Commit 0de788d1 authored by Christian Arnold's avatar Christian Arnold

Added details for the example analysis

parent a3674516
......@@ -60,7 +60,7 @@ Principally, there are two ways of installing *diffTF* and the proper tools:
Otherwise, consult the internet on how to best install Git for your system.
3. **To run the example analysis for 50 TF, simply perform the following steps:**
3. **To run diffTF with an example ATAC-Seq / RNA-seq dataset for 50 TF, simply perform the following steps (see section** :ref:`exampleDataset` **for dataset details)**:
* Change into the ``example/input`` directory within the Git repository
......@@ -97,7 +97,7 @@ Principally, there are two ways of installing *diffTF* and the proper tools:
Read in section :ref:`docs-singularityNotes` about the ``--bind`` option and what ``/your/diffTF/path`` means here , it is actually very easy!
You can also run the example analysis with all TF instead of only 50. For this, simply modify the ``TF`` parameter and set it to the special word ``all`` that tells *diffTF* to use all recognized TFs instead of a speciifc list only (see section :ref:`parameter_TFs` for details).
You can also run the example analysis with all TF instead of only 50. For this, simply modify the ``TF`` parameter and set it to the special word ``all`` that tells *diffTF* to use all recognized TFs instead of a specific list only (see section :ref:`parameter_TFs` for details).
4. **To run your own analysis**, modify the files ``config.json`` and ``sampleData.tsv``. See the instructions in the section `Run your own analysis`_ for more details.
5. **If your analysis finished successfully**, take a look into the ``FINAL_OUTPUT`` folder within your specified output directory, which contains the summary tables and visualization of your analysis. If you received an error, take a look in Section :ref:`docs-errors` to troubleshoot.
......@@ -143,7 +143,7 @@ A working ``R`` installation is needed and a number of packages from either CRAN
Run your own analysis
============================================================
Running your own analysis is almost as easy as running the example analysis. Carefully read and follow the following steps and notes:
Running your own analysis is almost as easy as running the example analysis (see section :ref:`exampleDataset`). Carefully read and follow the following steps and notes:
1. Copy the files ``config.json`` and ``startAnalysis.sh`` to a directory of your choice.
2. Modify the file ``config.json`` accordingly. For example, we strongly recommend running the analysis for all TF instead of just 50 as for the example analysis. For this, simply change the parameter “TFs” to “all”. See Section :ref:`configurationFile` for details about the meaning of the parameters. Do not delete or rename any parameters or sections.
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......@@ -9,6 +9,27 @@ For a graphical summary of the idea, see the section :ref:`workflow`
We also put the paper on *bioRxiv*, please see the section :ref:`citation` for details.
.. _exampleDataset:
Example dataset
=================
We provide a the toy dataset that is included in the Git repository to test diffTF. It is a small ATAC-Seq/RNA-Seq dataset comparing two cell types along the hematopoietic differentiation trajectory in mouse (multipotent progenitors - MPP - versus granulocyte-macrophage progenitors - GMP) and comes from `Rasmussen et al. 2018 <https://www.biorxiv.org/content/early/2018/05/31/336008>`_. Generally, hematopoiesis is organized in a hierarchical manner, and the following Figure shows the hematopoietic hierarchy in more detail and also places GMP and MPP cells:
.. figure:: Figures/GMP_MPP.jpg
:scale: 40 %
:alt: The hematopoietic hierarchy
:align: center
U.Blank et. al., Blood 2015 (http://www.bloodjournal.org/content/bloodjournal/125/23/3542/F1.large.jpg)
The data consists of ATAC-Seq data of 4 replicates for each of the two cell types (4 GMP vs. 4 MPP), and is limited to chr1 only to reduce running times and complexity. RNA-seq data are also available, which allows using the TF classification within the diffTF framework. As mentioned in the paper, the small number of samples makes the correlation-based classification of the TFs into activators and repressors unreliable, but we nevertheless include it here for the small dataset to show how to principally enable our AR classification in diffTF.
In the example analysis, you can investigate the differential TF activity of a set of 50 (or even all of the 400+) TFs to identify the known drivers of the well-studied mouse hematopoietic differentiation system. Overall, we expect to see TFs that more specific for stem cells renewal being more active in the MPPs, while in GMPs, TFs responsible for the further myeloid cell differentiation (CEBP family, NFIL3) should be enriched.
Help, contribute and contact
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