A robust semi-parametric normalization technique has been developed, based on the assumption that the large majority of genes will not have their relative expression levels changed from one treatment group to the next, and on the assumption that departures of the response from linearity are small and slowly varying. The method was tested using data simulated under various error models and it performs well.

Dataset Title: Data and Code for: "Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System" Description: This dataset contains source data and processing results for validating the Adaptive Multi-Interval Scale (AMIS) normalization method. Includes educational performance data (student grades), economic statistics (World Bank GDP), and Python implementation of the AMIS algorithm with graphical interface. Contents: - Source data: educational grades and GDP statistics - AMIS normalization results (3, 5, 9, 17-point models) - Comparative analysis with linear normalization - Ready-to-use Python code for data processing Applications: - Educational data normalization and analysis - Economic indicators comparison - Development of unified metric systems - Methodology research in data scaling Technical info: Python code with pandas, numpy, scipy, matplotlib dependencies. Data in Excel format.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set’s suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named ’lfproQC’ and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination.

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorizing into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the recent statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

This updated version includes a Python script (glucose_analysis.py) that performs statistical evaluation of the glucose normalization process described in the associated thesis. The script supports key analyses, including normality assessment (Shapiro–Wilk test), variance homogeneity (Levene’s test), mean comparison (ANOVA), effect size estimation (Cohen’s d), and calculation of confidence intervals for the mean difference. These results validate the impact of Min-Max normalization on clinical data structure and usability within CDSS workflows. The script is designed to be reproducible and complements the processed dataset already included in this repository.

A number of procedures for normalization and detection of differentially expressed genes have been proposed. Four different normalization methods and all possible combinations with three different statistical algorithms have been used for detection of differentially expressed genes on a dataset. The number of genes detected as differentially expressed differs by a factor of about three.

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

Study comparing scaling with ranked subsampling (SRS) and rarefying for the normalization of species count data.

Context

In 2015/2016, PDX Monthly began to collect statistical summary data about Portland real estate on a neighborhood by neighborhood basis.

This data set aggregates and cleans up that content. The data normalization code can be found in Github.

Content

In general, the dataset contains statistical aggregations (average sale price, average days on market, etc). The PDX Monthly crowd did not provide the underlying data, and the changed their columns over the years, so the data is a bit messy.

In this article, we propose a class of test statistics for a change point in the mean of high-dimensional independent data. Our test integrates the U-statistic based approach in a recent work by Wang et al. and the Lq-norm based high-dimensional test in a recent work by He et al., and inherits several appealing features such as being tuning parameter free and asymptotic independence for test statistics corresponding to even q’s. A simple combination of test statistics corresponding to several different q’s leads to a test with adaptive power property, that is, it can be powerful against both sparse and dense alternatives. On the estimation front, we obtain the convergence rate of the maximizer of our test statistic standardized by sample size when there is one change-point in mean and q = 2, and propose to combine our tests with a wild binary segmentation algorithm to estimate the change-point number and locations when there are multiple change-points. Numerical comparisons using both simulated and real data demonstrate the advantage of our adaptive test and its corresponding estimation method.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

CERN-SPS. NA4/BCDMS Collaboration. Plab 120 - 280 GeV/c. These are data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a deuterium target. The ranges of X,Q**2 are 0.06& lt;X& lt;0.8 and 8& lt;Q**2& lt;260 GeV**2. CR.= The publication lists values of F2 corresponding to R=0 and R=R(QCD) at each of the three energies, 120, 200 and 280 GeV. As well as the statistical errors also given are 5 factors representing the effects of estimated systematic errors on F2 associated with (1) beam momentum calibration, (2) magnetic field calibration, (3) spectrometer resolution, (4) detector and trigger inefficiencies, and (5) relative normalization uncertainty of data taken from external and internal targets. This record contains our attempt to merge these data at different energies using the statistical errors as weight factors. The final one-sigma systematic errors given here have been calculated using a prescription from the authors involving calculation of new merged F2 values for each of the systematic errors applied individually, and the combining in quadrature the differences in the new merged F2 values and the original F2. The individual F2 values at each energy are given in separate database records. Plab 120 GeV/c. These are data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a deuterium target. The ranges of X,Q**2 are 0.06& lt;X& lt;0.8 and 8& lt;Q**2& lt;260 GeV**2. CR.= The publication lists values of F2 corresponding to R=0 and R=R(QCD) at each of the three energies, 120, 200 and 280 GeV. As well as the statistical errors also given are 5 factors representing the effects of estimated systematic errors on F2 associated with (1) beam momentum calibration, (2) magnetic field calibration, (3) spectrometer resolution, (4) detector and trigger inefficiencies, and (5) relative normalization uncertainty of data taken from external and internal targets. The systematic error shown in the tables is a result of combining together the 5 individual errors according to a prescription provided by the authors. The method involves taking the quadratic sum of the errors from each source. Plab 200 GeV/c. These are data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a deuterium target. The ranges of X,Q**2 are 0.06& lt;X& lt;0.8 and 8& lt;Q**2& lt;260 GeV**2. CR.= The publication lists values of F2 corresponding to R=0 and R=R(QCD) at each of the three energies, 120, 200 and 280 GeV. As well as the statistical errors also given are 5 factors representing the effects of estimated systematic errors on F2 associated with (1) beam momentum calibration, (2) magnetic field calibration, (3) spectrometer resolution, (4) detector and trigger inefficiencies, and (5) relative normalization uncertainty of data taken from external and internal targets. The systematic error shown in the tables is a result of combining together the 5 individual errors according to a prescription provided by the authors. This method involves taking the quadratic sum of the errors from each source. Plab 280 GeV/c. These are data from the BCDMS Collaboration on F2 and R=SIG(L)/SIG(T) with a deuterium target. The ranges of X,Q**2 are 0.06& lt;X& lt;0.8 and 8& lt;Q**2& lt;260 GeV**2. CR.= The publication lists values of F2 corresponding to R=0 and R=R(QCD) at each of the three energies, 120, 200 and 280 GeV. As well as the statistical errors also given are 5 factors representing the effects of estimated systematic errors on F2 associated with (1) beam momentum calibration, (2) magnetic field calibration, (3) spectrometer resolution, (4) detector and trigger inefficiencies, and (5) relative normalization uncertainty of data taken from external and internal targets. The systematic error shown in the tables is a result of combining together the 5 individual errors according to a prescription provided by the authors. This method involves taking the quadratic sum of the errors from each source.

Statistical mirroring is the measure of the proximity or deviation of transformed data points from a specified location estimate within a given distribution [2]. Within the framework of Kabirian-based optinalysis [1], statistical mirroring is conceptualized as the isoreflectivity of the transformed data points to a defined statistical mirror. This statistical mirror is an amplified location estimate of the distribution, achieved through a specified size or length. The location estimate may include parameters such as the mean, median, mode, maximum, minimum, or reference value [2]. The process of statistical mirroring comprises two distinct phases: a) Preprocessing phase [2]: This involves applying preprocessing transformations, such as compulsory theoretical ordering, with or without centering the data. It also encompasses tasks like statistical mirror design and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the preprocessed data and its designed statistical mirror. b) Optinalytic model calculation phase [1]: This phase is focused on computing estimates based on Kabirian-based isomorphic optinalysis models.

References: [1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. https://doi.org/10.1016/j.mex.2023.102400 [2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682

Raw data corresponding to Gabay, Y. & Holt, L. L. (submitted). Short-Term Adaptation to Sound Statistics is Unimpaired in Developmental Dyslexia. The data support a study examining the influence of the long-term average spectrum of preceding speech and nonspeech sounds on speech categorization among adult listeners with developmental dyslexia and typical control listeners.

In this project, I have done exploratory data analysis on the UCI Automobile dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

This dataset consists of data From the 1985 Ward's Automotive Yearbook. Here are the sources

1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

Number of Instances: 398 Number of Attributes: 9 including the class attribute

Attribute Information:

mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string (unique for each instance)

This data set consists of three types of entities:

I - The specification of an auto in terms of various characteristics

II - Tts assigned an insurance risk rating. This corresponds to the degree to which the auto is riskier than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale. Actuaries call this process "symboling".

III - Its normalized losses in use as compared to other cars. This is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc...), and represents the average loss per car per year.

The analysis is divided into two parts:

Data Wrangling

1. Pre-processing data in python
2. Dealing with missing values
3. Data formatting
4. Data normalization
5. Binning

6. Exploratory Data Analysis

7. Descriptive statistics

8. Groupby

9. Analysis of variance

10. Correlation

11. Correlation stats

Acknowledgment Dataset: UCI Machine Learning Repository Data link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

BackgroundHigh throughput molecular-interaction studies using immunoprecipitations (IP) or affinity purifications are powerful and widely used in biology research. One of many important applications of this method is to identify the set of RNAs that interact with a particular RNA-binding protein (RBP). Here, the unique statistical challenge presented is to delineate a specific set of RNAs that are enriched in one sample relative to another, typically a specific IP compared to a non-specific control to model background. The choice of normalization procedure critically impacts the number of RNAs that will be identified as interacting with an RBP at a given significance threshold – yet existing normalization methods make assumptions that are often fundamentally inaccurate when applied to IP enrichment data.MethodsIn this paper, we present a new normalization methodology that is specifically designed for identifying enriched RNA or DNA sequences in an IP. The normalization (called adaptive or AD normalization) uses a basic model of the IP experiment and is not a variant of mean, quantile, or other methodology previously proposed. The approach is evaluated statistically and tested with simulated and empirical data.Results and ConclusionsThe adaptive (AD) normalization method results in a greatly increased range in the number of enriched RNAs identified, fewer false positives, and overall better concordance with independent biological evidence, for the RBPs we analyzed, compared to median normalization. The approach is also applicable to the study of pairwise RNA, DNA and protein interactions such as the analysis of transcription factors via chromatin immunoprecipitation (ChIP) or any other experiments where samples from two conditions, one of which contains an enriched subset of the other, are studied.

Study Characterization of the physiological variations of the metabolome in biofluids is critical to understand human physiology and to avoid confounding effects in cohort studies aiming at biomarker discovery. Dataset In this study conducted by the MetaboHUB French Infrastructure for Metabolomics, urine samples from 184 volunteers were analyzed by reversed-phase (C18) ultrahigh performance liquid chromatography (UPLC) coupled to high-resolution mass spectrometry (LTQ-Orbitrap). A total of 258 metabolites were identified at confidence levels provided by the metabolomics standards initiative (MSI) levels 1 or 2. Workflow This history describes the statistical analysis of the data set from the negative ionization mode (113 identified metabolites at MSI levels 1 or 2): correction of signal drift (loess model built on QC pools) and batch effects (two batches), variable filtering (QC coefficent of variation < 30%), normalization by the sample osmolality, log10 transformation, sample filtering (Hotelling, decile and missing pvalues > 0.001) resulting in the HU_096 sample being discarded, univariate hypothesis testing of significant variations with age, BMI, or between genders (FDR < 0.05), and OPLS(-DA) modeling of age, BMI and gender. Comments The ‘sacurine’ data set (after normalization and filtering) is also available in the ropls R package from the Bioconductor repository. For a comprehensive analysis of the dataset (starting from the preprocessing of the raw files and including all detected features in the subsequent steps), please see the companion ‘W4M00002_Sacurine-comprehensive’ reference history.

The advent of high-throughput technologies such as ChIP-seq has made possible the study of histone modifications. A problem of particular interest is the identification of regions of the genome where different cell types from the same organism exhibit different patterns of histone enrichment. This problem turns out to be surprisingly difficult, even in simple pairwise comparisons, because of the significant level of noise in ChIP-seq data. In this paper we propose a two-stage statistical method, called ChIPnorm, to normalize ChIP-seq data, and to find differential regions in the genome, given two libraries of histone modifications of different cell types. We show that the ChIPnorm method removes most of the noise and bias in the data and outperforms other normalization methods. We correlate the histone marks with gene expression data and confirm that histone modifications H3K27me3 and H3K4me3 act as respectively a repressor and an activator of genes. Compared to what was previously reported in the literature, we find that a substantially higher fraction of bivalent marks in ES cells for H3K27me3 and H3K4me3 move into a K27-only state. We find that most of the promoter regions in protein-coding genes have differential histone-modification sites. The software for this work can be downloaded from http://lcbb.epfl.ch/software.html.

Peru Social Security: Number of Contributors data was reported at 568,178.000 Person in 2017. This records an increase from the previous number of 559,250.000 Person for 2016. Peru Social Security: Number of Contributors data is updated yearly, averaging 473,923.000 Person from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 568,178.000 Person in 2017 and a record low of 319,254.000 Person in 1995. Peru Social Security: Number of Contributors data remains active status in CEIC and is reported by Social Security Normalization Office. The data is categorized under Global Database’s Peru – Table PE.G010: Social Security Statistics.