---
title: MultiplexCrisprDOE
---

<!-- this setup dependencies, but doesn't appear in the generated document -->
```julia; echo = false; results = "hidden"
using Pkg
"Plots" ∉ keys(Pkg.project().dependencies) && Pkg.add("Plots")
#"DSP" ∉ keys(Pkg.project().dependencies) && Pkg.add("DSP")
#"Images" ∉ keys(Pkg.project().dependencies) && Pkg.add("Images")
"DataStructures" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataStructures")
"PrettyTables" ∉ keys(Pkg.project().dependencies) && Pkg.add("PrettyTables")
"DataFrames" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataFrames")
"Latexify" ∉ keys(Pkg.project().dependencies) && Pkg.add("Latexify")
```
## Tool

* **Method:**     `j println(WEAVE_ARGS.tool_info["method"])`

* **Description:** `j println(WEAVE_ARGS.tool_info["description"])`

* **Mode:** `j println(WEAVE_ARGS.tool_info["mode"])`

* **Mode description:** `j println(WEAVE_ARGS.tool_info["mode_description"])`

## Variables

```julia; echo = false
using DataFrames
using PrettyTables
df = DataFrame("Argument" => collect(keys(WEAVE_ARGS.args_info)), "Value" => collect(values(WEAVE_ARGS.args_info)))
#pt = pretty_table(df, nosubheader=true; alignment=:l)
```

```julia; echo = false
    using Plots
    if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_reads")
        h1 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_reads"], label="", 
                        xlabel="Number of reads per gRNA", 
                        linecolor="white", 
                        normalize=:probability,
                        xtickfontsize=10,ytickfontsize=10,
                        color=:mediumturquoise, size=(600,350), bins = 25,
                        ylabel="Relative frequency", 
                        title="gRNA frequency distribution")
        display(h1)
    end
```

```julia; echo = false
    using Plots
    if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_edit")
        h2 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_edit"], 
                        normalize = :probability,
                        linecolor = "white",
                        label="", 
                        color=:turquoise4,
                        xtickfontsize=10,ytickfontsize=10, xlim = (0, 1),
                        xticks=(0:0.1:1),
                        bins = 150,
                        xlabel="gRNA editing efficiency", 
                        ylabel="Relative frequency", 
                        title="gRNA genome editing effiency distribution")
        display(h2)
    end
```

```julia; echo = false
    using Plots
    if haskey(WEAVE_ARGS.output,"output file")
        println("Output written to:")
        println(WEAVE_ARGS.output["output file"])
    elseif haskey(WEAVE_ARGS.output,"E_sim")
        E_sim = WEAVE_ARGS.output["E_sim"]
        sd_sim = WEAVE_ARGS.output["sd_sim"]
        k = WEAVE_ARGS.args_info["# of gRNAs / combi gRNA/Cas construct"]
        x = WEAVE_ARGS.args_info["# of target genes in the experiment"]
        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?**")
        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")
    elseif haskey(WEAVE_ARGS.output,"P_sim")
        p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["P_sim"], label="Pₓ₂", 
                title="Probability of full combinatorial coverage with respect to plant library size",
                xlabel="N", ylabel="Pₓₖ", 
                xticks = (0:500:50000, string.(0:500:50000)),
                size=(900,400), color=:turquoise4, linewidth=2)
                hline!([0.95], linestyle=:dash, color=:grey, label="Pₓₖ = 0.95", legend=:bottomright)
        display(p)
        println("At a given number of plants, what is the probability that all pairwise combinations of gene knockouts are observed?")
        println("N_95_P: ", WEAVE_ARGS.output["N_95_P"])
    elseif haskey(WEAVE_ARGS.output,"E_cov")
        p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["E_cov"],
                label="E[γₓ₂]", title="Expected combinatorial coverage w.r.t. plant library size",
                xlabel="N", ylabel="E[γₓₖ]", 
                xticks = (0:500:50000, string.(0:500:50000)),
                size=(800,400), color=:turquoise4, linewidth=2)
                hline!([0.95], linestyle=:dash, color=:grey, label="E[γₓₖ] = 0.95", legend=:bottomright)
        display(p)
        println("At a given number of plants, what is the expected coverage of pairwise gene knockout combinations?")
        println("N_95: ", WEAVE_ARGS.output["N_95"])
    end
```