<jupyter_start><jupyter_text>Describe tissue types of input data**<NAME>, 2019**Load in previously identified tissue type counts and output a supplementary table.<jupyter_code>import os
import pandas as pd
# Load TCGA data
file = os.path.join('data', 'tcga_sample_counts.tsv')
tcga_count_df = pd.read_table(file, sep='\t').rename({'cancertype': 'tissue'}, axis='columns')
tcga_count_df = tcga_count_df.assign(dataset="TCGA")
tcga_count_df.head()
# Load GTEX data
file = os.path.join('data', 'gtex_sample_counts.tsv')
gtex_count_df = pd.read_table(file, sep='\t').rename({'tissuetype': 'tissue'}, axis='columns')
gtex_count_df = gtex_count_df.assign(dataset="GTEX")
gtex_count_df.head()
# Load TARGET data
file = os.path.join('data', 'target_sample_counts.tsv')
target_count_df = pd.read_table(file, sep='\t').rename({'cancertype': 'tissue'}, axis='columns')
target_count_df = target_count_df.assign(dataset="TARGET")
target_count_df.head()
# Combine all data to generate supplementary table
full_count_df = (
    pd.concat([tcga_count_df, gtex_count_df, target_count_df], axis='rows')
    .sort_values(by='tissue', ascending=True)
    .reset_index(drop=True)
)


file = os.path.join('results', 'full_sample_counts.tsv')
full_count_df.to_csv(file, sep='\t', index=False)

full_count_df<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15296803652968036, 'fraction_numerical': 0.0030441400304414, 'mean_word_length': 7.651315789473684, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '384438', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 495, 'n_tokens_neox': 483, 'n_words': 87, 'path': '0.expression-download/3.sample-type-counts.ipynb', 'repo_name': 'ctb/BioBombe', 'size': '1319'}