Heatmap of transcription levels of identified DEGs associated with NK cell cytotoxicity. Scatter plot displaying genes related to NK cell cytotoxicity. Scatter plot displaying genes related to signaling pathways.Heatmaps displaying FKPM values. Line graphs showing the transcriptional changes in genes related to activation, migration or tumor killing activity of NK cells.

Additional file 1. Example of chromoMap interactive plot constructed using various features of chromoMap including polyploidy (used as multi-track), feature-associated data visualization (scatter and bar plots), chromosome heatmaps, data filters (color-coded scatter and bars). Differential gene expression in a cohort of patients positive for COVID19 and healthy individuals (NCBI Gene Expression Omnibus id: GSE162835) [12]. Each set of five tracks labeled with the same chromosome ID (e.g. 1-22, X & Y) contains the following information: From top to bottom: (1) number of differentially expressed genes (DEGs) (FDR < 0.05) (bars over the chromosome depictions) per genomic window (green boxes within the chromosome). Windows containing ≥ 5 DEGs are shown in yellow. (2) DEGs (FDR < 0.05) between healthy individuals and patients positive for COVID19 visualized as a scatterplot above the chromosome depiction (genes with logFC ≥ 2 or logFC ≤ −2 are highlighted in orange). Dots above the grey dashed line represent upregulated genes in COVID19 positive patients. Heatmap within chromosome depictions indicates the average LogFC value per window. (3–4) Normalized expression of differentially expressed genes (scatterplot) and of each genomic window containing DEG (green scale heatmap) in (3) patients with severe/critical outcomes and (4) asymptomatic/mild outcome patients. (5) logFC of DEGs between healthy individuals and patients positive for COVID19 visualized as scatter plot color-coded based on the metabolic pathway each DEG belongs to.

SUMMARYThis script performs Gene Ontology (GO) enrichment analysis on a set of clock-related CpGs in Nasonia vitripennis, using a background of differentially methylated genes. It identifies over-represented GO terms, applies FDR correction, and visualizes significant terms using semantic similarity metrics.ORIGINAdapted from code by Alun Jones (see Bebane et al., 2019).KEY STEPS1. Load GO annotations for the background gene set (differentially methylated genes).2. Create GOFrame and GeneSetCollection objects compatible with GOstats.3. Load a user-defined list of clock genes.4. Filter gene list to those with GO annotations.5. Run a hypergeometric test for enrichment across BP, CC, and MF ontologies.6. Apply FDR correction (Benjamini-Hochberg).7. Visualize enriched Biological Process terms using: - Treemap - Scatter plot - Heatmap - Word cloudINPUT FILES- diff_backgroundGOannotations.csv (GO annotations for differentially methylated genes)- clock_genes.csv (list of clock-related CpGs)OUTPUT FILES- supplementary_tables_pnas.xlsx → Sheet: Table_S4_GOterms (FDR-filtered GO terms)- diff_erin_methylated_clock_genes_GO_treemap.png → Treemap of reduced GO termsSOFTWARE REQUIREMENTS- R packages: GOstats, GSEABase, treemap, readr, dplyr, rrvgo, openxlsx, org.Dm.eg.dbNOTES- Uses Drosophila GO database for semantic similarity.- Focuses on over-representation in the Biological Process ontology.CITATIONBebane, P. et al. (2019). "Neonics and bumblebees." [Insert DOI]CONTACTEamonn Mallonebm3@le.ac.uk

E-commerce Sales Prediction Dataset

This repository contains a comprehensive and clean dataset for predicting e-commerce sales, tailored for data scientists, machine learning enthusiasts, and researchers. The dataset is crafted to analyze sales trends, optimize pricing strategies, and develop predictive models for sales forecasting.

📂 Dataset Overview

The dataset includes 1,000 records across the following features:

📊 Data Summary

General Properties

Date: - Range: 01-01-2023 to 12-31-2023. - Contains 1,000 unique values without missing data.

Product_Category: - Categories: Electronics (21%), Sports (21%), Other (58%). - Most common category: Electronics (21%).

Price: - Range: From 244 to 999. - Mean: 505, Standard Deviation: 290. - Most common price range: 14.59 - 113.07.

Discount: - Range: From 0.01% to 49.92%. - Mean: 24.9%, Standard Deviation: 14.4%. - Most common discount range: 0.01 - 5.00%.

Customer_Segment: - Segments: Regular (35%), Occasional (34%), Other (31%). - Most common segment: Regular.

Marketing_Spend: - Range: From 2.41k to 10k. - Mean: 4.91k, Standard Deviation: 2.84k.

Units_Sold: - Range: From 5 to 57. - Mean: 29.6, Standard Deviation: 7.26. - Most common range: 24 - 34 units sold.

📈 Data Visualizations

The dataset is suitable for creating the following visualizations: - 1. Price Distribution: Histogram to show the spread of prices. - 2. Discount Distribution: Histogram to analyze promotional offers. - 3. Marketing Spend Distribution: Histogram to understand marketing investment patterns. - 4. Customer Segment Distribution: Bar plot of customer segments. - 5. Price vs Units Sold: Scatter plot to show pricing effects on sales. - 6. Discount vs Units Sold: Scatter plot to explore the impact of discounts. - 7. Marketing Spend vs Units Sold: Scatter plot for marketing effectiveness. - 8. Correlation Heatmap: Identify relationships between features. - 9. Pairplot: Visualize pairwise feature interactions.

💡 How the Data Was Created

The dataset is synthetically generated to mimic realistic e-commerce sales trends. Below are the steps taken for data generation:

1. Feature Engineering:

   • Identified key attributes such as product category, price, discount, and marketing spend, typically observed in e-commerce data.
   • Generated dependent features like units sold based on logical relationships.

2. Data Simulation:

   • Python Libraries: Used NumPy and Pandas to generate and distribute values.
   • Statistical Modeling: Ensured feature distributions aligned with real-world sales data patterns.

3. Validation:

   • Verified data consistency with no missing or invalid values.
   • Ensured logical correlations (e.g., higher discounts → increased units sold).

Note: The dataset is synthetic and not sourced from any real-world e-commerce platform.

🛠 Example Usage: Sales Prediction Model

Here’s an example of building a predictive model using Linear Regression:

Written in python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the dataset
df = pd.read_csv('ecommerce_sales.csv')

# Feature selection
X = df[['Price', 'Discount', 'Marketing_Spend']]
y = df['Units_Sold']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model training
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse:.2f}')
print(f'R-squared: {r2:.2f}')

Cultivated cottons are the most important economic crop, which produce natural fiber for the textile industry. In recent years, the genetic basis of several essential traits for cultivated cottons has been gradually elucidated by decoding their genomic variations. Although an abundance of resequencing data is available in public, there is still a lack of a comprehensive tool to exhibit the results of genomic variations and genome-wide association study (GWAS). To assist cotton researchers in utilizing these data efficiently and conveniently, we constructed the cotton genomic variation database (CottonGVD; http://120.78.174.209/ or http://db.cngb.org/cottonGVD). This database contains the published genomic information of three cultivated cotton species, the corresponding population variations (SNP and InDel markers), and the visualized results of GWAS for major traits. Various built-in genomic tools help users retrieve, browse, and query the variations conveniently. The database also provides interactive maps (e.g., Manhattan map, scatter plot, heatmap, and linkage disequilibrium block) to exhibit GWAS and expression GWAS results. Cotton researchers could easily focus on phenotype-associated loci visualization, and they are interested in and screen for candidate genes. Moreover, CottonGVD will continue to update by adding more data and functions.

This dataset provides results obtained by first-principles calculations on diatomic systems and isolated systems based on SCAN+rVV10. All diatomic systems containing atomic species from H (Z=1) to Ra (Z=88) are considered. Calculations not only for diatomic systems but also for isolated systems are uploaded for evaluating binding energy, .

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raw_vasp_output_files [zip files (diatomic_db_raw.zip, isolated_db_raw.zip)] These zip files contain raw output files (OUTCAR and vasprun.xml) of VASP calculations.

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parsed_dataset [Python pickle files (diatomic_df.pickle, isolated_df.pickle) and csv files (diatomic_df.csv, isolated_df.csv)] These files contain tables of typical physical values files obtained from the VASP calculations. The python pickle files requires python environment with pandas and pymatgen. Files "*_df.pickle" and "*_df_protocol3.pickle" contains the same data, but they were saved with python pickle protocol 5 and 3, respectively.

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codes [diatomic_parser.zip] Simple python scripts for parsing raw VASP output files and plotting heatmaps and a scatter plot.

Figure 3. Tumor type-specific presence of cell types. Heatmap of the proportions (%) of each cell type present in each sample. Scatter plot on the left of the heatmap indicates the median stemness level of each cell type. Horizontal tracking bars indicate the tumor type and grade of each sample. Vertical tracking bars indicate the major cell types of the nuclei. The cell types with greater than 5% are labeled within each cell. ATC: Astrocytoma; EMB: Embryonal tumors; EPN: Ependymoma; GBM: Glioblastoma; GNN: Glioneuronal/neuronal tumors; NT: Non-tumor; SCH: Schwannoma.