Confronting false discoveries in single-cell differential expression
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE165003 | GEO | 2021/08/04
REPOSITORIES: GEO
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Project description:Confronting false discoveries in single-cell differential expression
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Project description:We developed a systematic statistical method, tTREAT, to identify differentially expressed genes with respect to a predefined FC threshold. The tTREAT approach aims to reduce false discoveries.
2014-01-08 | GSE53876 | GEO
Project description:De novo peptide sequencing is a fundamental research area in mass spectrometry (MS) based proteomics. However, those methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) and the significance of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate their FDRs. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data. Our FDR estimation also shows that some tools may perform better than the others in distinguishing between de novo PSMs and random matches, and can be used to assess the significance of de novo PSMs.
2024-08-28 | PXD055277 | Pride
Project description:The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
2022-09-05 | MTBLS1108 | MetaboLights
Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip See manuscript for details
2010-05-14 | E-GEOD-10112 | biostudies-arrayexpress
Project description:Objective: To develop a reliable, reproducible, and sensitive method for investigating gene-expression profiles from individual human oocytes. Setting: University medical center and university microarray laboratory. Patient(s): Couples undergoing intracytoplasmic sperm injection treatment were asked to donate their immature oocytes for research, and written informed consent was obtained in all cases. Seventy-nine human oocytes were used in total. Intervention(s): None. Main Outcome Measure(s): Amplification efficiency and microarray profiles. Result(s): Two of the five protocols (WT-Ovation One-Direct and Arcturus RiboAMp HS Plus) resulted in sufficient yields and high success rates and were further validated for their performance in obtaining reliable, reproducible, and sensitive expression profiles from individual human oocytes. Evaluation of these two protocols demonstrated that they both displayed low technical variation and produced highly reproducible profiles (r R 0.95). One of them identified significantly more transcripts but also had a higher number of false discoveries. Conclusion(s): Two protocols generated ample amounts of mRNA for (quantitative) polymerase chain reaction, microarray, and sequencing techniques. Further validation using a design that discriminates between biological and technical variation showed that both protocols can be used for gene-expression profiling of individual human oocytes.
2015-12-31 | GSE48141 | GEO
Project description:The goals of the study were to assess array platform performance and analysis algorithm performance. The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip, competition For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf DNA was spiked-in to a background DNA sample. Same DNA samples used in the arrays in this Series; these are technical array replicates.
2010-05-26 | E-GEOD-10090 | biostudies-arrayexpress
Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf 3 spike-in replicates and 3 genomic input control replicates
2007-12-12 | E-GEOD-9849 | biostudies-arrayexpress
Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf 9 spike-in replicates and 9 genomic input control replicates
2007-12-12 | E-GEOD-9848 | biostudies-arrayexpress
Project description:Case-control study for the analysis of the gene expression profile of epithelial cells microdissected from normal breast tissues obtained from 17 parous and 7 nulliparous women free of breast pathology (controls), and 39 parous and 8 nulliparous women with history of breast cancer (cases). Keywords: genetic modifications Four-condition experiment: nulliparous case, nulliparous control, parous case and parous control labeled with Cy5 and Universal human reference used as a common reference labeled with Cy3. Moderated t statistic was used as the basic statistic for significance analysis; it was computed for each probe and for each contrast. False discovery rate was controlled using the Benjamini and Hochberg. All genes with P value below a threshold of 0.05 were selected as differentially expressed, maintaining the proportion of false discoveries in the selected group below the threshold value, in this case 5%. Breast 11 parous control HuII, Breast 28 parous case HuII, and Breast 62 nulliparous control HuIII excluded: raw data is missing
2008-11-03 | E-GEOD-13293 | biostudies-arrayexpress