selectBoost: a general algorithm to enhance the performance of variable selection methods.

MOTIVATION: With the growth of big data, variable selection has become one of the critical challenges in statistics. Although many methods have been proposed in the literature, their performance in terms of recall (sensitivity) and precision (predictive positive value) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting. RESULTS: In this article, we propose a general algorithm, which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering. AVAILABILITY AND IMPLEMENTATION: Code is available as the SelectBoost package on the CRAN, https://cran.r-project.org/package=SelectBoost. Some network reverse-engineering functionalities are available in the Patterns CRAN package, https://cran.r-project.org/package=Patterns. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combining label-free and label-based accurate quantifications with SWATH-MS: Comparison with SRM and PRM for the evaluation of bovine muscle type effects.

Mass spectrometry has proven to be a valuable tool for the accurate quantification of proteins. In this study, the performances of three targeted approaches, namely selected reaction monitoring (SRM), parallel reaction monitoring (PRM) and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS), to accurately quantify ten potential biomarkers of beef meat tenderness or marbling in a cohort of 64 muscle samples were evaluated. So as to get the most benefit out of the complete MS2 maps that are acquired in SWATH-MS, an original label-free quantification method to estimate protein amounts using an I-spline regression model was developed. Overall, SWATH-MS outperformed SRM in terms of sensitivity and dynamic range, while PRM still performed the best, and all three strategies showed similar quantification accuracies and precisions for the absolute quantification of targets of interest. This targeted picture was extended by 585 additional proteins for which amounts were estimated using the label-free approach on SWATH-MS; thus, offering a more global profiling of muscle proteomes and further insights into muscle type effect on candidate biomarkers of beef meat qualities as well as muscle metabolism.

Laterality and visuospatial strategies among young children: A novel 3D-2D transcription task.

Recent findings showed that children, like adults, exhibit directional biases leading to asymmetrical drawings. This appears to be the result of a complex interaction between several biological, motoric, and cultural factors. We created a drawing task designed to investigate the influence of laterality (i.e., hemispherical functional specialization and handedness) and sex on children's graphical asymmetries. This task consists of transcribing a symmetrical three-dimensional landscape model to a two-dimensional representation. Sixty-six French pre-school children, aged between 5 and 6 years, were asked to undertake the 3D-2D transcription task, as well as the classical Alter's directionality task. The novel task exhibited higher sensitivity than the Alter's directionality test when examining the spatial biases resulting from handedness, and sex. Specific drawing patterns related to these variables were identified. These results suggest that, in addition to the influence of biomechanical factors and handedness, sex plays a role in children's early graphomotor development. They also support the influence of laterality as a key factor underlying early directional biases.

Determining the number of components in PLS regression on incomplete data set.

Partial least squares regression - or PLS regression - is a multivariate method in which the model parameters are estimated using either the SIMPLS or NIPALS algorithm. PLS regression has been extensively used in applied research because of its effectiveness in analyzing relationships between an outcome and one or several components. Note that the NIPALS algorithm can provide estimates parameters on incomplete data. The selection of the number of components used to build a representative model in PLS regression is a central issue. However, how to deal with missing data when using PLS regression remains a matter of debate. Several approaches have been proposed in the literature, including the Q2 criterion, and the AIC and BIC criteria. Here we study the behavior of the NIPALS algorithm when used to fit a PLS regression for various proportions of missing data and different types of missingness. We compare criteria to select the number of components for a PLS regression on incomplete data set and on imputed data set using three imputation methods: multiple imputation by chained equations, k-nearest neighbour imputation, and singular value decomposition imputation. We tested various criteria with different proportions of missing data (ranging from 5% to 50%) under different missingness assumptions. Q2-leave-one-out component selection methods gave more reliable results than AIC and BIC-based ones.

Matching for the nonconventional MHC-I MICA gene significantly reduces the incidence of acute and chronic GVHD.

Graft-versus-host disease (GVHD) is among the most challenging complications in unrelated donor hematopoietic cell transplantation (HCT). The highly polymorphic MHC class I chain-related gene A, MICA, encodes a stress-induced glycoprotein expressed primarily on epithelia. MICA interacts with the invariant activating receptor NKG2D, expressed by cytotoxic lymphocytes, and is located in the MHC, next to HLA-B Hence, MICA has the requisite attributes of a bona fide transplantation antigen. Using high-resolution sequence-based genotyping of MICA, we retrospectively analyzed the clinical effect of MICA mismatches in a multicenter cohort of 922 unrelated donor HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1 10/10 allele-matched HCT pairs. Among the 922 pairs, 113 (12.3%) were mismatched in MICA MICA mismatches were significantly associated with an increased incidence of grade III-IV acute GVHD (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.50-2.23; P < .001), chronic GVHD (HR, 1.50; 95% CI, 1.45-1.55; P < .001), and nonelapse mortality (HR, 1.35; 95% CI, 1.24-1.46; P < .001). The increased risk for GVHD was mirrored by a lower risk for relapse (HR, 0.50; 95% CI, 0.43-0.59; P < .001), indicating a possible graft-versus-leukemia effect. In conclusion, when possible, selecting a MICA-matched donor significantly influences key clinical outcomes of HCT in which a marked reduction of GVHD is paramount. The tight linkage disequilibrium between MICA and HLA-B renders identifying a MICA-matched donor readily feasible in clinical practice.

Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data.

MOTIVATION: A vast literature from the past decade is devoted to relating gene profiles and subject survival or time to cancer recurrence. Biomarker discovery from high-dimensional data, such as transcriptomic or single nucleotide polymorphism profiles, is a major challenge in the search for more precise diagnoses. The proportional hazard regression model suggested by Cox (1972), to study the relationship between the time to event and a set of covariates in the presence of censoring is the most commonly used model for the analysis of survival data. However, like multivariate regression, it supposes that more observations than variables, complete data, and not strongly correlated variables are available. In practice, when dealing with high-dimensional data, these constraints are crippling. Collinearity gives rise to issues of over-fitting and model misidentification. Variable selection can improve the estimation accuracy by effectively identifying the subset of relevant predictors and enhance the model interpretability with parsimonious representation. To deal with both collinearity and variable selection issues, many methods based on least absolute shrinkage and selection operator penalized Cox proportional hazards have been proposed since the reference paper of Tibshirani. Regularization could also be performed using dimension reduction as is the case with partial least squares (PLS) regression. We propose two original algorithms named sPLSDR and its non-linear kernel counterpart DKsPLSDR, by using sparse PLS regression (sPLS) based on deviance residuals. We compared their predicting performance with state-of-the-art algorithms on both simulated and real reference benchmark datasets. RESULTS: sPLSDR and DKsPLSDR compare favorably with other methods in their computational time, prediction and selectivity, as indicated by results based on benchmark datasets. Moreover, in the framework of PLS regression, they feature other useful tools, including biplots representation, or the ability to deal with missing data. Therefore, we view them as a useful addition to the toolbox of estimation and prediction methods for the widely used Cox's model in the high-dimensional and low-sample size settings. AVAILABILITY AND IMPLEMENTATION: The R-package plsRcox is available on the CRAN and is maintained by Frédéric Bertrand. http://cran.r-project.org/web/packages/plsRcox/index.html. CONTACT: pbastien@rd.loreal.com or fbertran@math.unistra.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia.

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.

Cascade: a R package to study, predict and simulate the diffusion of a signal through a temporal gene network.

SUMMARY: Temporal gene interactions, in response to environmental stress, form a complex system that can be efficiently described using gene regulatory networks. They allow highlighting the more influential genes and spotting some targets for biological intervention experiments. Despite that many reverse engineering tools have been designed, the Cascade package is an integrated solution adding several new and original key features such as the ability to predict changes in gene expressions after a biological perturbation in the network and graphical outputs that allow monitoring the spread of a signal through the network. AVAILABILITY AND IMPLEMENTATION: The R package Cascade is available online at http://www-math.u-strasbg.fr/genpred/spip.php?rubrique4.

How visual attention span and phonological skills contribute to N170 print tuning: An EEG study in French dyslexic students.

Developmental dyslexia is a disorder characterized by a sustainable learning deficit in reading. Based on ERP-driven approaches focusing on the visual word form area, electrophysiological studies have pointed a lack of visual expertise for written word recognition in dyslexic readers by contrasting the left-lateralized N170 amplitudes elicited by alphabetic versus non-alphabetic stimuli. Here, we investigated in 22 dyslexic participants and 22 age-matched control subjects how two behavioural abilities potentially affected in dyslexic readers (phonological and visual attention skills) contributed to the N170 expertise during a word detection task. Consistent with literature, dyslexic participants exhibited poorer performance in these both abilities as compared to healthy subjects. At the brain level, we observed (1) an unexpected preservation of the N170 expertise in the dyslexic group suggesting a possible compensatory mechanism and (2) a modulation of this expertise only by phonological skills, providing evidence for the phonological mapping deficit hypothesis.

The MHC class I MICA gene is a histocompatibility antigen in kidney transplantation.

The identity of histocompatibility loci, besides human leukocyte antigen (HLA), remains elusive. The major histocompatibility complex (MHC) class I MICA gene is a candidate histocompatibility locus. Here, we investigate its role in a French multicenter cohort of 1,356 kidney transplants. MICA mismatches were associated with decreased graft survival (hazard ratio (HR), 2.12; 95% confidence interval (CI): 1.45-3.11; P < 0.001). Both before and after transplantation anti-MICA donor-specific antibodies (DSA) were strongly associated with increased antibody-mediated rejection (ABMR) (HR, 3.79; 95% CI: 1.94-7.39; P < 0.001; HR, 9.92; 95% CI: 7.43-13.20; P < 0.001, respectively). This effect was synergetic with that of anti-HLA DSA before and after transplantation (HR, 25.68; 95% CI: 3.31-199.41; P = 0.002; HR, 82.67; 95% CI: 33.67-202.97; P < 0.001, respectively). De novo-developed anti-MICA DSA were the most harmful because they were also associated with reduced graft survival (HR, 1.29; 95% CI: 1.05-1.58; P = 0.014). Finally, the damaging effect of anti-MICA DSA on graft survival was confirmed in an independent cohort of 168 patients with ABMR (HR, 1.71; 95% CI: 1.02-2.86; P = 0.041). In conclusion, assessment of MICA matching and immunization for the identification of patients at high risk for transplant rejection and loss is warranted.

Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models.

Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult. We proposed algorithms that were able to fit Cox models in high dimensional settings using extensions of partial least squares regression to the Cox models. Some of them were able to cope with missing data. We were recently able to extend our most recent algorithms to big data, thus allowing to fit Cox model for big data with missing values. When cross-validating standard or extended Cox models, the commonly used criterion is the cross-validated partial loglikelihood using a naive or a van Houwelingen scheme -to make efficient use of the death times of the left out data in relation to the death times of all the data. Quite astonishingly, we will show, using a strong simulation study involving three different data simulation algorithms, that these two cross-validation methods fail with the extensions, either straightforward or more involved ones, of partial least squares regression to the Cox model. This is quite an interesting result for at least two reasons. Firstly, several nice features of PLS based models, including regularization, interpretability of the components, missing data support, data visualization thanks to biplots of individuals and variables -and even parsimony or group parsimony for Sparse partial least squares or sparse group SPLS based models, account for a common use of these extensions by statisticians who usually select their hyperparameters using cross-validation. Secondly, they are almost always featured in benchmarking studies to assess the performance of a new estimation technique used in a high dimensional or big data context and often show poor statistical properties. We carried out a vast simulation study to evaluate more than a dozen of potential cross-validation criteria, either AUC or prediction error based. Several of them lead to the selection of a reasonable number of components. Using these newly found cross-validation criteria to fit extensions of partial least squares regression to the Cox model, we performed a benchmark reanalysis that showed enhanced performances of these techniques. In addition, we proposed sparse group extensions of our algorithms and defined a new robust measure based on the Schmid score and the R coefficient of determination for least absolute deviation: the integrated R Schmid Score weighted. The R-package used in this article is available on the CRAN, http://cran.r-project.org/web/packages/plsRcox/index.html. The R package bigPLS will soon be available on the CRAN and, until then, is available on Github https://github.com/fbertran/bigPLS.

Temporal multiomic modeling reveals a B-cell receptor proliferative program in chronic lymphocytic leukemia.

B-cell receptor (BCR) signaling is crucial for the pathophysiology of most mature B-cell lymphomas/leukemias and has emerged as a therapeutic target whose effectiveness remains limited by the occurrence of mutations. Therefore, deciphering the cellular program activated downstream this pathway has become of paramount importance for the development of innovative therapies. Using an original ex vivo model of BCR-induced proliferation of chronic lymphocytic leukemia cells, we generated 108 temporal transcriptional and proteomic profiles from 1 h up to 4 days after BCR activation. This dataset revealed a structured temporal response composed of 13,065 transcripts and 4027 proteins, comprising a leukemic proliferative signature consisting of 430 genes and 374 proteins. Mathematical modeling of this complex cellular response further highlighted a transcriptional network driven by 14 early genes linked to proteins involved in cell proliferation. This group includes expected genes (EGR1/2, NF-kB) and genes involved in NF-kB signaling modulation (TANK, ROHF) and immune evasion (KMO, IL4I1) that have not yet been associated with leukemic cells proliferation. Our study unveils the BCR-activated proliferative genetic program in primary leukemic cells. This approach combining temporal measurements with modeling allows identifying new putative targets for innovative therapy of lymphoid malignancies and also cancers dependent on ligand-receptor interactions.

Compatibility at amino acid position 98 of MICB reduces the incidence of graft-versus-host disease in conjunction with the CMV status.

Graft-versus-host disease (GVHD) and cytomegalovirus (CMV)-related complications are leading causes of mortality after unrelated-donor hematopoietic cell transplantation (UD-HCT). The non-conventional MHC class I gene MICB, alike MICA, encodes a stress-induced polymorphic NKG2D ligand. However, unlike MICA, MICB interacts with the CMV-encoded UL16, which sequestrates MICB intracellularly, leading to immune evasion. Here, we retrospectively analyzed the impact of mismatches in MICB amino acid position 98 (MICB98), a key polymorphic residue involved in UL16 binding, in 943 UD-HCT pairs who were allele-matched at HLA-A, -B, -C, -DRB1, -DQB1 and MICA loci. HLA-DP typing was further available. MICB98 mismatches were significantly associated with an increased incidence of acute (grade II-IV: HR, 1.20; 95% CI, 1.15 to 1.24; P < 0.001; grade III-IV: HR, 2.28; 95% CI, 1.56 to 3.34; P < 0.001) and chronic GVHD (HR, 1.21; 95% CI, 1.10 to 1.33; P < 0.001). MICB98 matching significantly reduced the effect of CMV status on overall mortality from a hazard ratio of 1.77 to 1.16. MICB98 mismatches showed a GVHD-independent association with a higher incidence of CMV infection/reactivation (HR, 1.84; 95% CI, 1.34 to 2.51; P < 0.001). Hence selecting a MICB98-matched donor significantly reduces the GVHD incidence and lowers the impact of CMV status on overall survival.

Effect of the Phosphodiesterase 5 Inhibitor Sildenafil on Ischemia-Reperfusion-Induced Muscle Mitochondrial Dysfunction and Oxidative Stress.

Lower-limb ischemia-reperfusion (IR) is frequent and associated with significant morbidity and mortality. Phosphodiesterase 5 inhibitors demonstrated antioxidant and beneficial effects in several organs submitted to IR, but their effects on muscle mitochondrial functions after lower-limb IR are unknown. Unilateral hindlimb IR (2 h tourniquet followed by 2 h reperfusion) without or with sildenafil (1mg/kg ip 30 minutes before ischemia) was performed in 18 mice. Maximal oxidative capacity (VMax), relative contribution of the mitochondrial respiratory chain complexes, calcium retention capacity (CRC)-a marker of apoptosis-and reactive oxygen species (ROS) production were determined using high-resolution respirometry, spectrofluorometry, and electron paramagnetic resonance in gastrocnemius muscles from both hindlimbs. IR significantly reduced mitochondrial VMax (from 11.79 ± 1.74 to 4.65 ± 1.11 pmol/s*mg wet weight (ww), p < 0.05, -50.2 ± 16.3%) and CRC (from 2.33 ± 0.41 to 0.84 ± 0.18 µmol/mg dry weight (dw), p < 0.05; -61.1 ± 6.8%). ROS tended to increase in the ischemic limb (+64.3 ± 31.9%, p = 0.08). Although tending to reduce IR-related ROS production (-42.4%), sildenafil failed to reduce muscle mitochondrial dysfunctions (-63.3 ± 9.2%, p < 0.001 and -55.2 ± 7.6% p < 0.01 for VMax, and CRC, respectively). In conclusion, lower limb IR impaired skeletal muscle mitochondrial function, but, despite tending to reduce ROS production, pharmacological preconditioning with sildenafil did not show protective effects.

BCR-associated factors driving chronic lymphocytic leukemia cells proliferation ex vivo.

A chronic antigenic stimulation is believed to sustain the leukemogenic development of chronic lymphocytic leukemia (CLL) and most of lymphoproliferative malignancies developed from mature B cells. Reproducing a proliferative stimulation ex vivo is critical to decipher the mechanisms of leukemogenesis in these malignancies. However, functional studies of CLL cells remains limited since current ex vivo B cell receptor (BCR) stimulation protocols are not sufficient to induce the proliferation of these cells, pointing out the need of mandatory BCR co-factors in this process. Here, we investigated benefits of several BCR co-stimulatory molecules (IL-2, IL-4, IL-15, IL-21 and CD40 ligand) in multiple culture conditions. Our results demonstrated that BCR engagement (anti-IgM ligation) concomitant to CD40 ligand, IL-4 and IL-21 stimulation allowed CLL cells proliferation ex vivo. In addition, we established a proliferative advantage for ZAP70 positive CLL cells, associated to an increased phosphorylation of ZAP70/SYK and STAT6. Moreover, the use of a tri-dimensional matrix of methylcellulose and the addition of TLR9 agonists further increased this proliferative response. This ex vivo model of BCR stimulation with T-derived cytokines is a relevant and efficient model for functional studies of CLL as well as lymphoproliferative malignancies.