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Project description
# lizards-are-awesome
A Docker based workflow for performing a Plink/fastStructure analysis from on DArTseq SNP data, inferred from an Excel file.
## Overview
This software seeks to reduce the manual labour involved in preparing DArTseq SNP data in 1 row format for analysis with Plink and fastStructure. LAA is designed specifically for SNP data sets generated by DArTseq, in 1 row format. As such, input data will be the following metadata provided by DArTseq: “0” = Reference allele homozygote, “1”= SNP allele homozygote, “2”= heterozygote, and “-” = double null/ null allele homozygote (absence of fragment with SNP in genomic representation). LAA first converts these data into ped and map files for plink analysis.
Most of the work, besides the mentioned external packages, is done with a Python script. The primary operations performed by the script are:
Duplicating the input data.
Performing a substitution on certain characters in both sets of data, in order to create Plink compatible characters (i.e. “-” to “0”).
Independently indexing both sets of data.
Combining both sets of data.
Sorting on the combined index.
Transposing the combined data.
Outputting to Plink compatible ped and map formats.
Whereas before these steps would have been carred out manually using various software packages, they are now performed automatically.
In addition to the conversion operation, there are additional functions to perform analysis runs of Plink and fastStructre, passing the data files between the two programs automatically.
## Design Decisions
### Why Docker?
Plink is written for Linux based operating systems. As such on a Linux system all operations could be performed directly, without the need for any kind of virtualisation layer. But, in order to support researchers using Windows based operating systems the decision was made to leverage Docker virtualisation.
Docker provides a light-weight virtualisation layer enabling Linux software to run on Windows with (relative) ease. It also has the added benefit of providing a cloud based mechanism for disseminating software “images” to users. The advantage of Docker over other systems, like VirtualBox or VMWare, are:
cloud based distribution of prebuilt images,
future releases will allow native Docker containers, and
easy to replicate virtual image creation.
### Why Python?
Python is a powerful and expressive scripting language. It comes with many diverse packages, and has excellent support from developers (for example, fasStructure is written in Python).
## Dependencies
When installing on any platform there are number of requisite dependencies:
Python
Docker
If you happen to be installing on Windows, then there are a couple of extra requirements:
Visual Studio Python compiler
MsysGit
## Installation
Begin by installing all of the dependencies for your operating system as listed above.
Once complete, open a system terminal (please see the subsection on system terminals below, under usage).
From an open system terminal, install the LAA Python interface with:
`bash pip install laa `
Next, from a system terminal, download and prepare the laa docker image. This image contains plink, fastStructure, and the conversion scripts, all built into a light-weight Alpine linux image:
`bash laa init `
## Usage
### Terminals
Usage is currently done directly from your operating system terminal. In Linux like operating systems (including Mac OS X) use the system terminal emulator. In Windows operating systems use the Docker quick start terminal.
### Input Format
LAA accepts XLSX Excel formats and CSV. Unfortunately, XLSX is extremely slow to parse using opensource utilities. As such we recommend converting your Excel data to CSV before use with LAA (this is easy to do using Microsoft Office, or opensource spreadsheet tools, like Libre Office).
The data may be given to LAA in either a recombined or raw format. The raw format is more typical, as LAA handles recombination automatically, so we’ll only discuss the raw format.
The first two rows of the raw format should contain strings, defining the human readable names of the columns, and the short-hand names of the columns. All following rows contain the (??) data.
A short, completely fictitious, example:
<table class=”table table-bordered table-hover table-condensed”> <tbody><tr><td>AA</td> <td>AB</td> <td>AC</td> <td>AD</td> <td>AE</td> <td>AF</td> <td>AG</td> <td>AH</td> <td>AI</td> <td>AJ</td> </tr> <tr><td>aa</td> <td>ab</td> <td>ac</td> <td>ad</td> <td>ae</td> <td>af</td> <td>ag</td> <td>ah</td> <td>ai</td> <td>aj</td> </tr> <tr><td>0</td> <td>1</td> <td>2</td> <td>3</td> <td>4</td> <td>5</td> <td>6</td> <td>7</td> <td>8</td> <td>9</td> </tr> <tr><td>10</td> <td>11</td> <td>12</td> <td>13</td> <td>14</td> <td>15</td> <td>16</td> <td>17</td> <td>18</td> <td>19</td> </tr> <tr><td>20</td> <td>21</td> <td>22</td> <td>23</td> <td>24</td> <td>25</td> <td>26</td> <td>27</td> <td>28</td> <td>29</td> </tr> <tr><td>30</td> <td>31</td> <td>32</td> <td>33</td> <td>34</td> <td>35</td> <td>36</td> <td>37</td> <td>38</td> <td>39</td> </tr> <tr><td>40</td> <td>41</td> <td>42</td> <td>43</td> <td>44</td> <td>45</td> <td>46</td> <td>47</td> <td>48</td> <td>49</td> </tr> <tr><td>50</td> <td>51</td> <td>52</td> <td>53</td> <td>54</td> <td>55</td> <td>56</td> <td>57</td> <td>58</td> <td>59</td> </tr> <tr><td>60</td> <td>61</td> <td>62</td> <td>63</td> <td>64</td> <td>65</td> <td>66</td> <td>67</td> <td>68</td> <td>69</td> </tr> <tr><td>70</td> <td>71</td> <td>72</td> <td>73</td> <td>74</td> <td>75</td> <td>76</td> <td>77</td> <td>78</td> <td>79</td> </tr> <tr><td>80</td> <td>81</td> <td>82</td> <td>83</td> <td>84</td> <td>85</td> <td>86</td> <td>87</td> <td>88</td> <td>89</td> </tr> <tr><td>90</td> <td>91</td> <td>92</td> <td>93</td> <td>94</td> <td>95</td> <td>96</td> <td>97</td> <td>98</td> <td>99</td> </tr> </tbody></table>
And, in CSV format:
`csv AA,AB,AC,AD,AE,AF,AG,AH,AI,AJ aa,ab,ac,ad,ae,af,ag,ah,ai,aj 0,1,2,3,4,5,6,7,8,9 10,11,12,13,14,15,16,17,18,19 20,21,22,23,24,25,26,27,28,29 30,31,32,33,34,35,36,37,38,39 40,41,42,43,44,45,46,47,48,49 50,51,52,53,54,55,56,57,58,59 60,61,62,63,64,65,66,67,68,69 70,71,72,73,74,75,76,77,78,79 80,81,82,83,84,85,86,87,88,89 90,91,92,93,94,95,96,97,98,99 `
### Location
All LAA commands must be run from the same directory you have your CSV input file in. For the purpose of the examples, let’s say we have an input file, input.csv, located at /c/workspace/data:
`bash cd /c/workspace/data `
### Quick-run
To perform the complete process, including conversion, Plink, fastStructre and analysing for K values, you can just run:
`bash laa all input.csv --maxk=5 `
where –maxk=5 may be replaced with a suitable value for the maximum K value to use.
This will produce a range of files in the current working directory corresponding to the outputs of the conversion, Plink, and fastStructre.
### Conversion
Converting the input data will peform recombination, transposition, output to a PED file, and also generation of a suitable mapping file:
`bash laa convert input.csv output.ped `
This will generate two files: output.ped, and output.map. These files are suitable for use with Plink.
### Plink
To process the converted input files with Plink, run:
`bash laa plink output.ped `
### fastStructure
To process the Plink outputs with fastStructure, run:
`bash laa fast output `
### K Choice
To run fastStructure a number of times, and then choose an appropriate K value, run:
`bash laa choosek output --maxk=5 `
where –maxk=5 may be replaced with a suitable value for the maximum K value to use.
## Getting Help
Help is always available from the command-line. To get a printout of available commands, run:
`bash laa -h `
You may also get help for a specific command with something like:
`bash laa convert -h `
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