scholarly journals MLG: Multilayer graph clustering for multi-condition scRNA-seq data

2021 ◽  
Author(s):  
Shan Lu ◽  
Daniel J. Conn ◽  
Shuyang Chen ◽  
Kirby D Johnson ◽  
Emery H. Bresnick ◽  
...  
Keyword(s):  
Data Integration ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Graph Clustering ◽  
Cellular Heterogeneity ◽  
Integrative Approach ◽  
Signal To Noise ◽  
Noise Ratio

Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present Multi Layer Graph Clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale benchmarking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.

A modified exponential amplification reaction (EXPAR) with an improved signal-to-noise ratio for ultrasensitive detection of polynucleotide kinase

2019 ◽  
Vol 55 (53) ◽  
pp. 7611-7614 ◽  
Author(s):  
Yu-Peng Zhang ◽  
Yun-Xi Cui ◽  
Xiao-Yu Li ◽  
Yi-Chen Du ◽  
An-Na Tang ◽  
...  
Keyword(s):  
Single Cell ◽  
Kinase Activity ◽  
Signal To Noise Ratio ◽  
Ultrasensitive Detection ◽  
Single Cell Level ◽  
Signal To Noise ◽  
Cell Level ◽  
Polynucleotide Kinase ◽  
Noise Ratio ◽  
Amplification Reaction

We reported a modified exponential amplification reaction strategy and applied it to design an ultrasensitive biosensor for the detection of endogenous polynucleotide kinase activity at single-cell level.

scholarly journals De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition

2021 ◽  
Vol 13 (23) ◽  
pp. 4932
Author(s):  
Rui Zhou ◽  
Jiangtao Han ◽  
Zhenyu Guo ◽  
Tonglin Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Response Curves ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Decomposition

Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.

scholarly journals Comparison of linear spatial filters for identifying oscillatory activity in multichannel data

2016 ◽  
Author(s):  
Michael X Cohen
Keyword(s):  
Large Scale ◽  
Signal To Noise Ratio ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Oscillatory Activity ◽  
Signal To Noise ◽  
Spatial Filters ◽  
Electrical Fields ◽  
Noise Ratio ◽  
Filtering Techniques

AbstractBackgroundLarge-scale synchronous neural activity produces electrical fields that can be measured by electrodes outside the head, and volume conduction ensures that neural sources can be measured by many electrodes. However, most data analyses in M/EEG research are univariate, meaning each electrode is considered as a separate measurement. Several multivariate linear spatial filtering techniques have been introduced to the cognitive electrophysiology literature, but these techniques are not commonly used; comparisons across filters would be beneficial to the field.New methodThe purpose of this paper is to evaluate and compare the performance of several linear spatial filtering techniques, with a focus on those that use generalized eigendecomposition to facilitate dimensionality reduction and signal-to-noise ratio maximization.ResultsSimulated and empirical data were used to assess the accuracy, signal-to-noise ratio, and interpretability of the spatial filter results. When the simulated signal is powerful, different spatial filters provide convergent results. However, more subtle signals require carefully selected analysis parameters to obtain optimal results.Comparison with existing methodsLinear spatial filters can be powerful data analysis tools in cognitive electrophysiology, and should be applied more often; on the other hand, spatial filters can latch onto artifacts or produce uninterpretable results.ConclusionsHypothesis-driven analyses, careful data inspection, and appropriate parameter selection are necessary to obtain high-quality results when using spatial filters.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Speech Reception in the Presence of Classroom Noise

1979 ◽  
Vol 10 (4) ◽  
pp. 221-230 ◽  
Author(s):  
Veronica Smyth
Keyword(s):  
Signal To Noise Ratio ◽  
Learning Processes ◽  
Speech Discrimination ◽  
Signal To Noise ◽  
Speech Reception ◽  
Monosyllabic Words ◽  
Noise Ratio

Three hundred children from five to 12 years of age were required to discriminate simple, familiar, monosyllabic words under two conditions: 1) quiet, and 2) in the presence of background classroom noise. Of the sample, 45.3% made errors in speech discrimination in the presence of background classroom noise. The effect was most marked in children younger than seven years six months. The results are discussed considering the signal-to-noise ratio and the possible effects of unwanted classroom noise on learning processes.

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