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update readmes

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zouter committed Sep 18, 2018
1 parent a3ffc37 commit 22da4129408e1c9c6fbcc099a1f59b71a80ad2a5
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# Derived data

Here, we place all the data which is generated by a script. This includes:
Here, we place all the data which is generated by a script. This
includes:

- The datasets
- The method singularity images
- The main benchmarking results
- The datasets
- The method singularity images
- The main benchmarking results
@@ -1,10 +1,14 @@

# Real datasets

The generation of the real datasets is divided in two parts. We first download all the (annotated) expression files from sites such as GEO. Next, we filter and normalise all datasets, and wrap them into the common trajectory format of [dynwrap](https://www.github.com/dynverse/dynwrap).
The generation of the real datasets is divided in two parts. We first
download all the (annotated) expression files from sites such as GEO.
Next, we filter and normalise all datasets, and wrap them into the
common trajectory format of
[dynwrap](https://www.github.com/dynverse/dynwrap).

| \# | script/folder | description |
|:----|:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | [πŸ“„`download_from_sources.R`](01-download_from_sources.R) | Downloading the real datasets from their sources (eg. GEO), and constructing the gold standard model, using the helpers in [helpers-download\_from\_sources](helpers-download_from_sources) |
| 2 | [πŸ“„`filter_and_normalise.R`](02-filter_and_normalise.R) | Filtering and normalising the real datasets using `dynbenchmark::process_raw_dataset` All datasets are then saved into the dynwrap format. |
| | [πŸ“`helpers-download_from_sources`](helpers-download_from_sources) | |
| \# | script/folder | description |
| :- | :---------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 1 | [πŸ“„`download_from_sources.R`](01-download_from_sources.R) | Downloading the real datasets from their sources (eg. GEO), and constructing the gold standard model, using the helpers in [helpers-download\_from\_sources](helpers-download_from_sources) |
| 2 | [πŸ“„`filter_and_normalise.R`](02-filter_and_normalise.R) | Filtering and normalising the real datasets using `dynbenchmark::process_raw_dataset` All datasets are then saved into the dynwrap format. |
| | [πŸ“`helpers-download_from_sources`](helpers-download_from_sources) | |
@@ -1,20 +1,31 @@

# Synthetic datasets

Each synthetic dataset is based on some characteristics of some real datasets. These characteristics include:
Each synthetic dataset is based on some characteristics of some real
datasets. These characteristics include:

- The number of cells and features
- The number of features which are differentially expressed in the trajectory
- Estimates of the distribution of the library sizes, average expression, dropout probabilities, ... estimated by [Splatter](https://github.com/Oshlack/splatter).
- The number of cells and features
- The number of features which are differentially expressed in the
trajectory
- Estimates of the distribution of the library sizes, average
expression, dropout probabilities, … estimated by
[Splatter](https://github.com/Oshlack/splatter).

Here we estimate the parameters of these "platforms" and use them to simulate datasets using different simulators. Each simulation script first creates a design dataframe, which links particular platforms, different topologies, seeds and other parameters specific for a simulator.
Here we estimate the parameters of these β€œplatforms” and use them to
simulate datasets using different simulators. Each simulation script
first creates a design dataframe, which links particular platforms,
different topologies, seeds and other parameters specific for a
simulator.

The data is then simulated using wrappers around the simulators (see [/package/R/simulators.R](/package/R/simulators.R)), so that they all return datasets in a format consistent with dynwrap.
The data is then simulated using wrappers around the simulators (see
[/package/R/simulators.R](/package/R/simulators.R)), so that they all
return datasets in a format consistent with
dynwrap.

| \# | script/folder | description |
|:----|:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
| 1 | [πŸ“„`estimate_platform.R`](01-estimate_platform.R) | Estimation of the platforms from real data done by `dynbenchmark::estimate_platform` |
| 2a | [πŸ“„`simulate_dyngen_datasets.R`](02a-simulate_dyngen_datasets.R) | [dyngen](https://github.com/dynverse/dyngen), simulations of regulatory networks which will produce a particular trajectory |
| 2b | [πŸ“„`simulate_prosstt_datasets.R`](02b-simulate_prosstt_datasets.R) | [PROSSTT](https://github.com/soedinglab/prosstt), simulations of tree topologies using random walks |
| 2c | [πŸ“„`simulate_splatter_datasets.R`](02c-simulate_splatter_datasets.R) | [Splatter](https://github.com/Oshlack/splatter), simulations of non-linear paths between different states |
| 2d | [πŸ“„`simulate_dyntoy_datasets.R`](02d-simulate_dyntoy_datasets.R) | [dyntoy](https://github.com/dynverse/dyntoy), simulations of toy data using random expression gradients in a reduced space |
| \# | script/folder | description |
| :- | :------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------- |
| 1 | [πŸ“„`estimate_platform.R`](01-estimate_platform.R) | Estimation of the platforms from real data done by `dynbenchmark::estimate_platform` |
| 2a | [πŸ“„`simulate_dyngen_datasets.R`](02a-simulate_dyngen_datasets.R) | [dyngen](https://github.com/dynverse/dyngen), simulations of regulatory networks which will produce a particular trajectory |
| 2b | [πŸ“„`simulate_prosstt_datasets.R`](02b-simulate_prosstt_datasets.R) | [PROSSTT](https://github.com/soedinglab/prosstt), expression is sampled from a linear model which depends on pseudotime |
| 2c | [πŸ“„`simulate_splatter_datasets.R`](02c-simulate_splatter_datasets.R) | [Splatter](https://github.com/Oshlack/splatter), simulations of non-linear paths between different states |
| 2d | [πŸ“„`simulate_dyntoy_datasets.R`](02d-simulate_dyntoy_datasets.R) | [dyntoy](https://github.com/dynverse/dyntoy), simulations of toy data using random expression gradients in a reduced space |
@@ -1,8 +1,9 @@

# Dataset characterisation

| \# | script/folder | description |
|:----|:----------------------------------------------------|:------------|
| 1 | [πŸ“„`synthetic.R`](1-synthetic.R) | |
| 2 | [πŸ“„`real.R`](2-real.R) | |
| 3 | [πŸ“„`trajectory_type_dag.R`](3-trajectory_type_dag.R) | |
| \# | script/folder | description |
| :- | :-------------------------------------------------- | :---------- |
| 1 | [πŸ“„`topology.R`](01-topology.R) | |
| 1 | [πŸ“„`synthetic.R`](1-synthetic.R) | |
| 2 | [πŸ“„`real.R`](2-real.R) | |
| 3 | [πŸ“„`trajectory_type_dag.R`](3-trajectory_type_dag.R) | |

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