Merge pull request #155 from LCSB-BioCore/develop

Regular merge of develop

Project.toml

@@ -1,6 +1,6 @@
 name = "GigaSOM"
 uuid = "a03a9c34-069e-5582-a11c-5c984cab887c"
-version = "0.6.0"
+version = "0.6.1"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

README.md

@@ -4,9 +4,9 @@
 
 GigaSOM is a Julia toolkit for clustering and visualisation of really large cytometry data. Most generally, it can load FCS files, perform transformation and cleaning operations in their contents, run FlowSOM-style clustering, and visualize and export the results. GigaSOM is distributed and parallel in nature, which makes processing huge datasets a breeze -- a hundred of millions of cells with a few dozen parameters can be clustered and visualized in a few minutes.
 
-| **Documentation** | **Test Coverage** | **[ARTENOLIS](http://opencobra.github.io/artenolis)** |
-|:-----------------:|:------------:|:--------------------------:|
-| [![doc](https://img.shields.io/badge/doc-GigaSOM-blue)](http://git.io/GigaSOM.jl) | [![coverage status](http://codecov.io/github/LCSB-BioCore/GigaSOM.jl/coverage.svg?branch=master)](http://codecov.io/github/LCSB-BioCore/GigaSOM.jl?branch=master) | [![linux](https://prince.lcsb.uni.lu/jenkins/job/GigaSOM.jl-branches-auto-linux/badge/icon)](https://prince.lcsb.uni.lu/jenkins/job/GigaSOM.jl-branches-auto-linux/) |
+| **Documentation** | **Test Coverage** | **[ARTENOLIS](http://opencobra.github.io/artenolis)** | **SciCrunch** |
+|:-----------------:|:-----------------:|:-----------------------------------------------------:|:--------:|
+| [![doc](https://img.shields.io/badge/doc-GigaSOM-blue)](http://git.io/GigaSOM.jl) | [![coverage status](http://codecov.io/github/LCSB-BioCore/GigaSOM.jl/coverage.svg?branch=master)](http://codecov.io/github/LCSB-BioCore/GigaSOM.jl?branch=master) | [![linux](https://prince.lcsb.uni.lu/jenkins/job/GigaSOM.jl-branches-auto-linux/badge/icon)](https://prince.lcsb.uni.lu/jenkins/job/GigaSOM.jl-branches-auto-linux/) | [![rrid](https://img.shields.io/badge/RRID-SCR__019020-72c02c)](https://scicrunch.org/resolver/RRID:SCR_019020) |
 
 # How to get started
 

docs/src/tutorials/processingFCSData.md

@@ -39,9 +39,12 @@ getMetaData(params)
 
 `data` is a matrix with cell expressions, one cell per row, one marker per
 column. If you want to run SOM analysis on it, you can cluster and visualize it
-just as in the previous tutorial:
+just as in the previous tutorial, with one exception- we start with cutting off
+the `label` column that contains `NaN` values:
 
 ```
+
+data = data[:,1:13]
 som = initGigaSOM(data, 16, 16)
 som = trainGigaSOM(som, data)
 clusters = mapToGigaSOM(som, data)
@@ -160,8 +163,9 @@ extract information about FCS data columns from there. First, we read the
 actual content using the XLSX package:
 
 ```julia
-md = GigaSOM.DataFrame(GigaSOM.XLSX.readtable("PBMC8_metadata.xlsx", "Sheet1", infer_eltypes=true)...)
-panel = GigaSOM.DataFrame(GigaSOM.XLSX.readtable("PBMC8_panel.xlsx", "Sheet1", infer_eltypes=true)...)
+using XLSX
+md = GigaSOM.DataFrame(readtable("PBMC8_metadata.xlsx", "Sheet1", infer_eltypes=true)...)
+panel = GigaSOM.DataFrame(readtable("PBMC8_panel.xlsx", "Sheet1", infer_eltypes=true)...)
 ```
 
 After that, we can get the parameter structure from the first FCS files: