Le paysage comme nouvelle pratique de gouvernance territoriale : une perspective de développement social et de justice environnementale

Functional Annotation Clustering for PDE genes. (XLSX 14 kb

Sparse Sliced Inverse Regression via Lasso

For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and only if ρ=limpn=0, where p is the dimension and n is the sample size. Thus, when p is of the same or a higher order of n, additional assumptions such as sparsity must be imposed in order to ensure consistency for SIR. By constructing artificial response variables made up from top eigenvectors of the estimated conditional covariance matrix, we introduce a simple Lasso regression method to obtain an estimate of the SDR space. The resulting algorithm, Lasso-SIR, is shown to be consistent and achieves the optimal convergence rate under certain sparsity conditions when p is of order o(n2λ2), where λ is the generalized signal-to-noise ratio. We also demonstrate the superior performance of Lasso-SIR compared with existing approaches via extensive numerical studies and several real data examples. Supplementary materials for this article are available online.</p

Nonparametric <i>K</i>-Sample Tests via Dynamic Slicing

<div><p><i>K</i>-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.</p></div

Additional file 7 of Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model

Significant gene sets detected by the GSA. (XLSX 44 kb

Ranunculus shinano-alpinus Ohwi

原著和名: タカネキンポウゲ科名: キンポウゲ科 = Ranunculaceae採集地: 富山県 白馬鑓ヶ岳 (越中 白馬鑓ヶ岳)採集日: 1978/8/22採集者: 古瀬 義整理番号: JH000107国立科学博物館整理番号: TNS-VS-95010

Additional file 4 of Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model

Functional Annotation Clustering for genes specifically found by edgeR in comparison with NBMM. (XLSX 10 kb

Architecture of CPNM system.

<p>Architecture of CPNM system.</p

Additional file 7 of Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model

Significant gene sets detected by the GSA. (XLSX 44 kb

Node neighbour (hub-protein) statistics in the network diagram (Figure 5).

<p>This table shows that IL13, IL4, FLG, GRP, IL10, STAT6, and TSLP may be important hub-proteins in the network for the target biological context (IL13, asthma, human). Only nodes with two or more neighbours are shown.</p

Pathway involvement of the hub-node proteins in the context specific network generated by CPNM in Case Study I using pathway information given in hiPathDB database [61].

<p>Highlighted in bold are the pathways that are known to be associated with asthma as per annotation provided in the source databases in hiPathDB.</p