Application of the cluster analysis for assessing the savings and investment potential of the population of the siberian federal district

The article presents a multidimensional cluster analysis of the regions of the Siberian Federal Level by the level of savings and investment potential of the population. Clustering was carried out using the ACC Statistica based on the joint use of hierarchical and non-hierarchical algorithms. This approach made it possible to increase the reliability of dividing the regions of the district into homogeneous groups. As a result, the heterogeneity of the regions of the Siberian Federal District in terms of the studied potential is revealed. Three clusters with a high, medium and low level of savings and investment potential of the population were obtained. Each resulting cluster provides a tool for making effective decisions at the level of both a single region and the district as a whole. The analysis made it possible to analyze the current state and trends in the development of the level of savings and investment potential of the population in the Siberian Federal District. And also to find out the reasons for the low level of the investigated potential.

Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm

Aiming at the lack of search depth of traditional genetic algorithm in automobile assembly line balance optimization, an improved genetic algorithm based on bagging integrated clustering is proposed for balance optimization. Through the integrated learning of several K -means algorithm based learners through bagging, a population clustering analysis method based on bagging integrated clustering algorithm is established, and then, a dual objective automobile assembly line balance optimization model is established. The population clustering analysis method is used to improve the intersection link of genetic algorithm to improve the search depth. The effectiveness and search performance of the improved genetic algorithm in solving the double objective assembly line balance problem are verified in an example.

Neural ADMIXTURE: rapid population clustering with autoencoders

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. In addition, this network can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. These models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

Consensify: A Method for Generating Pseudohaploid Genome Sequences from Palaeogenomic Datasets with Reduced Error Rates

A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.

Consensify: a method for generating pseudohaploid genome sequences from palaeogenomic datasets with reduced error rates

A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, typically by selecting a single high quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic analysis, we find that Consensify is less affected by branch length artefacts than methods based on standard pseudohaploidisation, and it performs similarly for population clustering analysis based on genetic distances. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other available methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consenify will be a useful tool for future studies of palaeogenomes.

RhierBAPS: An R implementation of the population clustering algorithm hierBAPS

Identifying structure in collections of sequence data sets remains a common problem in genomics. hierBAPS, a popular algorithm for identifying population structure in haploid genomes, has previously only been available as a MATLAB binary. We provide an R implementation which is both easier to install and use, automating the entire pipeline. Additionally, we allow for the use of multiple processors, improve on the default settings of the algorithm, and provide an interface with the ggtree library to enable informative illustration of the clustering results. Our aim is that this package aids in the understanding and dissemination of the method, as well as enhancing the reproducibility of population structure analyses.

Faktor-Faktor Yang Mempengaruhi Intensi Berwirausaha Pada Mahasiswa Program Studi Manajemen STIEM Bongaya

This study intens to to determine what factors influencing the students’ entrepreneurial interest of Bongaya Institute of Economics of Makassar (STIEM Bongaya Makassar). The population of the study was unidentified. However, the population clustering used the students on academic years of 2013 to 2015. The sample was taken by applying snowball sampling technique, so that the sample obtained was consisted of 45 respondents. Based on the result of the study, it proved that the factors of working independently, risk tolerance, and self-efficacy had the positive and significant influence on the students’ entrepreneurial interest of Bongaya Institute of Economics of Makassar. From the three of the factors, the dominant factor was working independently

Population clustering based on copy number variations detected from next generation sequencing data

Copy number variations (CNVs) can be used as significant bio-markers and next generation sequencing (NGS) provides a high resolution detection of these CNVs. But how to extract features from CNVs and further apply them to genomic studies such as population clustering have become a big challenge. In this paper, we propose a novel method for population clustering based on CNVs from NGS. First, CNVs are extracted from each sample to form a feature matrix. Then, this feature matrix is decomposed into the source matrix and weight matrix with non-negative matrix factorization (NMF). The source matrix consists of common CNVs that are shared by all the samples from the same group, and the weight matrix indicates the corresponding level of CNVs from each sample. Therefore, using NMF of CNVs one can differentiate samples from different ethnic groups, i.e. population clustering. To validate the approach, we applied it to the analysis of both simulation data and two real data set from the 1000 Genomes Project. The results on simulation data demonstrate that the proposed method can recover the true common CNVs with high quality. The results on the first real data analysis show that the proposed method can cluster two family trio with different ancestries into two ethnic groups and the results on the second real data analysis show that the proposed method can be applied to the whole-genome with large sample size consisting of multiple groups. Both results demonstrate the potential of the proposed method for population clustering.