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annotate report.jmd @ 0:cc0957c46408 draft

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"planemo upload for repository https://github.com/kirstvh/MultiplexCrisprDOE commit b6c1b1860eee82b06ed4a592d1f9eee6886be318-dirty"
author padge
date Thu, 12 May 2022 17:39:18 +0000
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cc0957c46408 "planemo upload for repository https://github.com/kirstvh/MultiplexCrisprDOE commit b6c1b1860eee82b06ed4a592d1f9eee6886be318-dirty"
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1 ---
cc0957c46408 "planemo upload for repository https://github.com/kirstvh/MultiplexCrisprDOE commit b6c1b1860eee82b06ed4a592d1f9eee6886be318-dirty"
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2 title: MultiplexCrisprDOE
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3 ---
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5 <!-- this setup dependencies, but doesn't appear in the generated document -->
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6 ```julia; echo = false; results = "hidden"
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7 using Pkg
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8 "Plots" ∉ keys(Pkg.project().dependencies) && Pkg.add("Plots")
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9 #"DSP" ∉ keys(Pkg.project().dependencies) && Pkg.add("DSP")
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10 #"Images" ∉ keys(Pkg.project().dependencies) && Pkg.add("Images")
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11 "DataStructures" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataStructures")
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12 "PrettyTables" ∉ keys(Pkg.project().dependencies) && Pkg.add("PrettyTables")
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13 "DataFrames" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataFrames")
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14 "Latexify" ∉ keys(Pkg.project().dependencies) && Pkg.add("Latexify")
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15 ```
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16 ## Tool
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18 * **Method:** `j println(WEAVE_ARGS.tool_info["method"])`
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20 * **Description:** `j println(WEAVE_ARGS.tool_info["description"])`
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21
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22 * **Mode:** `j println(WEAVE_ARGS.tool_info["mode"])`
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24 * **Mode description:** `j println(WEAVE_ARGS.tool_info["mode_description"])`
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25
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26 ## Variables
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27
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28 ```julia; echo = false
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29 using DataFrames
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30 using PrettyTables
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31 df = DataFrame("Argument" => collect(keys(WEAVE_ARGS.args_info)), "Value" => collect(values(WEAVE_ARGS.args_info)))
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32 #pt = pretty_table(df, nosubheader=true; alignment=:l)
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33 ```
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34
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35 ```julia; echo = false
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36 using Plots
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37 if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_reads")
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38 h1 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_reads"], label="",
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39 xlabel="Number of reads per gRNA",
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40 linecolor="white",
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41 normalize=:probability,
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42 xtickfontsize=10,ytickfontsize=10,
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43 color=:mediumturquoise, size=(600,350), bins = 25,
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44 ylabel="Relative frequency",
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45 title="gRNA frequency distribution")
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46 display(h1)
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47 end
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48 ```
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49
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50 ```julia; echo = false
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51 using Plots
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52 if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_edit")
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53 h2 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_edit"],
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54 normalize = :probability,
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55 linecolor = "white",
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56 label="",
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57 color=:turquoise4,
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58 xtickfontsize=10,ytickfontsize=10, xlim = (0, 1),
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59 xticks=(0:0.1:1),
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60 bins = 150,
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61 xlabel="gRNA editing efficiency",
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62 ylabel="Relative frequency",
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63 title="gRNA genome editing effiency distribution")
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64 display(h2)
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65 end
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66 ```
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67
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68 ```julia; echo = false
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69 using Plots
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70 if haskey(WEAVE_ARGS.output,"output file")
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71 println("Output written to:")
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72 println(WEAVE_ARGS.output["output file"])
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73 elseif haskey(WEAVE_ARGS.output,"E_sim")
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74 E_sim = WEAVE_ARGS.output["E_sim"]
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75 sd_sim = WEAVE_ARGS.output["sd_sim"]
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76 k = WEAVE_ARGS.args_info["# of gRNAs / combi gRNA/Cas construct"]
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77 x = WEAVE_ARGS.args_info["# of target genes in the experiment"]
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78 println("**How many plants need to be included in the plant library (on average) to obtain full coverage of all k-combinations of gene knockouts?**")
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79 println("On average, ", Int(ceil(E_sim)), " plants need to be sampled at random to observe all ", k, "-combinations of ", x, " gene knockouts. Standard deviation = ", Int(ceil(sd_sim)), " plants")
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80 elseif haskey(WEAVE_ARGS.output,"P_sim")
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81 p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["P_sim"], label="Pₓ₂",
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82 title="Probability of full combinatorial coverage with respect to plant library size",
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83 xlabel="N", ylabel="Pₓₖ",
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84 xticks = (0:500:50000, string.(0:500:50000)),
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85 size=(900,400), color=:turquoise4, linewidth=2)
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86 hline!([0.95], linestyle=:dash, color=:grey, label="Pₓₖ = 0.95", legend=:bottomright)
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87 display(p)
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88 println("At a given number of plants, what is the probability that all pairwise combinations of gene knockouts are observed?")
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89 println("N_95_P: ", WEAVE_ARGS.output["N_95_P"])
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90 elseif haskey(WEAVE_ARGS.output,"E_cov")
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91 p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["E_cov"],
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92 label="E[γₓ₂]", title="Expected combinatorial coverage w.r.t. plant library size",
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93 xlabel="N", ylabel="E[γₓₖ]",
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94 xticks = (0:500:50000, string.(0:500:50000)),
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95 size=(800,400), color=:turquoise4, linewidth=2)
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96 hline!([0.95], linestyle=:dash, color=:grey, label="E[γₓₖ] = 0.95", legend=:bottomright)
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97 display(p)
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98 println("At a given number of plants, what is the expected coverage of pairwise gene knockout combinations?")
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99 println("N_95: ", WEAVE_ARGS.output["N_95"])
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100 end
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101 ```
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102