Computational aspects of the <i>k</i>NN local linear smoothing for some conditional models in high dimensional statistics

Almanjahie, Ibrahim M.; Alahmari, Wafa Mesfer; Laksaci, Ali; Rachdi, Mustapha

doi:10.1080/03610918.2021.1923745

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Therefore, caution is suggested when interpreting clusters and trajectories derived from datasets with these conditions. Furthermore, it is also relevant to mention that a growing number of computational methods such as MAGIC, SAVER, or kNN-smoothing (van Dijk et al, 2018;Huang et al, 2018;Almanjahie et al, 2021) have been proposed to resolve the increased sparsity observed in single-cell datasets by imputing data (thereby named as imputation methods) to values that are missing or unobservable, thus improving the analysis of datasets with an elevated fraction of zero counts (Patruno et al, 2021). Some imputation methods directly address the sparsity of the single-cell datasets by using probabilistic models to distinguish biological from technical zeros and then adding values only to the technical ones.…”

Section: Normalizationmentioning

confidence: 99%

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Durán

González-Orozco

Velasco

et al. 2022

Front. Cell Dev. Biol.

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Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer’s (AD) and Parkinson’s disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

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Section: Normalizationmentioning

confidence: 99%

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Durán

González-Orozco

Velasco

et al. 2022

Front. Cell Dev. Biol.

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Local linear-$k$NN smoothing for semi-functional partial linear regression

Nassima Houda,

Tawfik,

Amina

et al. 2024

Hacettepe Journal of Mathematics and Statistics

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The aim of this paper is to study a semi-functional partial linear regression model. The estimators are constructed by k-nearest neighbors local linear method. Some asymptotic results are established for an i.i.d sample under certain conditions, including asymptotic normality of the parametric component and the almost certain convergence (with rate) of the non-parametric component. Lastly, using cross-validation, the performances of our estimation method are presented on simulated data and on real data by comparing them with other known approaches for semi-functional partial linear regression models.

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Smooth kNN Local Linear Estimation of the Conditional Distribution Function

et al. 2021

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Previous works were dedicated to the functional k-Nearest Neighbors (kNN) and the local linearity method estimations of a regression operator. In this paper, a sequence pair of (Xi,Yi)i=1,…,n of functional mixing observations are considered. We treat the local linear estimation of the cumulative function of Yi given functional input variable Xi. Precisely, we combine the kNN method with the local linear algorithm to construct a new and fast efficiency estimator of the conditional distribution function. The main purpose of this paper is to prove the strong convergence of the constructed estimator under mixing conditions. An application to the functional times series prediction is used to compare our proposed estimator with the existing competitive estimators, and show its efficiency and superiority.

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Computational aspects of the kNN local linear smoothing for some conditional models in high dimensional statistics

Cited by 4 publications

References 40 publications

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Local linear-$k$NN smoothing for semi-functional partial linear regression

Smooth kNN Local Linear Estimation of the Conditional Distribution Function

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