scholarly journals Growth Score: a single metric to define growth in 96-well phenotype assays

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4681
Author(s):  
Daniel A. Cuevas ◽  
Robert A. Edwards
Keyword(s):  
Time Series ◽  
Systems Biology ◽  
High Throughput ◽  
Growth Curve ◽  
Time Series Data ◽  
Series Data ◽  
Direct Measurements ◽  
Essential Growth ◽  
Data Analytic ◽  
Require Data

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including ourPMAnalyzerpipeline.

scholarly journals ­Growth Score: A single metric to define growth in 96-well phenotype assays

2018 ◽  
Author(s):  
Daniel A Cuevas ◽  
Robert A Edwards
Keyword(s):  
Time Series ◽  
Systems Biology ◽  
High Throughput ◽  
Growth Curve ◽  
Time Series Data ◽  
Series Data ◽  
Direct Measurements ◽  
Essential Growth ◽  
Data Analytic ◽  
Require Data

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline.

scholarly journals ­Growth Score: A single metric to define growth in 96-well phenotype assays

2018 ◽  
Author(s):  
Daniel A Cuevas ◽  
Robert A Edwards
Keyword(s):  
Time Series ◽  
Systems Biology ◽  
High Throughput ◽  
Growth Curve ◽  
Time Series Data ◽  
Series Data ◽  
Direct Measurements ◽  
Essential Growth ◽  
Data Analytic ◽  
Require Data

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline.

Growth Curve Analysis in Marketing: Method and Application

1988 ◽  
Vol 25 (4) ◽  
pp. 391-396 ◽  
Author(s):  
Greg J. Lessne ◽  
R. Choudary Hanumara
Keyword(s):  
Time Series ◽  
Growth Curve ◽  
Time Series Data ◽  
Curve Analysis ◽  
Series Data ◽  
Growth Curve Analysis ◽  
Short Term ◽  
Market Data ◽  
Test Market

Extant methods are incapable of analyzing the short-term time series data often encountered by marketers. The authors present a growth curve approach developed by Finn that fills a void in the array of tools available to marketing researchers. The approach is particularly useful in analyzing test-market data.

scholarly journals Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Li C. Xia ◽  
Dongmei Ai ◽  
Jacob A. Cram ◽  
Xiaoyi Liang ◽  
Jed A. Fuhrman ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Chains ◽  
Trend Analysis ◽  
High Throughput ◽  
Time Series Data ◽  
Statistical Significance ◽  
Series Data ◽  
Throughput Time ◽  
Local Trend

Data Analysis and Visualization in Python for Polar Meteorological Data

2021 ◽  
Vol 2 (1) ◽  
pp. 32-60
Author(s):  
V. Sakthivel Samy ◽  
Koyel Pramanick ◽  
Veena Thenkanidiyoor ◽  
Jeni Victor
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Meteorological Data ◽  
Marked Change ◽  
Series Data ◽  
Future Data ◽  
Time Series Data Analysis ◽  
The Given ◽  
Data Analytic

The aim of this study is to analyze meteorological data obtained from the various expeditions made to the Indian stations in Antarctica over recent years and determine how significantly the weather has shown a marked change over the years. For any time series data analysis, there are two main goals: (a) the authors need to identify the nature of the phenomenon from the sequence of observations and (b) predict the future data. On account of these goals, the pattern in the time series data and its variability are to be accurately identified. This paper can then interpret and integrate the pattern established with its associated meteorological datasets collected in Antarctica. Using the data analytics knowledge the validity of interpretation for the given datasets a pattern has been identified, which could extrapolate the pattern towards prediction. To ease the time series data analysis, the authors developed online meteorological data analytic portal at NCPOR, Goa http://data.ncaor.gov.in/.

scholarly journals Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data

2015 ◽  
Vol 9 (Suppl 6) ◽  
pp. S4 ◽  
Author(s):  
Yung-Hao Wong ◽  
Chia-Chou Wu ◽  
Hsien-Yong Lai ◽  
Bo-Ren Jheng ◽  
Hsing-Yu Weng ◽  
...  
Keyword(s):  
Time Series ◽  
Systems Biology ◽  
Time Series Data ◽  
Cardioembolic Stroke ◽  
Series Data

scholarly journals SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Soumyashree Kar ◽  
Vincent Garin ◽  
Jana Kholová ◽  
Vincent Vadez ◽  
Surya S. Durbha ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area ◽  
High Throughput ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
Genotypic Differences ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
Over Time

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

scholarly journals Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

2018 ◽  
Author(s):  
Alexander M Crowell ◽  
Jennifer J. Loros ◽  
Jay C Dunlap
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
High Throughput ◽  
Reference Genes ◽  
Time Series Data ◽  
Prediction Interval ◽  
Series Data ◽  
Throughput Time ◽  
Additional Information ◽  
Ranking Score

AbstractMotivationIdentification of constitutive reference genes is critical for analysis of gene expression. Large numbers of high throughput time series expression data are available, but current methods for identifying invariant expression are not tailored for time series. Identification of reference genes from these data sets can benefit from methods which incorporate the additional information they provide.ResultsHere we show that we can improve identification of invariant expression from time series by modelling the time component of the data. We implement the Prediction Interval Ranking Score (PIRS) software, which screens high throughput time series data and provides a ranked list of reference candidates. We expect that PIRS will improve the quality of gene expression analysis by allowing researchers to identify the best reference genes for their system from publicly available time series.AvailabilityPIRS can be downloaded and installed with dependencies using ‘pip install pirs’ and Python code and documentation is available for download at https://github.com/aleccrowell/[email protected]

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