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    "# Exercise: Quantum machine learning on lower-dimensional single-cell RNAseq data\n",
    "\n",
    "We will consider Breast Cancer multi-omics data in this exercise and use it to classify breast cancer subtypes Luminal A and Luminal B. \n",
    "\n",
    "We obtained 545 breast cancer samples from TCGA for which both RNAseq and Methylation450 data were available. The dataset consisted of 414 Luminal-A and 141 Luminal-B samples. We considered 28,495 genes and 363,791 methylation sites for a total of 392,286 features. We concatenated the RNAseq and Methylation450 data and projected them to a 10-dimensional space using PCA.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the experiments from the other notebook and use it to classify Luminal A vs. Luminal B and report the results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data can be found in the the `../data/BrCa` subdirectory. "
   ]
  },
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   "metadata": {},
   "outputs": [],
   "source": [
    "#your code here\n"
   ]
  }
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