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@@ -54,16 +54,16 @@ $\ell_1$-regression problem, both of which are defined in
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our manuscript.
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The `search` mode takes as input an $m \times n$ frequency matrix $F$ and outputs
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an $n$-clonal tree that best fits the frequency matrix. *Important note: the search
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command requires a root vertex specified with the `-f/--assigned-root` flag.*
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an $n$-clonal tree that best fits the frequency matrix. **Important note: the search
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command requires a root vertex specified with the `-f/--assigned-root` flag.**
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By default this root vertex is set to be $0$. When the root vertex is unknown,
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it suffices to append an extra column to the beginning of the frequency matrix
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and specify the root as $0$.
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The `regress` mode
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takes as input an $m \times n$ frequency matrix $F$ and an $n$-clonal
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tree $\mathcal{T}$ and outputs the minimum value of
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`\lVert F - UB_{\mathcal{T}} \rVert_1` over all usage matrices $U$.
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$\lVert F - UB_{\mathcal{T}} \rVert_1$ over all usage matrices $U$.
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### Input format
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