Major overhaul of documentation towards version 0.6

README.md

@@ -23,6 +23,7 @@ This package is trying to be as user friendly as possible. One of the hopes is t
 * Patrick Roelli ([@Hoohm)](https://github.com/Hoohm))
 * Sebastian Mueller ([@seb-mueller)](https://github.com/seb-mueller))
 * Charles Girardot ([@cgirardot)](https://github.com/cgirardot))
+* Tom Kelly ([@TomKellyGenetics)](https://github.com/TomKellyGenetics))
 
 ## Usage
 
@@ -35,7 +36,7 @@ In any case, if you use this workflow in a paper, don't forget to give credits t
 
 ### Step 2: Configure workflow
 
-Configure the workflow according to your needs via editing the file `config.yaml` and the  `samples.tsv` following those [instructions](https://github.com/Hoohm/dropSeqPipe/wiki/Create-config-files)
+Configure the workflow according to your needs via editing the file `config.yaml` and the  `samples.csv` following those [instructions](https://hoohm.github.io/dropSeqPipe/)
 
 ### Step 3: Execute workflow
 
@@ -51,9 +52,9 @@ Execute the workflow locally via
 
 using `$N` cores on the `$WORKING_DIR`. Alternatively, it can be run in cluster or cloud environments (see [the docs](http://snakemake.readthedocs.io/en/stable/executable.html) for details).
 
-If you not only want to fix the software stack but also the underlying OS, use
+If you not only want to fix the software stack but also the underlying OS (using Singularity), use
 
-    snakemake --use-conda --use-singularity
+    snakemake --use-singularity
 
 in combination with any of the modes above.
 

docs/docs/Installation.md

@@ -1,7 +1,11 @@
-This pipeline is dependent on conda.
+#Installation
 
-### Step 1: Download and install miniconda3
-First you need to download and install miniconda3:
+DropSeqPipe is dependent upon the software management system `conda`.
+First step therefore is to install `miniconda3` a small version of `Anaconda` that includes conda, Python, the packages they depend on and a small number of other useful packages such as pip and zlib.
+
+
+## Step 1: Download and install miniconda3
+First download and install miniconda3 by executing the commands below on the command line.
 
 for linux
 ```
@@ -15,33 +19,53 @@ curl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o M
 bash Miniconda3-latest-MacOSX-x86_64.sh
 ```
 
+The command `conda` should now be available and can be tested by running `conda` on the command line.
+If the command doesn’t work, more information can be found [here](https://conda.io/projects/conda/en/latest/user-guide/install/index.html)
+
+## Step 2: Install snakemake
 
-### Step 2: Clone the workflow
+To run the pipeline [snakemake]( https://snakemake.readthedocs.io/en/stable/) is needed which will be installed using conda
 
-Clone the worflow
 ```
-git clone https://github.com/Hoohm/dropSeqPipe.git
+conda install -c bioconda -c conda-forge snakemake
 ```
+The command `snakemake` should now be available and can be tested by running `snakemake` on the command line. If the command doesn’t work, more detailed instruction can be found [here](https://snakemake.readthedocs.io/en/stable/)
 
-### Step 3: Install snakemake
+## Step 3: Obtain pipeline
+
+In order to download the actual pipeline either download it directly or use [git]( https://www.git-scm.com/) to clone the workflow.
+
+The pipeline can be directly downloaded [here]
+(https://github.com/Hoohm/dropSeqPipe/archive/master.zip)
+
+It is however recommended to download the pipeline by cloning using git rather than downloading the zip archive.
+If `git` hasn’t been installed yet `conda` can be used to do so.
 
 ```
-conda install -c bioconda -c conda-forge snakemake
+conda install -c bioconda -c conda-forge git
 ```
-
-Next step is config files completion
 
-[Complete the config.yaml](https://github.com/Hoohm/dropSeqPipe/wiki/Create-config-files) with the missing information
+Next the workflow can be obtained using the following command.
+
+```
+git clone https://github.com/Hoohm/dropSeqPipe.git
+```
+
+This should result in the set-up of a directory called `dropSeqPipe` which contains the pipeline.
 
-### UPDATES: How to update the pipeline
 
-Go to your experiment folder, then pull.
+
+## How to update the pipeline
+
+Go to the experiment folder, then pull.
+
 ```
-git pull https://github.com/Hoohm/dropSeqPipe.git
+git pull
 ```
 
-If you want to update files/plots based on the updates you can use this command:
+To update files/plots, use this command:
+
 ```
 snakemake -R `snakemake --list-codes-changes`
 ```
-This will update all the files that would be modified by the changes in the code (rules or script). Depending on how much and where the changes have been made, this might rerun the whole pipeline.
+This will update all the files that would be modified by the changes in the code (rules or script). Depending on how much and where the changes have been made, this might rerun the whole pipeline.

docs/docs/Output_misc.md

@@ -0,0 +1,29 @@
+# Other output
+
+Apart from plots, dSP also generates various other output, such as logs, tables, R objects, reports and intermediated files.
+
+## Tables
+Various summary statistics are also computed such as `barcode_stats_pre_filter.csv` and `barcode_stats_post_filter.csv`:
+
+###' barcode_stats_pre_filter.csv
+Various statistic (Total_raw_reads, Nr_barcodes_total	Nr_barcodes_more_than_1_reads	Nr_barcodes_more_than_10_reads	percentile99	percentile95	percentile50	Gini-index	Reads_assigned_to_expected_STAMPs) 
+
+### barcode_stats_post_filter.csv
+
+Statistics for barcodes that were taken forward as STAMPs as set as `expected_cells` in `config.yaml`, including:
+Total_nb_reads	Nb_STAMPS	Median_reads_per_STAMP	Mean_reads_per_STAMP	Total_nb_UMIs	Median_UMIs_per_STAMP	Mean_UMIs_per_STAMP	Mean_UMIs_per_Gene	Median_number_genes_per_STAMP	Mean_number_genes_per_STAMP	Mean_Ribo_pct	Mean_Mito_pct	Mean_Count_per_UMI	Read_length	Number_barcodes_used_for_debug	Pct_reads_after_filter_expected_cells	Pct_reads_after_filter_everything
+
+## R objects
+
+### Seurat 
+
+A Seurat object is generated automatically which can be used straight away using Seurat and can be found here:
+`summary/R_Seurat_objects.rdata`
+Note, dSP up to version 0.5 produces Seurat v2 object. Newer version (dSP 0.6+) generate Seurat v3 objects.
+
+As from dSP version 0.6 Seurat, you can import the object as follows:
+
+'''
+library(Seurat)
+dspData <- readRDS("path/to/working_dir/results/summary/R_Seurat_objects.rdata")
+'''