Differential Expression Analysis

Description

A personal project of exploring a differential analysis of gene expression workflow by following along a practical guide from a conference workshop. The dataset analyzed contains microarray data of genes and different experimental conditions in hTNFTg mouse model of inflammatory polyarthritis Karagianni et al. 2019.

Part of the project is an implementation of the same workflow using python libraries as an endeavor to potentially create an application that takes in any .tsv file uploaded by the user, runs the analysis automatically and displays the results in a Streamlit dashboard.

Main Repo Files

- DEG.qmd:

Quarto markdown file containing the differential expression analysis workflow in R script code blocks, as well as personal notes on the pipeline's theoretical background.

Contents include:

• Exploratory Data Analysis
• Data Distribution
• Dimensionality Reduction Algorithms (UMAP & PCA)
• Statistical Analysis (ANOVA & post-hoc tests)
• Volcano Plot
• Hierarchical Clustering
• Functional Enrichment Analysis

Libraries Used:

preprocessCore umap ggplot2 multcomp gplots factoextra cowplot RColorBrewer kableExtra randomForest dplyr plotly gprofiler2 caret

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- DEG.html:

Rendered DEG.qmd file in HTML format.

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- DEG_machine_learning.ipynb:

A Jupyter notebook following along the last part of the workshop, where an attempt was made to use machine learning algorithms for treatment prediction, based on gene expression data, for research purposes.

Libraries Used:

• pandas
• numpy
• collections.OrderedDict
• sklearn.model_selection.train_test_split
• sklearn.naive_bayes.GaussianNB
• sklearn.neighbors.KNeighborsClassifier
• sklearn.linear_model.LogisticRegression
• sklearn.linear_model.LinearRegression, Ridge, Lasso, RidgeCV, LassoCV
• sklearn.neural_network.MLPClassifier
• sklearn.svm.SVC
• sklearn.tree.DecisionTreeClassifier
• sklearn.ensemble.RandomForestClassifier
• sklearn.metrics.classification_report, f1_score, accuracy_score, confusion_matrix
• random.shuffle
• sklearn.preprocessing.StandardScaler
• sklearn.decomposition.PCA
• tqdm
• time
• hyperopt.fmin, tpe, hp
• tensorflow
• tensorflow.keras.models.Sequential
• tensorflow.keras.layers.Input, Dense
• tensorflow.keras.models.Model

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- DEG.py:

To render the file with Streamlit, run streamlit run DEG.py in the command line.

Pending python implementation of the differential expression analysis workflow [with a Streamlit application potential*] exploring the following python libraries: streamlit pandas seaborn numpy umap scipy statsmodels sklearn bokeh matplotlib sys

*Note: Needs adjustments to take in any .tsv file!