Proteomics

Dataset Information

0

DDA-PASEF and diaPASEF acquired HCC1806/HS578T proteomic datasets with deliberate batch effects

ABSTRACT: We generated two comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent and Data-Indepentdent Acquisition modes. This dataset contain a balanced two-class design (cell lines: HCC1806 vs HS578T), allowing for investigating mixed effects from class, batch and acquisition method. Investigators can also compare and integrate DDA and DIA platforms, delve into the various patterns and mechanisms of missing values, benchmark batch effects correction algorithms and assess confounding between different technical issues.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: HE WANG  

LAB HEAD: Wilson Goh

PROVIDER: PXD041391 | Pride | 2023-11-21

REPOSITORIES: Pride

Json Xml

Dataset's files

Source:
Action DRS
CAD20220628wucl_WOSP00101_DDA_60min_HCC1806_rep1_1_Slot2-1_1_6902.d.zip Other
CAD20220628wucl_WOSP00101_DDA_60min_HCC1806_rep1_2_Slot2-1_1_6903.d.zip Other
CAD20220628wucl_WOSP00101_DDA_60min_HCC1806_rep1_3_Slot2-1_1_6904.d.zip Other
CAD20220628wucl_WOSP00101_DDA_60min_HCC1806_rep2_1_Slot2-3_1_6906.d.zip Other
CAD20220628wucl_WOSP00101_DDA_60min_HCC1806_rep2_2_Slot2-3_1_6907.d.zip Other
Items per page:
1 - 5 of 77
altmetric image

Publications

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects.

Wang He H   Lim Kai Peng KP   Kong Weijia W   Gao Huanhuan H   Wong Bertrand Jern Han BJH   Phua Ser Xian SX   Guo Tiannan T   Goh Wilson Wen Bin WWB  

Scientific data 20231202 1

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But t  ...[more]

Similar Datasets

Proteomics DDA-PASEF and diaPASEF acquired A549/K562 proteomic datasets with deliberate batch effects
2023-11-21 | PXD041421 | Pride
Proteomics Comparative DDA and DIA-PASEF proteomics profiling of cross-kingdom macrophage infection reveals new mechanisms driving pathogen virulence
2024-08-13 | PXD054527 | Pride
Proteomics The GlycoPaSER prototype as a real-time N-glycopeptide identification tool based on the PaSER parallel computing platform
2023-03-13 | PXD040716 | Pride
Proteomics Proteomic characterization of 1,000 human and murine neutrophils freshly isolated from blood and sites of sterile inflammation
2024-10-11 | PXD050682 | Pride
Proteomics Analysis of cryopreservation of human granulosa cells
2021-07-28 | PXD025632 | Pride
Proteomics,Multiomics Analysis of innate immune antagonism by SARS-CoV-2 reveals options for therapeutic immune modulation and differences to SARS-CoV
2021-06-23 | PXD021899 | Pride
Proteomics PIP2 promotes MLKL-derived pathogenic process through enhancing its channel activity
2022-11-05 | PXD030814 | Pride
Proteomics Heart LC-MS/MS of a primary carnitine deficiency mouse model
2023-06-21 | PXD043158 | Pride
Proteomics Proteomic profiling of NCI60 cell lines from Cancer Cell Line Encyclopedia
2017-01-27 | E-PROT-2 | biostudies-arrayexpress
Proteomics A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
2022-02-22 | PXD023175 | Pride