Analysis of data measured on different scales is a relevant challenge. Biomedical studies often focus on high-throughput datasets of, e.g., quantitative measurements. However, the need for integration of other features possibly measured on different scales, e.g. clinical or cytogenetic factors, becomes increasingly important. The analysis results (e.g. a selection of relevant genes) are then visualized, while adding further information, like clinical factors, on top. However, a more integrative approach is desirable, where all available data are analyzed jointly, and where also in the visualization different data sources are combined in a more natural way. Here we specifically target integrative visualization and present a heatmap-style graphic display. To this end, we develop and explore methods for clustering mixed-type data, with special focus on clustering variables. Clustering of variables does not receive as much attention in the literature as does clustering of samples. We extend the variables clustering methodology by two new approaches, one based on the combination of different association measures and the other on distance correlation. With simulation studies we evaluate and compare different clustering strategies. Applying specific methods for mixed-type data proves to be comparable and in many cases beneficial as compared to standard approaches applied to corresponding quantitative or binarized data. Our two novel approaches for mixed-type variables show similar or better performance than the existing methods ClustOfVar and bias-corrected mutual information. Further, in contrast to ClustOfVar, our methods provide dissimilarity matrices, which is an advantage, especially for the purpose of visualization. Real data examples aim to give an impression of various kinds of potential applications for the integrative heatmap and other graphical displays based on dissimilarity matrices. We demonstrate that the presented integrative heatmap provides more information than common data displays about the relationship among variables and samples. The described clustering and visualization methods are implemented in our R package CluMix available from https://cran.r-project.org/web/packages/CluMix.

This dataset shows the individual SUS and preference scores of the HMD and DB applications.

This is data collected as part of the PhD thesis 'Doctor Doom In The Marvel Age: An Empirical Approach To Transmedia Character Coherence'. 266 Texts were identified in which Doctor Doom appeared, taken from comics dated November 1961 to October 1987 - 'The Marvel Age' - and from other media texts issued contemporaneously.From this corpus, a sample of 69 texts was selected using stratified random sampling.Each text in the sample was examined for signifiers to do with Doctor Doom. The data was recorded using a unified catalogue of transmedia character components which brings together aspects of the models devised by Pearson and Uricchio, Klastrup and Tosca, Marie-Laurie Ryan, Paolo Bertetti and Matthew Freeman within a framework based on Jan-Noël Thon's ideas of Transmedia Character Networks that extends Henry Jenkin's formulation of 'transmedia' in line with Scolari, Bertetti and Freeman's Transmedia Archaeology. Where gaps were identified within these definitions, specifically around the area of 'behaviour', additional definitions were brought in using the psycholexical approach, the Big Five Index, and the idea of character motivations from creative writing practice. Where necessary the components were re-named for clarity, and finally were placed into groups based on Matthew Freeman's classification of transmedia, with 'behaviour' extracted into a group of its own. In theory this catalogue can be used as a tool for mapping the coherence of transmedia characters as they move across time and media. Used over the course of a sample of texts, and by recording the signifiers within each component for each text, it should be possible not only to identify a character's core components across time, but also to see whether they vary across different media or storyworlds. This idea is investigated within the thesis itself.

Use the resources below to understand how smart mapping tools work and how to apply them to enhance your mapping projects.GoalsAccess available smart mapping options to explore and better understand your data.Style maps using cartographically correct symbology for qualitative and quantitative data.Use Arcade expressions to quickly customize data display and map elements.

The Environment Agency uses semi-quantitative gas chromatography-mass spectrometry (GC-MS and LC-MS) targeted screening methods to analyse water samples for a wide range of substances at once. This dataset represents positive GC-MS and LC-MS results for samples collected and analysed by the Environment Agency since 2007.

More specifically, the data shows what substance has been detected in a sample, the specific method used, the analytical method limit of detection, the sampling location and time, sample type, purpose and material. Where available, a semi quantitative concentration measurement (µg/l) is reported. As the target database is continually being updated the date of addition is also listed against each substance.

Users should ensure they have read the 'Read Me' file included in the downloaded package in full before viewing or making use of the data, as it contains important information.

Code and associated data for the following preprint:

AP Browning, JA Sharp, RJ Murphy, G Gunasingh, B Lawson, K Burrage, NK Haass, MJ Simpson. 2021 Quantitative analysis of tumour spheroid structure. eLife http://dx.doi.org/https://doi.org/10.7554/eLife.73020

Data comprises measurements relating to the size and inner structure of spheroids grown from WM793b and WM983b melanoma cells over up to 24 days.

Code, data, and interactive figures are available as a Julia module on GitHub:

Browning AP (2021) Github ID v.0.6.2. Quantitative analysis of tumour spheroid structure. https://github.com/ap-browning/Spheroids

(copy archived here)

Code used to process the experimental images is available on Zenodo:

Browning AP, Murphy RJ (2021) Zenodo Image processing algorithm to identify structure of tumour spheroids with cell cycle labelling. https://doi.org/10.5281/zenodo.5121093

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

Graphics are very effective for communicating numerical information quickly and efficiently, but many of the design choices we make are based on subjective measures, such as personal taste or conventions of the discipline rather than objective criteria. We briefly introduce perceptual principles such as preattentive features and gestalt heuristics, and then discuss the design and results of a factorial experiment examining the effect of plot aesthetics such as color and trend lines on participants’ assessment of ambiguous data displays. The quantitative and qualitative experimental results strongly suggest that plot aesthetics have a significant impact on the perception of important features in data displays. Supplementary materials for this article are available online.

Map InformationThis nowCOAST time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14 inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST every hour for the period ending at the top of the hour. The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST shortly afterwards. For more detailed information about the update schedule.Background InformationThe NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-h precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.<=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields. (Both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-,6-,12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program.Time InformationThis map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation.References For more information about the MRMS/Q3 system.

This reference contains geospatial datasets (shapefiles) for the Siletz Bay NWR: Tidal Marsh Restoration and Reference Sites: Baseline Plant Community Monitoring and Mapping inventory performed by Green Point Consulting in 2001. During summer 2001, Green Point Consulting (GPC) conducted baseline monitoring and computerized (GIS) mapping of emergent wetland plant communities at USFWS tidal marsh restoration sites and matched reference sites in the Siletz and Nestucca estuaries. The monitoring work used a robust, quantitative protocol which allows comparison of results to tidal marsh monitoring activities elsewhere on the west coast. Plant community composition was measured, and results were used to map plant communities throughout the restoration and reference sites. The goals of the project were to assist USFWS in restoration planning, implementation, evaluation, and adaptive management; and to advance scientific understanding of west coast estuaries. Plant community classification and mapping also provides the basis for stratification of further sampling in future monitoring projects. This project's vegetation analysis and mapping modifies and adds detail to the estuary-wide mapping of Siletz tidal marsh completed by GPC for the Confederated Tribes of Siletz Indians in 2001. Products include GIS layers of plant communities and sampling locations; paper maps of plant communities; graphic displays of plant community composition; photodocumentation of transects; spreadsheets of quantitative data collected and analysis of that data; and this narrative report. This project was not designed to provide data for regulatory requirements (such as wetland fill-removal permits), but it's results may be useful as supplementary data in a regulatory context.

Map Information This nowCOAST time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14 inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST every hour for the period ending at the top of the hour. The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST shortly afterwards. For more detailed information about the update schedule, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule Background Information The NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-h precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields. (Both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-,6-,12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program. Time Information This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component. This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned. In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service. Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service: Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the Return Updates link at the bottom of this page under Supported Operations. Refer to the ArcGIS REST API Map Service Documentation for more information. Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the Image Footprints with Time Attributes layer in the same group as the target Image layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following: validtime: Valid timestamp. starttime: Display start time. endtime: Display end time. reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time). projmins: Number of minutes from reference time to valid time. desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match. desigprojmins: Number of minutes from designated reference time to valid time. Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available time stops (i.e. valid times), individual timestamps, or the valid time of a layer's latest available data (i.e. Product Time). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfo References For more information about the MRMS/Q3 system, please see http://nmq.ou.edu and http://www.nssl.noaa.gov/projects/mrms.

A dominance hierarchy is an important feature of the social organisation of group living animals. Although formal and/or agonistic dominance has been found in captive wolves and free-ranging dogs, applicability of the dominance concept in domestic dogs is highly debated, and quantitative data are scarce. Therefore, we investigated 7 body postures and 24 behaviours in a group of domestic dogs for their suitability as formal status indicators. The results showed that high posture, displayed in most dyadic relationships, and muzzle bite, displayed exclusively by the highest ranking dogs, qualified best as formal dominance indicators. The best formal submission indicator was body tail wag, covering most relationships, and two low postures, covering two-thirds of the relationships. In addition, both mouth lick, as included in Schenkel's active submission, and pass under head qualified as formal submission indicators but were shown almost exclusively towards the highest ranking dogs. Furthermore, a status assessment based on changes in posture displays, i.e., lowering of posture (LoP) into half-low, low, low-on-back or on-back, was the best status indicator for most relationships as it showed good coverage (91% of the dyads), a nearly linear hierarchy (h' = 0.94, p<0.003) and strong unidirectionality (DCI = 0.97). The associated steepness of 0.79 (p<0.0001) indicated a tolerant dominance style for this dog group. No significant correlations of rank with age or weight were found. Strong co-variation between LoP, high posture, and body tail wag justified the use of dominance as an intervening variable. Our results are in line with previous findings for captive wolves and free-ranging dogs, for formal dominance with strong linearity based on submission but not aggression. They indicate that the ethogram for dogs is best redefined by distinguishing body postures from behavioural activities. A good insight into dominance hierarchies and its indicators will be helpful in properly interpreting dog-dog relationships and diagnosing problem behaviour in dogs.

Protein-Protein, Genetic, and Chemical Interactions for Babu M (2014):Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.

See information included in the text of our manuscript being submitted to Plos Computational Biology.

There are no missing values.

EM Quantitation:

Raw data gathered from EM images taken to determine nanodisc population size distribution.

NNB TGN46 analysis:

Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.

Polymer conditions:

Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.

Protein groups polymer screen original file:

Original output of MaxQuant data processing of polymer screen data.

Organelle matching:

Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.

Polymer code:

Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.

Granulocyte-colony stimulating factor receptor (G-CSFR) controls myeloid progenitor proliferation and differentiation to neutrophils. Mutations in CSF3R (encoding G-CSFR) have been reported in patients with chronic neutrophilic leukemia (CNL) and acute myeloid leukemia (AML); however, despite years of research, the malignant downstream signaling of the mutated G-CSFRs is not well understood. Here, we utilized a quantitative phospho-tyrosine analysis to generate a comprehensive signaling map of G-CSF induced tyrosine phosphorylation in the normal versus mutated (proximal: T618I and truncated: Q741x) G-CSFRs. Unbiased clustering and kinase enrichment analysis identified rapid induction of phospho-proteins associated with endocytosis by the wild-type G-CSFR only; while G-CSFR mutants showed abnormal kinetics of canonical STAT3, STAT5 and MAPK phosphorylation, and aberrant activation of Bruton’s Tyrosine Kinase (Btk). Mutant-G-CSFR-expressing cells displayed enhanced sensitivity (5-fold lower IC50) for Ibrutinib-based chemical inhibition of Btk. Finally, primary murine progenitor cells from G-CSFR-d715x knock-in mice validate activation of Btk by the mutant receptor, and display enhanced sensitivity to Ibrutinib. Together, these data demonstrate the strength of unsupervised proteomics analyses in dissecting oncogenic pathways, and suggest repositioning Ibrutinib for therapy of myeloid leukemia bearing CSF3R mutations.

Start early and stay the course: Proactive management outperforms reactive actions for wildlife disease control :

To understand the impact of different management scenarios on host and pathogen persistence, we used a multistate dynamic occupancy model. We incorporated management actions (or lack of) via the estimated effects on parameters in the transition matrix. We considered four scenarios for the timing of management interventions on host and pathogen persistence: (i) no management scenario, (ii) proactive management scenario, (iii) reactive management scenario, and (iv) proactive + reactive management scenario. Expert elicitation was used to obtain estimates for the majority of the parameters given the limited empirical data available. The raw estimates and confidence level from the 4-point elicitation method are provided in the two data files provided here. Methodologies are further explained in the main text of the paper.

Description of the Data and file structure

...

Background Earlier methods for detecting major genes responsible for a quantitative trait rely critically upon a well-structured pedigree in which the segregation pattern of genes exactly follow Mendelian inheritance laws. However, for many outcrossing species, such pedigrees are not available and genes also display population properties. Results In this paper, a hierarchical statistical model is proposed to monitor the existence of a major gene based on its segregation and transmission across two successive generations. The model is implemented with an EM algorithm to provide maximum likelihood estimates for genetic parameters of the major locus. This new method is successfully applied to identify an additive gene having a large effect on stem height growth of aspen trees. The estimates of population genetic parameters for this major gene can be generalized to the original breeding population from which the parents were sampled. A simulation study is presented to evaluate finite sample properties of the model. Conclusions A hierarchical model was derived for detecting major genes affecting a quantitative trait based on progeny tests of outcrossing species. The new model takes into account the population genetic properties of genes and is expected to enhance the accuracy, precision and power of gene detection.