Dataset Information

Ovarian cancer imputation analysis, LC MS/MS

ABSTRACT: Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set of clinical ovarian cancer patient samples.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Ovarian Cancer Cell

SUBMITTER: Kruttika Dabke

LAB HEAD: Sarah Parker

PROVIDER: PXD023040 | Pride | 2021-05-07

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	CORE_SP_200514_DIA_S100.mzML	Mzml
	CORE_SP_200514_DIA_S100.raw	Raw
	CORE_SP_200514_DIA_S102.mzML	Mzml
	CORE_SP_200514_DIA_S102.raw	Raw
	CORE_SP_200514_DIA_S12.raw	Raw

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Publications

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.

Dabke Kruttika K Kreimer Simion S Jones Michelle R MR Parker Sarah J SJ

Journal of proteome research 20210503 6

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have ...[more]

PMID: 33939434

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