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MetabolomicsEFFECT OF DIFFERENT POOLED QC SAMPLES ON DATA QUALITY DURING AN INTER-BATCH EXPERIMENT IN UNTARGETED UHPLC-HRMS ANALYSIS ON TWO DIFFERENT MS PLATFORMS
Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice there are several ways to p...
2026-02-04 | MTBLS10568 | MetaboLights
MetabolomicsDirect infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control
Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computatio...
2022-05-16 | MTBLS79 | MetaboLights
TranscriptomicsStandard of hygiene and immune adaptation in newborn infants [15 rehybridized/batch correction samples]
The prevalence of immune-mediated diseases such as allergies and autoimmune diseases is on the rise in the developed world. Microbial exposure is known to modulate the risk for these diseases. In order to explore differences in the gene expression patterns induced in utero in infants born in contras...
ORGANISM(S): Homo sapiens 
2014-09-25 | E-GEOD-53472 | biostudies-arrayexpress
ProteomicsDDA-PASEF and diaPASEF acquired HCC1806/HS578T proteomic datasets with deliberate batch effects
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), a...
ORGANISM(S): Homo sapiens (Human) 
2023-11-21 | PXD041391 | Pride
Dda Pasef Dia Hcc1806 Hs578t Batch effects 
ProteomicsDDA-PASEF and diaPASEF acquired A549/K562 proteomic datasets with deliberate batch effects
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: A549 vs K562), allowi...
ORGANISM(S): Homo sapiens (Human) 
2023-11-21 | PXD041421 | Pride
Dda Pasef Dia K562 A549 Batch effects 
OtherAnnotation- and Batch Effect Correction in TCGA IsomiR Expression Data [Third-party re-analysis]
The Cancer Genome Atlas (TCGA) Isoform Expression Quantification Data is the largest ressource of isomiR level sequenced cancer data publicly available. Since the datasets were built up over years and through different contributing institutions, it is not free of batch effects. We evaluated differen...
ORGANISM(S): Homo sapiens 
2021-02-17 | GSE164767 | GEO
UnknownMBECS: Microbiome Batch Effects Correction Suite.
Not available
S-EPMC10155362 | biostudies-literature
TranscriptomicsTranscription analysis of E. coli processed at persistent and decreasing glucose limitation in a fed-batch cultivation
A time series of whole-genome transcription profiling of E. coli K-12 W3110 was performed to study the dynamic physiological response of E. coli K-12 W3110 to limited carbon conditions. cDNA from eight time intervals after glucose limitation, representing different stages of glucose depletion, along...
ORGANISM(S): Escherichia coli W3110 
2010-05-19 | E-GEOD-10307 | biostudies-arrayexpress
GenomicsiSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement
iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement
PRJNA675647 | ENA
UnknownBlind estimation and correction of microarray batch effect.
Not available
S-EPMC7145015 | biostudies-literature
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