remove old README

README.md

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 ![workflow](https://github.com/fraterenz/ecdna-evo/actions/workflows/test.yml/badge.svg)
 [![codecov](https://codecov.io/gh/fraterenz/ecdna-evo/branch/master/graph/badge.svg?token=0ZLN5UWXQQ)](https://codecov.io/gh/fraterenz/ecdna-evo)
 
-1. [Introduction](#introduction)
-2. [Code organization](#code-organization)
-3. [Installation](#installation): [Simulations](#simulations) and [Plots](#plots)
-4. [Usage](#usage)
-5. [Input and output](#input-and-output)
-
-## Introduction
-Extrachromosomal DNA (ecDNA) seems to play an important role in fostering
-cancer progression.
-We expand the mathematical and computational model developed in
-[Lange et al. 2022](https://www.nature.com/articles/s41588-022-01177-x)
-to study the ecDNA dynamics during tumour progression.
-
-To study the impact of the ecDNAs in tumour progression we use agent-based
-stochastic simulations. We use a Gillespie algorithm to simulate the
-proliferation and death of cancer cells at each time step proportional to
-pre-defined proliferation and death rates.
-
-The goal of this project is to two-fold:
-
-1. to study the evolution of ecDNA dynamics in cancer using a computational
-model based stochastic simulations
-2. to fit this model to patient's data by inferring the proliferative advantage
-(i.e. selection strength) present in tumours with ecDNAs.
-
-## Code organization
-We implemented a command line interface used to configure and run the simulations.
-
-## Installation
-The simulations can be ran in Linux, Windows and macOS.
-
-### Simulations
-Install the rust package either from the pre-built binaries or from source, see
-below.
-
-#### From binaries (recommended)
-Pre-built binaries for macOS, Windows and Linux can also be downloaded from the
-[GitHub releases page](https://github.com/fraterenz/ecdna-evo/releases).
-
-For older version of Linux or is something does not work, install
-[from source](#from-source).
-
-#### From source
-Need rust to be [installed](https://www.rust-lang.org/tools/install). Download
-the source code and compile with cargo with the **`--release` flag**;
-1. `git clone git@github.com:fraterenz/ecdna-evo.git`
-2. `cargo build --release -- --help`
-
-When building from source, the `/path/to/ecdna` (see [usage](#Usage)) will be
-`/path/to/ecdna-evo/target/release/ecdna`.
-
-## Usage
-There are two main usages: TODO.
-
-1. study the dynamics of ecDNA by simulating an exponentially growing tumor
-   population carrying ecDNA copies: `/path/to/ecdna simulate --help`, see also
-   [here](./dynamics.md).
-2. infer the proliferation advantage of cells with ecDNA copies ($\rho_1$) from
-   data using approximate Bayesian computation (ABC): `/path/to/ecdna abc --help`,
-   see also [here](./abc.md).
-
-#### Example
-When prebuilt binaries are used, replace in the example
-`./target/release/ecdna` by `/path/to/ecdna`, where `/path/to/ecdna` is the
-path to the ecdna binaries. TODO.
-
-1. Simulate 10 tumour growths (10000 cells each) with $\rho_1$ equals to 1
-   (neutral case) using the code compiled from source (see
-   `./target/release/ecdna simulate --help`):
-```shell
-# simulate tumour growth
-./target/release/ecdna simulate --cells 10000 --runs 10 --rho1 1 --dynamics nplus nminus --patient example
-```
-
-2. Prepare the data for the abc inference, add to the patient `example` one sample
-`sample1` defined by the ecdna distribution
-`results/example/10000samples10000cells/0/ecdna/0.json`, this sample having an
-estimated population of 10000 tumour cells (see
-`./target/release/preproces --help`):
-```shell
-./target/release/ecdna preprocess example sample1 10000 --distribution results/example/10000samples10000cells/0/ecdna/0.json
-```
-
-3. Now perform the bayesian inference.
-Performing the bayesian inference with more the runs will generate more
-accurate results, but will also take more time.
-Infer the proliferation advantage and the initial copy number using 1000 runs
-for the patient `example` (see `./target/release/ecdna abc --help`):
-```shell
-./target/release/ecdna abc --runs 1000 --rho1-range 1 3 --rho2-range 1 --delta1-range 0 --delta2-range 0 --copies-range 1 20 --patient results/preprocessed/example.json
- ```
-
-## Input and output
-See [here](./dynamics.md) for the first usage and [here](./abc.md) for the
-bayesian inference framework.
-
+TODO.