Use this Python program for basic OMICs data analysis using the pandas library:

```python

import pandas as pd

# Load OMICs data from a CSV file

data = pd.read_csv('omic_data.csv')

# Perform basic data exploration

print("Data shape:", data.shape)

print("Column names:", data.columns)

# Calculate summary statistics

summary_stats = data.describe()

print("Summary statistics:")

print(summary_stats)

# Perform data filtering

filtered_data = data[data['Gene Expression'] > 10]

print("Filtered data:")

print(filtered_data)

# Perform data aggregation

aggregated_data = data.groupby('Sample Type').mean()

print("Aggregated data:")

print(aggregated_data)

# Perform data visualization using matplotlib

import matplotlib.pyplot as plt

# Box plot of gene expression by sample type

data.boxplot(column='Gene Expression', by='Sample Type')

plt.title('Gene Expression by Sample Type')

plt.xlabel('Sample Type')

plt.ylabel('Gene Expression')

plt.show()

```

In this example, we assume that the OMICs data is stored in a CSV file named 'omic_data.csv', and the data has columns like 'Gene Expression' and 'Sample Type'. The program demonstrates basic data exploration, summary statistics calculation, data filtering, data aggregation, and data visualization using pandas and matplotlib libraries or power BI.

Hope it helps

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