{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ['MKL_NUM_THREADS'] = '2'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pymc3 as pm\n", "import seaborn as sb\n", "import theano\n", "import theano.tensor as tt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "counts = pd.read_csv('pasilla_gene_counts.tsv', sep='\\t', index_col=0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | untreated1 | \n", "untreated2 | \n", "untreated3 | \n", "untreated4 | \n", "treated1 | \n", "treated2 | \n", "treated3 | \n", "
---|---|---|---|---|---|---|---|
gene_id | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
FBgn0000003 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "0 | \n", "1 | \n", "
FBgn0000008 | \n", "92 | \n", "161 | \n", "76 | \n", "70 | \n", "140 | \n", "88 | \n", "70 | \n", "
FBgn0000014 | \n", "5 | \n", "1 | \n", "0 | \n", "0 | \n", "4 | \n", "0 | \n", "0 | \n", "
FBgn0000015 | \n", "0 | \n", "2 | \n", "1 | \n", "2 | \n", "1 | \n", "0 | \n", "0 | \n", "
FBgn0000017 | \n", "4664 | \n", "8714 | \n", "3564 | \n", "3150 | \n", "6205 | \n", "3072 | \n", "3334 | \n", "