Tomato Gene Expression Data by Kaggle | Healthcare

About this Dataset

Tomato Gene Expression Data

Non-Organic Imprints

About this dataset

This dataset contains gene expression data from tomato (Solanum lycopersicum) imprint non-organic samples, captured with SNP-based chip expression at incredibly high resolutions. Uncover powerful insight into molecular transcriptional dynamics in plants and explore the intrinsic relationships between genetic variation and transcriptional variation that have major implications for improving crop yields and quality. With this comprehensive dataset, gain greater knowledge of gene regulatory networks, explore potential expression patterns of genes of interest, and investigate plant protein-protein or biochemical interactions. Unlock the genetic mysteries of plants by accessing this unique source of valuable data – a great resource for researchers seeking to advance their understanding of gene transcriptional dynamics in the tomato plant!

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How to use the dataset

Assess the data: Before attempting to use the dataset, assess it thoroughly to ensure it is clean, has enough detail and resolution to help you reach your research or analysis goals, and includes enough samples for a meaningful study.
Conduct exploratory analysis: Use visualizations and summary statistics such as means, medians, standard deviations etc., to gain an understanding of the distributions of genes across different geographies or groups of genes within various clusters in the expression data. Doing this will help you gain additional insight into potential patterns within your dataset that can guide further analyses.
    	    	   	   	   	   	   	   5 validation steps: Validate every step along your workflow process with appropriate metrics including R2 scores (where appropriate), p-values, q-values etc ،To ensure quality control over results stemming from this dataset

Research Ideas

Discovering new regulatory networks in tomato plants by mapping gene expression patterns to SNPs.

Examining the correlation between genetic variations and transcriptional variations in order to improve crop yields and quality.

Studying protein-protein interactions and biochemical pathways in tomato plants,which could lead to insights on improving disease resistance or discovering novel metabolites with potential commercial value

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Cherrytomato Markers Matrix

@kaggle.thedevastator_tomato_gene_expression_data.cherrytomato_markers_matrix

35.76 MB
16228 rows
1601 columns


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