Review of single-cell RNA-seq data clustering for cell-type identification and characterization.

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.

Wavelength-Controlled Photoconductance Polarity Switching via Harnessing Defects in Doped PdSe2 for Artificial Synaptic Features.

Optoelectronic synapses are currently drawing significant attention as fundamental building blocks of neuromorphic computing to mimic brain functions. In this study, a two-terminal synaptic device based on a doped PdSe2 flake is proposed to imitate the key neural functions in an optical pathway. Due to the wavelength-dependent desorption of oxygen clusters near the intrinsic selenide vacancy defects, the doped PdSe2 photodetector achieves a high negative photoresponsivity of -7.8 × 103 A W-1 at 473 nm and a positive photoresponsivity of 181 A W-1 at 1064 nm. This wavelength-selective bi-direction photoresponse endows an all-optical pathway to imitate the fundamental functions of artificial synapses on a device level, such as psychological learning and forgetting capability, as well as dynamic logic functions. The underpinning photoresponse is further demonstrated on a flexible platform, providing a viable technology for neuromorphic computing in wearable electronics. Furthermore, the p-type doping results in an effective increase of the channel's electrical conductivity and a significant reduction in power consumption. Such low-power-consuming optical synapses with simple device architecture and low-dimensional features demonstrate tremendous promise for building multifunctional artificial neuromorphic systems in the future.

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning.

Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs.

The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over 600 composite DNA sites (i.e. heterodimeric motifs) bound by cooperative TF pairs. However, there are over 25 000 inferentially effective heterodimeric TFs in the human cells. It is not practically feasible to validate all heterodimeric motifs due to cost and labor. We introduce DeepMotifSyn, a deep learning-based tool for synthesizing heterodimeric motifs from monomeric motif pairs. Specifically, DeepMotifSyn is composed of heterodimeric motif generator and evaluator. The generator is a U-Net-based neural network that can synthesize heterodimeric motifs from aligned motif pairs. The evaluator is a machine learning-based model that can score the generated heterodimeric motif candidates based on the motif sequence features. Systematic evaluations on CAP-SELEX data illustrate that DeepMotifSyn significantly outperforms the current state-of-the-art predictors. In addition, DeepMotifSyn can synthesize multiple heterodimeric motifs with different orientation and spacing settings. Such a feature can address the shortcomings of previous models. We believe DeepMotifSyn is a more practical and reliable model than current predictors on heterodimeric motif synthesis. Contact:kc.w@cityu.edu.hk.

Stiffness and BMP-2 Mimetic Peptide Jointly Regulate the Osteogenic Differentiation of Rat Bone Marrow Stromal Cells in a Gelatin Cryogel.

Both biochemical and mechanical cues could regulate the function of stem cells, but the interaction mechanism of their signaling pathway remains unclear, especially in the three-dimensional (3D) culture mode. Higher matrix stiffness promotes osteogenic differentiation of stem cells, and bone morphogenic protein-2 (BMP-2) has been clinically applied to promote bone regeneration. Here, the crosstalk of extracellular mechanical signals on BMP-2 signaling was investigated in rat bone marrow stromal cells (rMSCs) cultured inside cryogels with interconnective pores. Stiff cryogel independently promoted osteogenic differentiation and enhanced the autocrine secretion of BMP-2, thus stimulating increased phosphorylation levels of the Smad1/5/8 complex. BMP-2 mimetic peptide (BMMP) and high cryogel stiffness jointly guided the osteogenic differentiation of rMSCs. Inhibition of rho-associated kinase (ROCK) by Y-27632 or inhibition of nonmuscle myosin II (NM II) by blebbistatin showed that osteogenesis induction by BMP-2 signaling, as well as autocrine secretion of BMP-2 and phosphorylation of the Smad complex, requires the involvement of cytoskeletal tension and ROCK pathway signaling. An interconnective microporous cryogel scaffold promoted rMSC osteogenic differentiation by combining matrix stiffness and BMMP, and it accelerated critical cranial defect repair in the rat model.

Recovery-Based Occluded Face Recognition by Identity-Guided Inpainting.

Occlusion in facial photos poses a significant challenge for machine detection and recognition. Consequently, occluded face recognition for camera-captured images has emerged as a prominent and widely discussed topic in computer vision. The present standard face recognition methods have achieved remarkable performance in unoccluded face recognition but performed poorly when directly applied to occluded face datasets. The main reason lies in the absence of identity cues caused by occlusions. Therefore, a direct idea of recovering the occluded areas through an inpainting model has been proposed. However, existing inpainting models based on an encoder-decoder structure are limited in preserving inherent identity information. To solve the problem, we propose ID-Inpainter, an identity-guided face inpainting model, which preserves the identity information to the greatest extent through a more accurate identity sampling strategy and a GAN-like fusing network. We conduct recognition experiments on the occluded face photographs from the LFW, CFP-FP, and AgeDB-30 datasets, and the results indicate that our method achieves state-of-the-art performance in identity-preserving inpainting, and dramatically improves the accuracy of normal recognizers in occluded face recognition.

Ligand-Controlled Electroreduction of CO2 to Formate over Facet-Defined Bimetallic Sulfide Nanoplates.

CO2 reduction (CO2R) catalyzed by an efficient, stable, and earth-abundant electrocatalyst offers an attractive means to store energy derived from renewable sources. Here, we describe the synthesis of facet-defined Cu2SnS3 nanoplates and the ligand-controlled CO2R property. We show that thiocyanate-capped Cu2SnS3 nanoplates possess excellent selectivity toward formate over a wide range of potentials and current densities, attaining a maximum formate Faradaic efficiency of 92% and partial current densities as high as 181 mA cm-2 when tested using a flow cell with gas-diffusion electrode. In situ spectroscopic measurements and theoretical calculations reveal that the high formate selectivity originates from favorable adsorption of HCOO* intermediates on cationic Sn sites that are electronically modulated by thiocyanates bound to adjacent Cu sites. Our work illustrates that well-defined multimetallic sulfide nanocrystals with tailored surface chemistries could provide a new avenue for future CO2R electrocatalyst design.

Deep embedded clustering with multiple objectives on scRNA-seq data.

In recent years, single-cell RNA sequencing (scRNA-seq) technologies have been widely adopted to interrogate gene expression of individual cells; it brings opportunities to understand the underlying processes in a high-throughput manner. Deep embedded clustering (DEC) was demonstrated successful in high-dimensional sparse scRNA-seq data by joint feature learning and cluster assignment for identifying cell types simultaneously. However, the deep network architecture for embedding clustering is not trivial to optimize. Therefore, we propose an evolutionary multiobjective DEC by synergizing the multiobjective evolutionary optimization to simultaneously evolve the hyperparameters and architectures of DEC in an automatic manner. Firstly, a denoising autoencoder is integrated into the DEC to project the high-dimensional sparse scRNA-seq data into a low-dimensional space. After that, to guide the evolution, three objective functions are formulated to balance the model's generality and clustering performance for robustness. Meanwhile, migration and mutation operators are proposed to optimize the objective functions to select the suitable hyperparameters and architectures of DEC in the multiobjective framework. Multiple comparison analyses are conducted on twenty synthetic data and eight real data from different representative single-cell sequencing platforms to validate the effectiveness. The experimental results reveal that the proposed algorithm outperforms other state-of-the-art clustering methods under different metrics. Meanwhile, marker genes identification, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cell type identification and characterization mechanisms.

Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing.

Gene-expression profiling can define the cell state and gene-expression pattern of cells at the genetic level in a high-throughput manner. With the development of transcriptome techniques, processing high-dimensional genetic data has become a major challenge in expression profiling. Thanks to the recent widespread use of matrix decomposition methods in bioinformatics, a computational framework based on compressed sensing was adopted to reduce dimensionality. However, compressed sensing requires an optimization strategy to learn the modular dictionaries and activity levels from the low-dimensional random composite measurements to reconstruct the high-dimensional gene-expression data. Considering this, here we introduce and compare four compressed sensing frameworks coming from nature-inspired optimization algorithms (CSCS, ABCCS, BACS and FACS) to improve the quality of the decompression process. Several experiments establish that the three proposed methods outperform benchmark methods on nine different datasets, especially the FACS method. We illustrate therefore, the robustness and convergence of FACS in various aspects; notably, time complexity and parameter analyses highlight properties of our proposed FACS. Furthermore, differential gene-expression analysis, cell-type clustering, gene ontology enrichment and pathology analysis are conducted, which bring novel insights into cell-type identification and characterization mechanisms from different perspectives. All algorithms are written in Python and available at https://github.com/Philyzh8/Nature-inspired-CS.

Identification of pan-cancer Ras pathway activation with deep learning.

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1.

scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects. RESULTS: In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Changes in the salivary metabolome in patients with chronic erosive gastritis.

INTRODUCTION: Chronic erosive gastritis (CEG) is closely related to gastric cancer, which requires early diagnosis and intervention. The invasiveness and discomfort of electronic gastroscope have limited its application in the large-scale screening of CEG. Therefore, a simple and noninvasive screening method is needed in the clinic. OBJECTIVES: The aim of this study is to screen potential biomarkers that can identify diseases from the saliva samples of CEG patients using metabolomics. METHODS: Saliva samples from 64 CEG patients and 30 healthy volunteers were collected, and metabolomic analysis was performed using UHPLC-Q-TOF/MS in the positive and negative ion modes. Statistical analysis was performed using both univariate (Student's t-test) and multivariate (orthogonal partial least squares discriminant analysis) tests. Receiver operating characteristic (ROC) analysis was conducted to determine significant predictors in the saliva of CEG patients. RESULTS: By comparing the saliva samples from CEG patients and healthy volunteers, 45 differentially expressed metabolites were identified, of which 37 were up-regulated and 8 were down-regulated. These differential metabolites were related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. In the ROC analysis, the AUC values of 7 metabolites were greater than 0.8, among which the AUC values of 1,2-dioleoyl-sn-glycoro-3-phosphodylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) were greater than 0.9. CONCLUSIONS: In summary, a total of 45 metabolites were identified in the saliva of CEG patients. Among them, 1,2-dioleoyl-sn-glycoro-3-phosphorylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phosphorine (SOPC) might have potential clinical application value.

Uncovering the key dimensions of high-throughput biomolecular data using deep learning.

Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. However, the transcriptomic profiles are high-dimensional and sparse in nature. To address it, a deep learning framework based on auto-encoder, termed DeepAE, is proposed to elucidate high-dimensional transcriptomic profiling data in an encode-decode manner. Comparative experiments were conducted on nine transcriptomic profiling datasets to compare DeepAE with four benchmark methods. The results demonstrate that the proposed DeepAE outperforms the benchmark methods with robust performance on uncovering the key dimensions of single-cell RNA-seq data. In addition, we also investigate the performance of DeepAE in other contexts and platforms such as mass cytometry and metabolic profiling in a comprehensive manner. Gene ontology enrichment and pathology analysis are conducted to reveal the mechanisms behind the robust performance of DeepAE by uncovering its key dimensions.

EVtracker: An Event-Driven Spatiotemporal Method for Dynamic Object Tracking.

An event camera is a novel bio-inspired sensor that effectively compensates for the shortcomings of current frame cameras, which include high latency, low dynamic range, motion blur, etc. Rather than capturing images at a fixed frame rate, an event camera produces an asynchronous signal by measuring the brightness change of each pixel. Consequently, an appropriate algorithm framework that can handle the unique data types of event-based vision is required. In this paper, we propose a dynamic object tracking framework using an event camera to achieve long-term stable tracking of event objects. One of the key novel features of our approach is to adopt an adaptive strategy that adjusts the spatiotemporal domain of event data. To achieve this, we reconstruct event images from high-speed asynchronous streaming data via online learning. Additionally, we apply the Siamese network to extract features from event data. In contrast to earlier models that only extract hand-crafted features, our method provides powerful feature description and a more flexible reconstruction strategy for event data. We assess our algorithm in three challenging scenarios: 6-DoF (six degrees of freedom), translation, and rotation. Unlike fixed cameras in traditional object tracking tasks, all three tracking scenarios involve the simultaneous violent rotation and shaking of both the camera and objects. Results from extensive experiments suggest that our proposed approach achieves superior accuracy and robustness compared to other state-of-the-art methods. Without reducing time efficiency, our novel method exhibits a 30% increase in accuracy over other recent models. Furthermore, results indicate that event cameras are capable of robust object tracking, which is a task that conventional cameras cannot adequately perform, especially for super-fast motion tracking and challenging lighting situations.

Van der Waals Superstructure and Twisting in Self-Intercalated Magnet with Near Room-Temperature Perpendicular Ferromagnetism.

The emergence of van der Waals (vdW) magnets has created unprecedented opportunities to manipulate magnetism for advanced spintronics based upon all-vdW heterostructures. Among various vdW magnets, Cr1+δTe2 possesses high temperature ferromagnetism along with possible topological spin textures. As this system can support self-intercalation in the vdW gap, it is crucial to precisely pinpoint the exact intercalation to understand the intrinsic magnetism of the system. Here, we developed an iterative method to determine the self-intercalated structures and show evidence of vdW "superstructures" in individual Cr1+δTe2 nanoplates exhibiting magnetic behaviors distinct from bulk chromium tellurides. Among 26,332 possible configurations, we unambiguously identified the Cr-intercalated structure as 3-fold symmetry broken Cr1.5Te2 segmented by vdW gaps. Moreover, a twisted Cr-intercalated layered structure is observed. The spontaneous formation of twisted vdW "superstructures" not only provides insight into the diverse magnetic properties of intercalated vdW magnets but may also add complementary building blocks to vdW-based spintronics.

Bazi Bushen capsule attenuates cognitive deficits by inhibiting microglia activation and cellular senescence.

CONTEXT: Bazi Bushen capsule (BZBS) has anti-ageing properties and is effective in enhancing memory. OBJECTIVE: To find evidence supporting the mechanisms and biomarkers by which BZBS functions. MATERIALS AND METHODS: Male C57BL/6J mice were randomly divided into five groups: normal, ageing, ß-nicotinamide mononucleotide capsule (NMN), BZBS low-dose (LD-BZ) and BZBS high-dose (HD-BZ). The last four groups were subcutaneously injected with d-galactose (d-gal, 100 mg/kg/d) to induce the ageing process. At the same time, the LD-BZ, HD-BZ and NMN groups were intragastrically injected with BZBS (1 and 2 g/kg/d) and NMN (100 mg/kg/d) for treatment, respectively. After 60 days, the changes in overall ageing status, brain neuron morphology, expression of p16INK4a, proliferating cell nuclear antigen (PCNA), ionized calcium-binding adapter molecule 1 (Iba1), postsynaptic density protein 95 (PSD95), CD11b, Arg1, CD206, Trem2, Ym1 and Fizz1, and the senescence-associated secretory phenotype (SASP) factors were observed. RESULTS: Compared with the mice in the ageing group, the HD-BZ mice exhibited obvious improvements in strength, endurance, motor coordination, cognitive function and neuron injury. The results showed a decrease in p16INK4a, Iba1 and the upregulation of PCNA, PSD95 among brain proteins. The brain mRNA exhibited downregulation of Iba1 (p < 0.001), CD11b (p < 0.001), and upregulation of Arg1 (p < 0.01), CD206 (p < 0.05), Trem2 (p < 0.001), Ym1 (p < 0.01), Fizz1 (p < 0.05) and PSD95 (p < 0.01), as well as improvement of SASP factors. CONCLUSIONS: BZBS improves cognitive deficits via inhibition of cellular senescence and microglia activation. This study provides experimental evidence for the wide application of BZBS in clinical practice for cognitive deficits.

Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning.

MOTIVATION: In recent years, single-cell RNA sequencing enables us to discover cell types or even subtypes. Its increasing availability provides opportunities to identify cell populations from single-cell RNA-seq data. Computational methods have been employed to reveal the gene expression variations among multiple cell populations. Unfortunately, the existing ones can suffer from realistic restrictions such as experimental noises, numerical instability, high dimensionality and computational scalability. RESULTS: We propose an evolutionary multiobjective ensemble pruning algorithm (EMEP) that addresses those realistic restrictions. Our EMEP algorithm first applies the unsupervised dimensionality reduction to project data from the original high dimensions to low-dimensional subspaces; basic clustering algorithms are applied in those new subspaces to generate different clustering results to form cluster ensembles. However, most of those cluster ensembles are unnecessarily bulky with the expense of extra time costs and memory consumption. To overcome that problem, EMEP is designed to dynamically select the suitable clustering results from the ensembles. Moreover, to guide the multiobjective ensemble evolution, three cluster validity indices including the overall cluster deviation, the within-cluster compactness and the number of basic partition clusters are formulated as the objective functions to unleash its cell type discovery performance using evolutionary multiobjective optimization. We applied EMEP to 55 simulated datasets and seven real single-cell RNA-seq datasets, including six single-cell RNA-seq dataset and one large-scale dataset with 3005 cells and 4412 genes. Two case studies are also conducted to reveal mechanistic insights into the biological relevance of EMEP. We found that EMEP can achieve superior performance over the other clustering algorithms, demonstrating that EMEP can identify cell populations clearly. AVAILABILITY AND IMPLEMENTATION: EMEP is written in Matlab and available at https://github.com/lixt314/EMEP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications.

MOTIVATION: The RNA-guided CRISPR/Cas9 system has been widely applied to genome editing. CRISPR/Cas9 system can effectively edit the on-target genes. Nonetheless, it has recently been demonstrated that many homologous off-target genomic sequences could be mutated, leading to unexpected gene-editing outcomes. Therefore, a plethora of tools were proposed for the prediction of off-target activities of CRISPR/Cas9. Nonetheless, each computational tool has its own advantages and drawbacks under diverse conditions. It is hardly believed that a single tool is optimal for all conditions. Hence, we would like to explore the ensemble learning potential on synergizing multiple tools with genomic annotations together to enhance its predictive abilities. RESULTS: We proposed an ensemble learning framework which synergizes multiple tools together to predict the off-target activities of CRISPR/Cas9 in different combinations. Interestingly, the ensemble learning using AdaBoost outperformed other individual off-target predictive tools. We also investigated the effect of evolutionary conservation (PhyloP and PhastCons) and chromatin annotations (ChromHMM and Segway) and found that only PhyloP can enhance the predictive capabilities further. Case studies are conducted to reveal ensemble insights into the off-target predictions, demonstrating how the current study can be applied in different genomic contexts. The best prediction predicted by AdaBoost is up to 0.9383 (AUC) and 0.2998 (PRC) that outperforms other classifiers. This is ascribable to the fact that AdaBoost introduces a new weak classifier (i.e. decision stump) in each iteration to learn the DNA sequences that were misclassified as off-targets until a small error rate is reached iteratively. AVAILABILITY AND IMPLEMENTATION: The source codes are freely available on GitHub at https://github.com/Alexzsx/CRISPR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Ni-PyBisulidine complex for the asymmetric hydrophosphonylation of aldehydes.

A novel Ni-PyBisulidine complex has been developed for the asymmetric hydrophosphonylation of aldehydes. A variety of aromatic, heteroaromatic, condensed-ring, α,ß-unsaturated, and aliphatic aldehydes are found to be suitable substrates for the reaction, and the desired α-hydroxy phosphonates are obtained in up to 99% yield and 97% ee.

Electrical spin injection and detection in silicon nanowires through oxide tunnel barriers.

We demonstrate all-electrical spin injection, transport, and detection in heavily n-type-doped Si nanowires using ferromagnetic Co/Al(2)O(3) tunnel barrier contacts. Analysis of both local and nonlocal spin valve signals at 4 K on the same nanowire device using a standard spin-transport model suggests that high spin injection efficiency (up to ~30%) and long spin diffusion lengths (up to ~6 µm) are achieved. These values exceed those reported for spin transport devices based on comparably doped bulk Si. The spin valve signals are found to be strongly bias and temperature dependent and can invert sign with changes in the dc bias current. The influence of the nanowire morphology on field-dependent switching of the contacts is also discussed. Owing to their nanoscale geometry, ~5 orders of magnitude less current is required to achieve nonlocal spin valve voltages comparable to those attained in planar microscale spin transport devices, suggesting lower power consumption and the potential for applications of Si nanowires in nanospintronics.