appia 7.0.0

Chromatography processing made easy

Appia - simple chromatography processing

Appia is a set of scripts to process and view chromatography data from AKTA, Shimadzu, and Waters systems. Chromatography data can then be viewed on the easy-to-use and intuitive web interface, built with plotly dash. Please check out the web demo hosted on heroku!

Additionally, automatic plots will be prepared for all data using ggplot in R. Options to copy a manual file for plot tweaking are available.

Installation

Server installation

1. Install docker
2. Copy docker-compose.yml wherever you want the database to save data
3. Set the $COUCHDB_USER and $COUCHDB_PASSWORD environment variables (in your environment!)
4. Run docker-compose up in the same directory as docker-compose.yml
5. Open the port from docker-compose.yml to allow other instruments to access the web ui
6. (Optional) Instead of opening the port, put Appia behind a reverse proxy server with authentication

Local/processing-only installation:

This process will install Python and all the packages/libraries you need. I highly recommend you use a virtual environment for the python packages. Conda is also fine, but I'm trying to keep this as simple as possible. If you want to use ggplot to make manually-tweaked plots, you should also follow the steps to install R and RStudio.

1. Clone this repo (or if you don't have git installed, just download it)
2. Install python3
   1. Run python -m virtualenv venv
   2. Run venv/Scripts/activate (Windows) or source venv/bin/activate (Mac/Linux)
   3. Run python -m pip install -r requirements.txt
3. (Optional) Install R for manual plotting
   1. In an R session (R.exe or R for Windows or Mac) run install.packages('tidyverse')
4. (Optional) Install R Studio for easier use of the manual plot scripts

GUI

Appia has a GUI built using Gooey which can be launched by running appia with no arguments. If you wish to use the command line interface instead include --ignore-gooey.

HPLC Processing

Appia reads .arw or .asc files (from Waters and Shimadzu HPLCs, respectively) for information about the sample and how it was run, then optionally collects all the indicated traces into an Experiment for upload to the visualization database.

Please note that our lab uses Waters instruments. Others are supported, but we will need more information from you for non-Waters bug reports and feature requests!

Waters Export Method

When exporting your data, please export the headers as two rows with multiple columns, rather than two columns with multiple rows.

The Waters script requires SampleName, Channel, Instrument Method Name and Sample Set Name. The order is not important, so long as the required headers are present in the .arw file. Other information can be there as well, it won't hurt anything. Flow rate information is pulled from processors/flow_rates.json. If your Instrument Method contains exactly one key from that JSON file, the flow rate is set accordingly. If the file does not exist, or if your Method matches more or fewer than one key, you will be asked to fill provide a flow rate. They can also be provided using the --hplc-flow-rate argument.

Shimadzu Data Export

If you are using an old Shimadzu instrument, your method will need the standard headers, including Sample ID, Total Data Points, and Sampling Rate. When you process, you will need to pass a set of arguments to tell Appia which channel corresponds to what, since Shimadzu instruments only output a letter. Additionally, you will be prompted for a flow rate (or you can provide one with --hplc-flow-rate).

Newer Shimadzu instruments output much more information about samples, which is great. Manual input of flow rate is still necessary, and if you have more than one sample with the same Sample Name and Sample ID being processed at the same time, they will conflict. This should not happen unless you're combining samples from different runs into a single processing event, which I consider a rare event. If this is essential for your workflow please submit an issue.

Agilent Data Export

Unfortunately, Agilent has rather limited support for data export. Versions of OpenLab prior to 2.4 lack the ability to export data in a format that Appia can read. However, OpenLab 2.4 introduced the ability to export data as a csv.

Following those instructions should yield a CSV with two unnamed columns, one representing retention time and the other signal. Given this lack of information, other data has to be provided by the user. If your file includes the pattern Channel<###> (where <###> is replaced by exactly three digits), Appia will set the channel for that file to the provided number. If your file includes the pattern Flow<##.##> (where <##.##> is replaced by any number of digits and a period followed by any number of digits, e.g., 1.25) Appia will set the flow rate for that file to that number, in mL/min. Otherwise, the user will be prompted for this information at the command line.

We do not have access to an Agilent instrument, and we welcome collaboration on this front!

AKTA FPLC Processing

The AKTA processing is straightforward. First, export your data from the AKTA in .csv format. You'll have to open the trace in Unicorn and use the export button there, just using "Export Data" saves a zipped binary which Appia can't read. Everything is handled automatically, but there are several arguments for producing and customizing automatic plots, if desired.

Web UI

When you process HPLC and/or FPLC data with Appia, you create an Experiment. These Experiments are then uploaded to a CouchDB server. The Appia web server pulls data from the CouchDB to display traces using plotly dash. This is the main power of Appia --- you can use your browser to quickly see your data, zoom in and out, select different traces, combine experiments to compare different runs, re-normalize the data, and share links with lab members.

Uploading an Experiment

To upload an experiment, when you process it include the -d flag. This will attempt to read the environment variables $COUCHDB_USER, $COUCHDB_PASSWORD, and $COUCHDB_HOST and use those to upload the Experiment to the correct database. You can also pass a JSON file to -d instead (but you should never save passwords in plaintext).

Viewing the experiment

Simply navigate to your server and view the trace page. The docker default is {myserver}:8080/traces. You can search experiments in the dropdown menu and concatenate HPLC results to compare across experiments. Clicking "Renormalize HPLC" will re-normalize the traces to set the maximum of the currently-viewed unnormalized region to 1, allowing you to compare specific peaks.

Batch scripts

From the command line, the best way to use Appia is to run appia.py. However, several batch scripts are included in this repo to give users who prefer not to use command line interfaces a set of commonly-used options. You could write equivalent shell scripts for Linux or Mac machines, but since most chromatography systems run on Windows I've included these for those machines.

process.bat

Read all files in the current directory and process all CSV, ASC, and ARW files into a new experiment which is uploaded to the database using environment variables

process-and-rename.bat

Same as above, but specify an Experiment ID yourself instead of reading one from the data.

Manual plot fine-tuning

For final publication plots, we recommend fine-tuning the appearance of the plot using ggplot2. To this end, we include some R scripts as suggested starting points for building publication plots. These manual plotting scripts can be copied into the processed data directory using the --copy-manual argument during processing. As you develop your own style, you can save your own templates (still named manual_plot_HPLC.R and manual_plot_FPLC.R) and pass the directory containing these templates to the --copy-manual argument.

Example Data

Examples of correctly-formatted Waters, Shimadzu, and AKTA files can be found in /test-files/. The directory /processed-tests/ is the result of the command:

python appia.py -v process test-files/*.arw .\test-files\2018_0821SEC_detergentENaC.csv -kpo processed-tests -m 5 20 -f 16 28 2

I included the -k parameter because I want to keep the raw files there, but if I had not, they'd be moved to their own respective directories in /processed-tests/. You'll see that in /processed-tests/ there are three files representing the compiled data, as well as auto-generated plots.

HPLC Data

For ease of use, HPLC data is stored in both a long and wide format.

Long format

mL is calculated from Time during processing. Sample and Channel are self-explanatory. Normalization tells if Value is the raw signal or a normalized Signal from 0 to 1, 0 being the minimum and 1 being the maximum over that sample/channel combination, unless a specific range over which to normalize was passed into Appia during processing.

mL	Sample	Channel	Time	Normalization	Value
0	05_25_BB	GFP	0	Signal	-1
0	05_25_BB	Trp	0	Signal	-35
0	05_25_D	GFP	0	Signal	3
0	05_25_D	Trp	0	Signal	0

Wide format

Wide format is the same data, but presented in a more traditional, "excel-style" format. Each column represents a trace, with a single column for Time to go along with it. You may note that the example wide table has a strange format, with many empty rows. This is because Shimadzu and Waters sample at different rates, meaning they do not have overlapping sampling points for the most part. Appia handles this, by using a single Time column and introducing empty rows in the Signal columns. Your plotting software should be able to deal with that, or you can just filter for non-empty rows.

Time	05_25_BB GFP	05_25_BB Trp	05_25_D GFP	05_25_D Trp
0	-1	-35	3	0
0.033333	-1	-20	0	-1

FPLC data

FPLC data is only stored in long format since, by and large, it is the same as what wide format would be. You just need to filter out channels you don't care about to reproduce what a wide-format table would be. Interestingly, AKTAs sample each channel at different rates, meaning that each channel has different x-axis values. This is all handled correctly by Appia, but that would introduce blank rows in the wide table, as with the HPLC example data. The fraction column indicates the vial into which that data point was dumped. This is used to fill fractions of interest, as seen in the example FPLC plot and the web interface.

mL	CV	Channel	Fraction	Sample	Normalization	Value
-0.00701	-0.00029	mAU	1	2018_0821SEC_detergentENaC	Signal	0.031309
-0.00618	-0.00026	mAU	1	2018_0821SEC_detergentENaC	Signal	0.022083
-0.00535	-0.00022	mAU	1	2018_0821SEC_detergentENaC	Signal	0.022115