1
|
Turkarslan S, He Y, Hothi P, Murie C, Nicolas A, Kannan K, Park JH, Pan M, Awawda A, Cole ZD, Shapiro MA, Stuhlmiller TJ, Lee H, Patel AP, Cobbs C, Baliga NS. An atlas of causal and mechanistic drivers of interpatient heterogeneity in glioma. medRxiv 2024:2024.04.05.24305380. [PMID: 38633778 PMCID: PMC11023657 DOI: 10.1101/2024.04.05.24305380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Grade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional "programs", influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2 nd line treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2 nd line treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.
Collapse
|
2
|
Murie C, Turkarslan S, Patel A, Coffey DG, Becker PS, Baliga NS. Individualized dynamic risk assessment for multiple myeloma. medRxiv 2024:2024.04.01.24305024. [PMID: 38633807 PMCID: PMC11023676 DOI: 10.1101/2024.04.01.24305024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Individualized treatment decisions for patients with multiple myeloma (MM) requires accurate risk stratification that takes into account patient-specific consequences of genetic abnormalities and tumor microenvironment on disease outcome and therapy responsiveness. Methods Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated the mmSYGNAL network, which uncovered different causal and mechanistic drivers of genetic programs associated with disease progression across MM subtypes. Here, we have trained a machine learning (ML) algorithm on activities of mmSYGNAL programs within individual patient tumor samples to develop a risk classification scheme for MM that significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting risk of PFS across four independent patient cohorts. Results We demonstrate that, unlike other tests, mmSYGNAL can accurately predict disease progression risk at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized dynamic risk assessment throughout the disease trajectory. Conclusion mmSYGNAL provides improved individualized risk stratification that accounts for a patient's distinct set of genetic abnormalities and can monitor risk longitudinally as each patient's disease characteristics change.
Collapse
|
3
|
Bernard F, Zare M, Murie C, Sagot JC. Dimensions physiques et cognitives : vers une nécessaire prise en compte en maintenabilité aéronautique. ARCH MAL PROF ENVIRO 2021. [DOI: 10.1016/j.admp.2020.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
4
|
Oertlin C, Lorent J, Murie C, Furic L, Topisirovic I, Larsson O. Generally applicable transcriptome-wide analysis of translation using anota2seq. Nucleic Acids Res 2020; 47:e70. [PMID: 30926999 PMCID: PMC6614820 DOI: 10.1093/nar/gkz223] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 03/18/2019] [Accepted: 03/28/2019] [Indexed: 12/28/2022] Open
Abstract
mRNA translation plays an evolutionarily conserved role in homeostasis and when dysregulated contributes to various disorders including metabolic and neurological diseases and cancer. Notwithstanding that optimal and universally applicable methods are critical for understanding the complex role of translational control under physiological and pathological conditions, approaches to analyze translatomes are largely underdeveloped. To address this, we developed the anota2seq algorithm which outperforms current methods for statistical identification of changes in translation. Notably, in contrast to available analytical methods, anota2seq also allows specific identification of an underappreciated mode of gene expression regulation whereby translation acts as a buffering mechanism which maintains protein levels despite fluctuations in corresponding mRNA abundance (‘translational buffering’). Thus, the universal anota2seq algorithm allows efficient and hitherto unprecedented interrogation of translatomes which is anticipated to advance knowledge regarding the role of translation in homeostasis and disease.
Collapse
Affiliation(s)
- Christian Oertlin
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Julie Lorent
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Carl Murie
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Luc Furic
- Cancer Program, Biomedicine Discovery Institute and Department of Anatomy & Developmental Biology, Monash University, VIC, Australia.,Prostate Cancer Translational Research Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Ivan Topisirovic
- Lady Davis Institute, SMBD Jewish General Hospital, Gerald Bronfman Department of Oncology, and Departments of Experimental Medicine, and Biochemistry McGill University, Montreal, Canada
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
5
|
Sandri BJ, Masvidal L, Murie C, Bartish M, Avdulov S, Higgins L, Markowski T, Peterson M, Bergh J, Yang P, Rolny C, Limper AH, Griffin TJ, Bitterman PB, Wendt CH, Larsson O. Distinct Cancer-Promoting Stromal Gene Expression Depending on Lung Function. Am J Respir Crit Care Med 2019; 200:348-358. [PMID: 30742544 PMCID: PMC6680296 DOI: 10.1164/rccm.201801-0080oc] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 02/08/2019] [Indexed: 12/31/2022] Open
Abstract
Rationale: Chronic obstructive pulmonary disease is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that lung stromal cells activate pathological gene expression programs that support oncogenesis.Objectives: To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer.Methods: The study included subjects with and without lung cancer across a spectrum of lung-function values. We conducted a multiomics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome.Measurements and Main Results: Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation modulating protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms that drove these cancer-associated translation programs differed based on lung function. In subjects with normal to mildly impaired lung function, the mammalian target of rapamycin (mTOR) pathway served as an upstream driver, whereas in subjects with severe airflow obstruction, pathways downstream of pathological extracellular matrix emerged. Consistent with a role during cancer initiation, both the mTOR and extracellular matrix gene expression programs paralleled the activation of previously identified procancer secretomes. Furthermore, an in situ examination of lung tissue showed that stromal fibroblasts expressed cancer-associated proteins from two procancer secretomes: one that included IL-6 (in cases of mild or no airflow obstruction), and one that included BMP1 (in cases of severe airflow obstruction).Conclusions: Two distinct stromal gene expression programs that promote cancer initiation are activated in patients with lung cancer depending on lung function. Our work has implications both for screening strategies and for personalized approaches to cancer treatment.
Collapse
Affiliation(s)
- Brian J. Sandri
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Laia Masvidal
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Carl Murie
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Margarita Bartish
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Svetlana Avdulov
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Todd Markowski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Mark Peterson
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Jonas Bergh
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | | | - Charlotte Rolny
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Andrew H. Limper
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota; and
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Peter B. Bitterman
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Chris H. Wendt
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
- Pulmonary, Allergy, Critical Care, and Sleep Medicine, Veterans Affairs Medical Center, Minneapolis, Minnesota
| | - Ola Larsson
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| |
Collapse
|
6
|
Rothen J, Murie C, Carnes J, Anupama A, Abdulla S, Chemba M, Mpina M, Tanner M, Lee Sim BK, Hoffman SL, Gottardo R, Daubenberger C, Stuart K. Whole blood transcriptome changes following controlled human malaria infection in malaria pre-exposed volunteers correlate with parasite prepatent period. PLoS One 2018; 13:e0199392. [PMID: 29920562 PMCID: PMC6007927 DOI: 10.1371/journal.pone.0199392] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 05/29/2018] [Indexed: 12/15/2022] Open
Abstract
Malaria continues to be one of mankind’s most devastating diseases despite the many and varied efforts to combat it. Indispensable for malaria elimination and eventual eradication is the development of effective vaccines. Controlled human malaria infection (CHMI) is an invaluable tool for vaccine efficacy assessment and investigation of early immunological and molecular responses against Plasmodium falciparum infection. Here, we investigated gene expression changes following CHMI using RNA-Seq. Peripheral blood samples were collected in Bagamoyo, Tanzania, from ten adults who were injected intradermally (ID) with 2.5x104 aseptic, purified, cryopreserved P. falciparum sporozoites (Sanaria® PfSPZ Challenge). A total of 2,758 genes were identified as differentially expressed following CHMI. Transcriptional changes were most pronounced on day 5 after inoculation, during the clinically silent liver phase. A secondary analysis, grouping the volunteers according to their prepatent period duration, identified 265 genes whose expression levels were linked to time of blood stage parasitemia detection. Gene modules associated with these 265 genes were linked to regulation of transcription, cell cycle, phosphatidylinositol signaling and erythrocyte development. Our study showed that in malaria pre-exposed volunteers, parasite prepatent period in each individual is linked to magnitude and timing of early gene expression changes after ID CHMI.
Collapse
Affiliation(s)
- Julian Rothen
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Carl Murie
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Jason Carnes
- Center for Infectious Disease Research, Seattle, Washington, United States of America
| | - Atashi Anupama
- Center for Infectious Disease Research, Seattle, Washington, United States of America
| | - Salim Abdulla
- Bagamoyo Research and Training Centre, Ifakara Health Institute, Bagamoyo, Tanzania
| | - Mwajuma Chemba
- Bagamoyo Research and Training Centre, Ifakara Health Institute, Bagamoyo, Tanzania
| | - Maxmillian Mpina
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland.,Bagamoyo Research and Training Centre, Ifakara Health Institute, Bagamoyo, Tanzania
| | - Marcel Tanner
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - B Kim Lee Sim
- Sanaria Inc., Rockville, Maryland, United States of America
| | | | - Raphael Gottardo
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Claudia Daubenberger
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Ken Stuart
- Center for Infectious Disease Research, Seattle, Washington, United States of America
| |
Collapse
|
7
|
Murie C, Sandri B, Sandberg AS, Griffin TJ, Lehtiö J, Wendt C, Larsson O. Normalization of mass spectrometry data (NOMAD). Adv Biol Regul 2018; 67:128-133. [PMID: 29174395 PMCID: PMC5885284 DOI: 10.1016/j.jbior.2017.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 11/16/2017] [Accepted: 11/16/2017] [Indexed: 04/13/2023]
Abstract
iTRAQ and TMT reagent-based mass spectrometry (MS) are commonly used technologies for quantitative proteomics in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. Such MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that reference normalization does not effectively remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data sets. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality. NOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD effectively removes bias which improves valid across MS run comparisons.
Collapse
Affiliation(s)
- Carl Murie
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Brian Sandri
- Division of Pulmonary and Critical Care Medicine and VAMC, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Ann-Sofi Sandberg
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN, USA; Center for Mass Spectrometry and Proteomics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Janne Lehtiö
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Christine Wendt
- Division of Pulmonary and Critical Care Medicine and VAMC, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden.
| |
Collapse
|
8
|
Sood S, Szkop KJ, Nakhuda A, Gallagher IJ, Murie C, Brogan RJ, Kaprio J, Kainulainen H, Atherton PJ, Kujala UM, Gustafsson T, Larsson O, Timmons JA. iGEMS: an integrated model for identification of alternative exon usage events. Nucleic Acids Res 2016; 44:e109. [PMID: 27095197 PMCID: PMC4914109 DOI: 10.1093/nar/gkw263] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 04/02/2016] [Indexed: 12/16/2022] Open
Abstract
DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with (n = 9) or without (n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (∼90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3′UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5–10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.
Collapse
Affiliation(s)
- Sanjana Sood
- Division of Genetics and Molecular Medicine, King's College London, WC2R 2LS, London, UK Research Department, XRGenomics Ltd, 35 Kingsland Road, London E2 8AA, UK
| | - Krzysztof J Szkop
- Division of Genetics and Molecular Medicine, King's College London, WC2R 2LS, London, UK Research Department, XRGenomics Ltd, 35 Kingsland Road, London E2 8AA, UK
| | - Asif Nakhuda
- Division of Genetics and Molecular Medicine, King's College London, WC2R 2LS, London, UK School of Medicine, University of Nottingham, Derby Royal Hospital, Derbyshire, DE22 3DT, UK
| | - Iain J Gallagher
- School of Health Sciences, University of Stirling, Stirling, FK9 4LA, Scotland
| | - Carl Murie
- Department of Oncology-Pathology, SciLifeLab, Karolinska Institutet, 171 76 Stockholm, Sweden
| | - Robert J Brogan
- Research Department, XRGenomics Ltd, 35 Kingsland Road, London E2 8AA, UK
| | - Jaakko Kaprio
- Department of Public Health and the Institute for Molecular Medicine (FIMM), University of Helsinki, FI-00014, Helsinki, Finland National Institute for Health and Welfare, University of Helsinki, FI-00014, Helsinki, Finland
| | - Heikki Kainulainen
- Department of Biology of Physical Activity, University of Jyväskylä, FI-40014, Jyväskylä, Finland
| | - Philip J Atherton
- School of Medicine, University of Nottingham, Derby Royal Hospital, Derbyshire, DE22 3DT, UK
| | - Urho M Kujala
- Department of Health Sciences, University of Jyväskylä, FI-40014, Jyväskylä, Finland
| | - Thomas Gustafsson
- Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska University Hospital, 14186, Huddinge, Sweden
| | - Ola Larsson
- Department of Oncology-Pathology, SciLifeLab, Karolinska Institutet, 171 76 Stockholm, Sweden
| | - James A Timmons
- Division of Genetics and Molecular Medicine, King's College London, WC2R 2LS, London, UK Research Department, XRGenomics Ltd, 35 Kingsland Road, London E2 8AA, UK
| |
Collapse
|
9
|
Abstract
The success of high-throughput screening (HTS) strategies depends on the effectiveness of both normalization methods and study design. We report comparisons among normalization methods in two titration series experiments. We also extend the results in a third experiment with two differently designed but otherwise identical screens: compounds in replicate plates were either placed in the same well locations or were randomly assigned to different locations. Best results were obtained when randomization was combined with normalization methods that corrected for within-plate spatial bias. We conclude that potent, reliable, and accurate HTS requires replication, randomization design strategies, and more extensive normalization than is typically done and that formal statistical testing is desirable. The Statistics and dIagnostic Graphs for HTS (SIGHTS) Microsoft Excel Add-In software is available to conduct most analyses reported here.
Collapse
Affiliation(s)
- Carl Murie
- McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Caroline Barette
- Equipe Criblage pour des Molécules Bio-Actives (CMBA), U1038 INSERM/CEA/UJF, CEA Grenoble, Grenoble Cedex 09, France
| | - Jennifer Button
- McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
| | - Laurence Lafanechère
- Equipe Criblage pour des Molécules Bio-Actives (CMBA), U1038 INSERM/CEA/UJF, CEA Grenoble, Grenoble Cedex 09, France
- Institut Albert Bonniot, CRI INSERM/UJF U823, Team 3 “Polarity, Development and Cancer,” Rond-point de la Chantourne, La Tronche Cedex, France
| | - Robert Nadon
- McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
10
|
Murie C, Barette C, Lafanechère L, Nadon R. Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features. ACTA ACUST UNITED AC 2013; 19:661-71. [DOI: 10.1177/1087057113516003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 11/15/2013] [Indexed: 11/17/2022]
Abstract
Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR’s performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR’s performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.
Collapse
Affiliation(s)
- C. Murie
- McGill University and Génome Québec Innovation Centre, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
| | - C. Barette
- Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble, France
| | - L. Lafanechère
- Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble, France
- NSERM, Université Joseph Fourier-Grenoble 1, Institut Albert Bonniot, Grenoble, France
| | - R. Nadon
- McGill University and Génome Québec Innovation Centre, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
| |
Collapse
|
11
|
Murie C, Barette C, Lafanechère L, Nadon R. Single assay-wide variance experimental (SAVE) design for high-throughput screening. Bioinformatics 2013; 29:3067-72. [DOI: 10.1093/bioinformatics/btt538] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
12
|
Murie C, Woody O, Lee AY, Nadon R. Comparison of small n statistical tests of differential expression applied to microarrays. BMC Bioinformatics 2009; 10:45. [PMID: 19192265 PMCID: PMC2674054 DOI: 10.1186/1471-2105-10-45] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2008] [Accepted: 02/03/2009] [Indexed: 11/20/2022] Open
Abstract
Background DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data. Results Three Empirical Bayes methods (CyberT, BRB, and limma t-statistics) were the most effective statistical tests across simulated and both 2-colour cDNA and Affymetrix experimental data. The CyberT regularized t-statistic in particular was able to maintain expected false positive rates with simulated data showing high variances at low gene intensities, although at the cost of low true positive rates. The Local Pooled Error (LPE) test introduced a bias that lowered false positive rates below theoretically expected values and had lower power relative to the top performers. The standard two-sample t-test and fold change were also found to be sub-optimal for detecting differentially expressed genes. The generalized log transformation was shown to be beneficial in improving results with certain data sets, in particular high variance cDNA data. Conclusion Pre-processing of data influences performance and the proper combination of pre-processing and statistical testing is necessary for obtaining the best results. All three Empirical Bayes methods assessed in our study are good choices for statistical tests for small n microarray studies for both Affymetrix and cDNA data. Choice of method for a particular study will depend on software and normalization preferences.
Collapse
Affiliation(s)
- Carl Murie
- McGill University and Genome Quebec Innovation Centre, 740 Avenue du Docteur Penfield, Montreal, Quebec H3A1A4, Canada.
| | | | | | | |
Collapse
|
13
|
Abstract
UNLABELLED Jain et al. introduced the Local Pooled Error (LPE) statistical test designed for use with small sample size microarray gene-expression data. Based on an asymptotic proof, the test multiplicatively adjusts the standard error for a test of differences between two classes of observations by pi/2 due to the use of medians rather than means as measures of central tendency. The adjustment is upwardly biased at small sample sizes, however, producing fewer than expected small P-values with a consequent loss of statistical power. We present an empirical correction to the adjustment factor which removes the bias and produces theoretically expected P-values when distributional assumptions are met. Our adjusted LPE measure should prove useful to ongoing methodological studies designed to improve the LPE's; performance for microarray and proteomics applications and for future work for other high-throughput biotechnologies. AVAILABILITY The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org).
Collapse
Affiliation(s)
- Carl Murie
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, Canada
| | | |
Collapse
|
14
|
Miron M, Woody OZ, Marcil A, Murie C, Sladek R, Nadon R. A methodology for global validation of microarray experiments. BMC Bioinformatics 2006; 7:333. [PMID: 16822306 PMCID: PMC1539027 DOI: 10.1186/1471-2105-7-333] [Citation(s) in RCA: 62] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2006] [Accepted: 07/05/2006] [Indexed: 12/03/2022] Open
Abstract
Background DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies. Results We present an approach for the global validation of DNA microarray experiments that will allow researchers to evaluate the general quality of their experiment and to extrapolate validation results of a subset of genes to the remaining non-validated genes. We illustrate why the popular strategy of selecting only the most differentially expressed genes for validation generally fails as a global validation strategy and propose random-stratified sampling as a better gene selection method. We also illustrate shortcomings of often-used validation indices such as overlap of significant effects and the correlation coefficient and recommend the concordance correlation coefficient (CCC) as an alternative. Conclusion We provide recommendations that will enhance validity checks of microarray experiments while minimizing the need to run a large number of labour-intensive individual validation assays.
Collapse
Affiliation(s)
- Mathieu Miron
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada
| | - Owen Z Woody
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada
| | - Alexandre Marcil
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada
| | - Carl Murie
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada
| | - Robert Sladek
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada
| | - Robert Nadon
- McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada
- McGill University Department of Human Genetics, 1205 avenue du Docteur Penfield N5/13, Montreal, Quebec, H3A 1B1, Canada
| |
Collapse
|
15
|
Boucher JP, Leduc B, Prouix P, Pepin A, Murie C, Danakas M. 11. Med Sci Sports Exerc 1987. [DOI: 10.1249/00005768-198704001-00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|