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comparison 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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1 ---
2 title: MultiplexCrisprDOE
3 ---
4
5 <!-- this setup dependencies, but doesn't appear in the generated document -->
6 ```julia; echo = false; results = "hidden"
7 using Pkg
8 "Plots" ∉ keys(Pkg.project().dependencies) && Pkg.add("Plots")
9 #"DSP" ∉ keys(Pkg.project().dependencies) && Pkg.add("DSP")
10 #"Images" ∉ keys(Pkg.project().dependencies) && Pkg.add("Images")
11 "DataStructures" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataStructures")
12 "PrettyTables" ∉ keys(Pkg.project().dependencies) && Pkg.add("PrettyTables")
13 "DataFrames" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataFrames")
14 "Latexify" ∉ keys(Pkg.project().dependencies) && Pkg.add("Latexify")
15 ```
16 ## Tool
17
18 * **Method:** `j println(WEAVE_ARGS.tool_info["method"])`
19
20 * **Description:** `j println(WEAVE_ARGS.tool_info["description"])`
21
22 * **Mode:** `j println(WEAVE_ARGS.tool_info["mode"])`
23
24 * **Mode description:** `j println(WEAVE_ARGS.tool_info["mode_description"])`
25
26 ## Variables
27
28 ```julia; echo = false
29 using DataFrames
30 using PrettyTables
31 df = DataFrame("Argument" => collect(keys(WEAVE_ARGS.args_info)), "Value" => collect(values(WEAVE_ARGS.args_info)))
32 #pt = pretty_table(df, nosubheader=true; alignment=:l)
33 ```
34
35 ```julia; echo = false
36 using Plots
37 if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_reads")
38 h1 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_reads"], label="",
39 xlabel="Number of reads per gRNA",
40 linecolor="white",
41 normalize=:probability,
42 xtickfontsize=10,ytickfontsize=10,
43 color=:mediumturquoise, size=(600,350), bins = 25,
44 ylabel="Relative frequency",
45 title="gRNA frequency distribution")
46 display(h1)
47 end
48 ```
49
50 ```julia; echo = false
51 using Plots
52 if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_edit")
53 h2 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_edit"],
54 normalize = :probability,
55 linecolor = "white",
56 label="",
57 color=:turquoise4,
58 xtickfontsize=10,ytickfontsize=10, xlim = (0, 1),
59 xticks=(0:0.1:1),
60 bins = 150,
61 xlabel="gRNA editing efficiency",
62 ylabel="Relative frequency",
63 title="gRNA genome editing effiency distribution")
64 display(h2)
65 end
66 ```
67
68 ```julia; echo = false
69 using Plots
70 if haskey(WEAVE_ARGS.output,"output file")
71 println("Output written to:")
72 println(WEAVE_ARGS.output["output file"])
73 elseif haskey(WEAVE_ARGS.output,"E_sim")
74 E_sim = WEAVE_ARGS.output["E_sim"]
75 sd_sim = WEAVE_ARGS.output["sd_sim"]
76 k = WEAVE_ARGS.args_info["# of gRNAs / combi gRNA/Cas construct"]
77 x = WEAVE_ARGS.args_info["# of target genes in the experiment"]
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?**")
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")
80 elseif haskey(WEAVE_ARGS.output,"P_sim")
81 p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["P_sim"], label="Pₓ₂",
82 title="Probability of full combinatorial coverage with respect to plant library size",
83 xlabel="N", ylabel="Pₓₖ",
84 xticks = (0:500:50000, string.(0:500:50000)),
85 size=(900,400), color=:turquoise4, linewidth=2)
86 hline!([0.95], linestyle=:dash, color=:grey, label="Pₓₖ = 0.95", legend=:bottomright)
87 display(p)
88 println("At a given number of plants, what is the probability that all pairwise combinations of gene knockouts are observed?")
89 println("N_95_P: ", WEAVE_ARGS.output["N_95_P"])
90 elseif haskey(WEAVE_ARGS.output,"E_cov")
91 p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["E_cov"],
92 label="E[γₓ₂]", title="Expected combinatorial coverage w.r.t. plant library size",
93 xlabel="N", ylabel="E[γₓₖ]",
94 xticks = (0:500:50000, string.(0:500:50000)),
95 size=(800,400), color=:turquoise4, linewidth=2)
96 hline!([0.95], linestyle=:dash, color=:grey, label="E[γₓₖ] = 0.95", legend=:bottomright)
97 display(p)
98 println("At a given number of plants, what is the expected coverage of pairwise gene knockout combinations?")
99 println("N_95: ", WEAVE_ARGS.output["N_95"])
100 end
101 ```
102