scholarly journals SeroTools: a Python package for Salmonella serotype data analysis

2020 ◽  
Vol 5 (53) ◽  
pp. 2556
Author(s):  
Joseph Baugher,
Keyword(s):  
Data Analysis ◽  
Salmonella Serotype ◽  
Python Package

scholarly journals OceanSpy: A Python package to facilitate ocean model data analysis and visualization

2019 ◽  
Vol 4 (39) ◽  
pp. 1506 ◽  
Author(s):  
Mattia Almansi ◽  
Renske Gelderloos ◽  
Thomas Haine ◽  
Atousa Saberi ◽  
Ali Siddiqui
Keyword(s):  
Data Analysis ◽  
Ocean Model ◽  
Model Data ◽  
Python Package

scholarly journals Two useful Python tools - dimpy and tablefile for data analysis applications

2021 ◽  
Author(s):  
Dwaipayan Deb
Keyword(s):  
Data Analysis ◽  
Data File ◽  
List Type ◽  
Python Package ◽  
Mathematics And Physics

Abstract Python’s list array is more powerful than arrays in other languages like C, C++, Fortran, or Java. However, in some cases it becomes tedious and complicated to construct a multidimensional ‘list’ type array in Python. A Python tool namely ‘dimpy’ is discussed in this paper which can easily generate any multidimensional ‘list’ type array in python. Another Python package called tablefile for reading and analysing column-wise data from a data-file is also discussed. How these two tools may be useful and reduce steps of programming is shown by using some mathematics and physics related problems.

scholarly journals JMASM 53: MicceriRD

2020 ◽  
Vol 18 (2) ◽  
pp. 2-4
Author(s):  
Michael Lance
Keyword(s):  
Monte Carlo ◽  
Data Analysis ◽  
Monte Carlo Simulations ◽  
Python Package ◽  
Fortran 77

Fortran 77 and 90 modules (REALPOPS.lib) exist for invoking the 8 distributions estimated by Micceri (1989). These respective modules were created by Sawilowsky et al. (1990) and Sawilowsky and Fahoome (2003). The MicceriRD (Micceri’s Real Distributions) Python package was created because Python is increasingly used for data analysis and, in some cases, Monte Carlo simulations.

scholarly journals EMDA: A Python package for Electron Microscopy Data Analysis

2021 ◽  
pp. 107826
Author(s):  
Rangana Warshamanage ◽  
Keitaro Yamashita ◽  
Garib N. Murshudov
Keyword(s):  
Electron Microscopy ◽  
Data Analysis ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Python Package

scholarly journals Scedar: a scalable Python package for single-cell RNA-seq exploratory data analysis

2018 ◽  
Author(s):  
Yuanchao Zhang ◽  
Man S. Kim ◽  
Erin R. Reichenberger ◽  
Ben Stear ◽  
Deanne M. Taylor
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Large Scale ◽  
Exploratory Data Analysis ◽  
Statistical Distributions ◽  
Rna Seq ◽  
Exploratory Data ◽  
Further Development ◽  
Python Package ◽  
Open Source Development

AbstractIn single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

scholarly journals fuelcell: A Python package and graphical user interface for electrochemical data analysis

2021 ◽  
Vol 6 (59) ◽  
pp. 2940
Author(s):  
Samay Garg ◽  
Julie Fornaciari ◽  
Adam Weber ◽  
Nemanja Danilovic
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Graphical User Interface ◽  
Python Package

scholarly journals Seqfam: A python package for analysis of Next Generation Sequencing DNA data in families

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 281
Author(s):  
Matthew Frampton ◽  
Elena R. Schiff ◽  
Nikolas Pontikos ◽  
Anthony W. Segal ◽  
Adam P. Levine
Keyword(s):  
Data Analysis ◽  
Next Generation Sequencing ◽  
Rare Variants ◽  
Pedigree Information ◽  
Attractive Alternative ◽  
Next Generation ◽  
Kinship Coefficients ◽  
Single Marker ◽  
Python Package ◽  
Generation Sequencing

This article introduces seqfam, a python package which is primarily designed for analysing next generation sequencing (NGS) DNA data from families with known pedigree information in order to identify rare variants that are potentially causal of a disease/trait of interest. It uses the popular and versatile Pandas library, and can be straightforwardly integrated into existing analysis code/pipelines. Seqfam can be used to verify pedigree information, to perform Monte Carlo gene dropping, to undertake regression-based gene burden testing, and to identify variants which segregate by affection status in families via user-defined pattern of occurrence rules. Additionally, it can generate scripts for running analyses in a “MapReduce pattern” on a computer cluster, something which is usually desirable in NGS data analysis and indeed “big data” analysis in general. This article summarises how seqfam’s main user functions work and motivates their use. It also provides explanatory context for example scripts and data included in the package which demonstrate use cases. With respect to verifying pedigree information, software exists for efficiently calculating kinship coefficients, so seqfam performs the necessary extra steps of mapping pedigrees and kinship coefficients to expected and observed degrees of relationship respectively. Gene dropping and the application of variant pattern of occurrence rules in families can provide evidence for a variant being causal. The authors are unaware of other software which performs these tasks in familial cohorts, so seqfam fulfils this need. Gene burden rather than single marker tests are often used to detect rare causal variants due to greater power. Seqfam may be an attractive alternative to existing gene burden testing software due to its flexibility, particularly in grouping and aggregating variants.

scholarly journals Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis

2020 ◽  
Vol 16 (4) ◽  
pp. e1007794
Author(s):  
Yuanchao Zhang ◽  
Man S. Kim ◽  
Erin R. Reichenberger ◽  
Ben Stear ◽  
Deanne M. Taylor
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Exploratory Data Analysis ◽  
Rna Seq ◽  
Exploratory Data ◽  
Python Package
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