and Alquicira-Hernandez, José and Vandesompele, Jo and Powell, Joseph and Mestdagh, Pieter and De Preter, Katleen},
  booktitle    = {ISMB/ECCB 2019, Abstracts},
  keywords     = {computational deconvolution,transcriptomics},
  language     = {eng},
  location     = {Basel (Switzerland)},
  title        = {Benchmarking the impact of data transformation, pre-processing and choice of method in the computational deconvolution of transcriptomics data},
  url          = {https://www.iscb.org/ismbeccb2019},
  year         = {2019},
}