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comparison main.jl @ 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
parents
children 4a5c94d1d8bb
comparison
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-1:000000000000 0:cc0957c46408
1 using Pkg
2 Pkg.add("Plots");
3 Pkg.add("Distributions");
4 Pkg.add("LinearAlgebra");
5 Pkg.add("Combinatorics");
6 Pkg.add("BioCCP");
7 Pkg.add("ArgParse");
8 Pkg.add("XLSX");
9 Pkg.add("DataFrames");
10 Pkg.add("Weave");
11 Pkg.add("DataStructures");
12 Pkg.add("PrettyTables");
13
14 using Random
15 using Plots
16 using Distributions
17 using LinearAlgebra
18 using Combinatorics
19 using BioCCP
20 using ArgParse
21 using XLSX
22 using DataFrames
23 using Weave
24 using DataStructures
25 using PrettyTables
26
27 global current_dir = pwd()
28 include("MultiplexCrisprDOE.jl");
29
30 function main(args)
31
32 aps = ArgParseSettings("MultiplexCrisprDOE")
33
34 @add_arg_table! aps begin
35 "gfd" #, "gRNA_freq_dist"
36 action = :command # adds a command which will be read from an argument
37 help = "gRNA/Cas9 frequencies"
38 "ged" #, "gRNA_edit_dist"
39 action = :command
40 help = "gRNA/Cas9 editing efficiencies"
41 "sim" # simulation
42 action = :command
43 help = "simulation-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage (and other relevant statistics)"
44 "ccp" # bioccp
45 action = :command
46 help = "BioCCP-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage (and other relevant statistics)"
47 end
48
49 @add_arg_table! aps["gfd"] begin # add command arg_table: same as usual, but invoked on s["grna"]
50 "m"
51 arg_type = Int
52 help = "plant library size"
53 "sd"
54 arg_type = Int
55 help = "the standard deviation on the gRNA abundances (in terms of absolute or relative frequency)"
56 "l"
57 arg_type = Int
58 help = "minimal gRNA abundance (in terms of absolute or relative frequency)"
59 "u"
60 arg_type = Int
61 help = "maximal gRNA abundance (in terms of absolute or relative frequency)"
62 "n" #, "--n_gRNA_total"
63 arg_type = Int
64 help = "the total number of gRNAs in the experiment"
65 "--normalize"
66 action = :store_true
67 # arg_type = Bool
68 # default = true
69 help = "if provided, the gRNA abundances (absolute frequencies) are converted into relative frequencies"
70 "--visualize"
71 action = :store_true
72 # arg_type = Bool
73 # default = false
74 help = "if provided, a histogram of all gRNA abundances is plotted"
75 "--out_file"
76 arg_type = String
77 default = "gRNA_reads"
78 help = "Output excel file prefix"
79 end
80
81 @add_arg_table! aps["ged"] begin # add command arg_table: same as usual, but invoked on s["grna"]
82 "f_act"
83 arg_type = Float16
84 help = "fraction of all gRNAs that is active"
85 "eps_edit_act"
86 arg_type = Float16
87 help = "Average genome editing efficiency for active gRNAs - mean of the genome editing efficiency distribution for active gRNAs"
88 "eps_edit_inact"
89 arg_type = Float16
90 help = "Average genome editing efficiency for inactive gRNAs - mean of the genome editing efficiency distribution for inactive gRNAs"
91 "sd_act"
92 arg_type = Float16
93 help = "standard deviation of the genome editing efficiency distributions for active and inactive gRNAs"
94 "n_gRNA_total"
95 arg_type = Int
96 help = "the total number of gRNAs in the experiment"
97 "--visualize"
98 action = :store_true
99 # arg_type = Bool
100 # default = false
101 help = "if provided a histogram of all genome editing efficiency is plotted"
102 "--out_file"
103 arg_type = String
104 default = "gRNA_edit"
105 help = "Output excel file prefix"
106 end
107
108 @add_arg_table! aps["sim"] begin
109 "M" #, "--mode"
110 # action = :command
111 # dest_name = "M"
112 arg_type = Int
113 range_tester = x -> 1 <= x <= 4
114 help = """Select simulation mode (1: simulate_Nₓ₁; 2: simulate_Nₓ₂; 3: simulate_Nₓ₃; 4: simulate_Nₓ₂_countKOs)"""
115 "x"
116 arg_type = Int
117 help = "number of target genes in the experiment"
118 "g"
119 arg_type = Int
120 help = "number of gRNAs designed per target gene"
121 "r"
122 arg_type = Int
123 help = "number of gRNA sequences per combinatorial gRNA/Cas construct"
124 "t"#, "--n_gRNA_total"
125 arg_type = Int
126 help = "total number of gRNAs in the experiment"
127 "f"#, "--p_gRNA_freq"
128 arg_type = String #Vector{Float64}
129 help = "vector with relative frequencies for all gRNAs in the construct library (normalized!)"
130 "e"#, "--p_gRNA_edit"
131 arg_type = String #Vector{Float64}
132 help = "vector with genome editing efficiencies for all gRNAs"
133 "E"#, "--ϵ_KO"
134 arg_type=Float16
135 help = "global knockout efficiency; fraction of mutations leading to effective gene knockout"
136 "--i", "--iter"
137 arg_type = Int
138 default = 500
139 help = "number of CRISPR/Cas experiments that are simulated"
140 end
141
142 @add_arg_table! aps["ccp"] begin
143 "M"#, "--mode"
144 arg_type = Int
145 range_tester = x -> 1 <= x <= 9
146 help = """Select BioCCP mode (1: BioCCP_Nₓ₁; 2: BioCCP_Nₓ₂; 3: BioCCP_Nₓ₃; 4: BioCCP_Pₓ₁; 5: BioCCP_Pₓ₂ ;
147 6: BioCCP_Pₓ₃; 7: BioCCP_γₓ₁; 8: BioCCP_γₓ₂; 9: BioCCP_γₓ₃)"""
148 "x"
149 arg_type = Int
150 help = "number of target genes in the experiment"
151 "N"
152 arg_type = Int
153 help = "(Minimum) plant library size"
154 "--s", "--step"
155 arg_type = Int
156 default = 5
157 range_tester = x -> 1 <= x <= 10
158 help = "Step size for plant library size (optional for calculating expected combinatorial coverage / plant library size)"
159 "--MN", "--max_pl_size"
160 arg_type = Int
161 default = 4000
162 help = "Maximum plant library size (optional for calculating expected combinatorial coverage / plant library size)"
163 "g"
164 arg_type = Int
165 help = "number of gRNAs designed per target gene"
166 "r"
167 arg_type = Int
168 help = "number of gRNA sequences per combinatorial gRNA/Cas construct"
169 "t"#, "--n_gRNA_total"
170 arg_type = Int
171 help = "total number of gRNAs in the experiment"
172 "f"#, "--p_gRNA_freq"
173 arg_type = String #Vector{Float64}
174 help = "File containing vector with relative frequencies for all gRNAs in the construct library (normalized!)"
175 "e"#, "--p_gRNA_edit"
176 arg_type = String #Vector{Float64}
177 help = "File containing vector with genome editing efficiencies for all gRNAs"
178 "E"#, "--ϵ_KO"
179 arg_type=Float16
180 help = "global knockout efficiency; fraction of mutations leading to effective gene knockout"
181 end
182
183 parsed_args = parse_args(args, aps)
184 command_args = parsed_args[parsed_args["%COMMAND%"]]
185 println(command_args)
186
187 tool_info = OrderedDict()
188 args_info = OrderedDict()
189 grna_dict = Dict()
190 out_dict = Dict()
191 if parsed_args["%COMMAND%"] == "gfd"
192 tool_info["method"] = "gRNA_ frequency _distribution"
193 tool_info["description"] = "Generates vector with frequencies in the combinatorial "*
194 "gRNA/Cas9 construct library for all gRNAs"
195 tool_info["mode"] = ""
196 tool_info["mode_description"] = ""
197 args_info["Plant library size"] = command_args["m"]
198 args_info["SD on the gRNA abundances"] = command_args["sd"]
199 args_info["Minimal gRNA abundance"] = command_args["l"]
200 args_info["Maximal gRNA abundance"] = command_args["u"]
201 args_info["Total number of gRNAs"] = command_args["n"]
202 args_info["Convert gRNA abundances to relative frequencies"] = string(command_args["normalize"])
203 args_info["Plot gRNA abundances"] = string(command_args["visualize"])
204
205 m = command_args["m"]
206 sd = command_args["sd"]
207 l = command_args["l"]
208 u = command_args["u"]
209 n_gRNA_total = command_args["n"]
210 norm = command_args["normalize"]
211 viz = command_args["visualize"]
212
213 println(string(norm))
214 println(string(viz))
215
216 p_gRNA_reads = gRNA_frequency_distribution(m, sd, l, u, n_gRNA_total; normalize = norm, visualize = false)
217 grna_dict["p_gRNA_reads"] = p_gRNA_reads
218
219 # println(p_gRNA_reads)
220 # write to excel file
221 fn = command_args["out_file"] * ".xlsx"
222 labels = ["gRNA_read"]
223 columns = Vector()
224 push!(columns, p_gRNA_reads)
225 XLSX.openxlsx(fn, mode="w") do xf
226 sheet = xf[1]
227 XLSX.writetable!(sheet, columns, labels)
228 end
229
230 out_dict["output file"] = fn
231
232 elseif parsed_args["%COMMAND%"] == "ged"
233 tool_info["method"] = "gRNA_ edit _distribution"
234 tool_info["description"] = "Generates vector with genome editing efficiencies "*
235 "for all the gRNAs in the experiment"
236 tool_info["mode"] = ""
237 tool_info["mode_description"] = ""
238 args_info["Fraction of active gRNAs"] = command_args["f_act"]
239 args_info["Average genome editing efficiency of active gRNAs"] = command_args["eps_edit_act"]
240 args_info["Average genome editing efficiency of inactive gRNAs"] = command_args["eps_edit_inact"]
241 args_info["Standard deviation"] = command_args["sd_act"]
242 args_info["Total number of gRNAs"] = command_args["n_gRNA_total"]
243 args_info["Plot genome editing efficiency"] = string(command_args["visualize"])
244
245 f_act = command_args["f_act"]
246 eps_edit_act = command_args["eps_edit_act"]
247 eps_edit_inact = command_args["eps_edit_inact"]
248 sd_act = command_args["sd_act"]
249 n_gRNA_total = command_args["n_gRNA_total"]
250 viz = ["visualize"]
251
252 p_gRNA_edit = gRNA_edit_distribution(f_act, eps_edit_act, eps_edit_inact, sd_act, n_gRNA_total; visualize=false)
253 grna_dict["p_gRNA_edit"] = p_gRNA_edit
254 # write to excel file
255 fn = command_args["out_file"] * ".xlsx"
256 labels = ["gRNA_edit_efficiency"]
257 columns = Vector()
258 push!(columns, p_gRNA_edit)
259 XLSX.openxlsx(fn, mode="w") do xf
260 sheet = xf[1]
261 XLSX.writetable!(sheet, columns, labels)
262 end
263
264 out_dict["output file"] = fn
265
266 elseif parsed_args["%COMMAND%"] == "sim" || parsed_args["%COMMAND%"] == "ccp"
267
268 filename = command_args["f"]
269 sheet = 1
270 data = DataFrame(XLSX.readtable(filename, sheet)...)
271 p_gRNA_reads = data[!,"gRNA_read"]
272 p_gRNA_reads_normalized = p_gRNA_reads/sum(p_gRNA_reads) # normalize
273 f = p_gRNA_reads_normalized
274 grna_dict["p_gRNA_reads"] = f
275
276 filename = command_args["e"]
277 sheet = 1
278 data = DataFrame(XLSX.readtable(filename, sheet)...)
279 p_gRNA_edit = data[!,"gRNA_edit_efficiency"]
280 e = p_gRNA_edit
281 grna_dict["p_gRNA_edit"] = e
282
283 x = command_args["x"]
284 g = command_args["g"]
285 r = command_args["r"]
286 t = command_args["t"] # n_gRNA_total
287 E = command_args["E"] # ϵ_KO # iter = 500
288
289 args_info["# of target genes in the experiment"] = command_args["x"]
290 args_info["# of gRNAs designed per target gene"] = command_args["g"]
291 args_info["# of gRNAs / combi gRNA/Cas construct"] = command_args["r"]
292 args_info["Total number of gRNAs"] = command_args["t"]
293 args_info["Relative frequencies for all gRNAs"] = command_args["f"]
294 args_info["Genome editing efficiencies for all gRNAs"] = command_args["e"]
295 args_info["Global knockout efficiency"] = command_args["E"]
296
297 if parsed_args["%COMMAND%"] == "sim"
298 tool_info["method"] = "simulation"
299 tool_info["description"] = "simulation-based approaches for computing the minimal "*
300 "plant library size that guarantees full combinatorial "*
301 "coverage (and other relevant statistics)"
302 i = command_args["i"] # iter = 500
303 args_info["# of simulated experiments"] = command_args["i"]
304
305 if command_args["M"] == 1
306 tool_info["mode"] = "simulate_Nx1"
307 tool_info["mode_description"] = "Computes the expected value and the standard deviation "*
308 "of the minimal plant library size for full coverage of "*
309 "all single gene knockouts (E[Nx,1] and σ[Nx,1]) using simulation"
310 E_sim, sd_sim = simulate_Nₓ₁(x, g, r, t, f, e, E; iter=i)
311 out_dict["E_sim"] = E_sim
312 out_dict["sd_sim"] = sd_sim
313
314 elseif command_args["M"] == 2
315 tool_info["mode"] = "simulate_Nx2"
316 tool_info["mode_description"] = "Computes the expected value and the standard deviation of "*
317 "the minimal plant library size for full coverage of "*
318 "all pairwise combinations of gene knockouts in a "*
319 "multiplex CRISPR/Cas experiment (E[Nx,2] and σ[Nx,2]) using simulation"
320
321 E_sim, sd_sim = simulate_Nₓ₂(x, g, r, t, f, e, E; iter=i)
322 out_dict["E_sim"] = E_sim
323 out_dict["sd_sim"] = sd_sim
324
325 elseif command_args["M"] == 3
326 tool_info["mode"] = "simulate_Nx3"
327 tool_info["mode_description"] = "Computes the expected value and the standard deviation of "*
328 "the minimal plant library size for full coverage of "*
329 "all triple combinations of gene knockouts in a "*
330 "multiplex CRISPR/Cas experiment (E[Nx,3] and σ[Nx,3]) using simulation"
331
332 E_sim, sd_sim = simulate_Nₓ₃(x, g, r, t, f, e, E; iter=i)
333 out_dict["E_sim"] = E_sim
334 out_dict["sd_sim"] = sd_sim
335
336 elseif command_args["M"] == 4
337 tool_info["mode"] = "simulate_Nx2_countKOs"
338 tool_info["mode_description"] = "Counts of the number of knockouts per plant in the experiment"
339
340 n_KOs_vec = simulate_Nₓ₂_countKOs(x, g, r, t, f, e, E; iter=i)
341 out_dict["n_KOs_vec"] = n_KOs_vec
342
343 # write to excel file
344 fn = "countKOs.xlsx"
345 labels = ["countKOs"]
346 columns = Vector()
347 push!(columns, n_KOs_vec)
348 XLSX.openxlsx(fn, mode="w") do xf
349 sheet = xf[1]
350 XLSX.writetable!(sheet, columns, labels)
351 end
352
353 out_dict["output file"] = fn
354
355 end
356
357 elseif parsed_args["%COMMAND%"] == "ccp"
358 tool_info["method"] = "BioCCP"
359 tool_info["description"] = "BioCCP-based approaches for computing the minimal "*
360 "plant library size that guarantees full combinatorial "*
361 "coverage (and other relevant statistics)"
362
363 N = command_args["N"]
364 if haskey(command_args,"s") && haskey(command_args,"MN")
365 s = command_args["s"]
366 MN = command_args["MN"]
367 args_info["Step size"] = command_args["s"]
368 args_info["Maximum Plant library size"] = command_args["MN"]
369 end
370 args_info["Plant library size"] = command_args["N"]
371
372
373 if command_args["M"] == 1
374 tool_info["mode"] = "BioCCP_Nx1"
375 tool_info["mode_description"] = "Computes the expected value and the standard deviation of "*
376 "the minimal plant library size for full coverage of all "*
377 "single gene knockouts (E[Nx,1] and σ[Nx,1]) using BioCCP"
378
379 E_sim, sd_sim = BioCCP_Nₓ₁(x, g, r, t, f, e, E)
380 out_dict["E_sim"] = E_sim
381 out_dict["sd_sim"] = sd_sim
382
383 elseif command_args["M"] == 2
384 tool_info["mode"] = "BioCCP_Nx2"
385 tool_info["mode_description"] = "Computes the expected value and the standard deviation of "*
386 "the minimal plant library size for full coverage of all "*
387 "pairwise combinations of gene knockouts in a multiplex "*
388 "CRISPR/Cas experiment (E[Nx,2] and σ[Nx,2]) using BioCCP"
389
390 E_sim, sd_sim = BioCCP_Nₓ₂(x, g, r, t, f, e, E)
391 out_dict["E_sim"] = E_sim
392 out_dict["sd_sim"] = sd_sim
393
394 elseif command_args["M"] == 3
395 tool_info["mode"] = "BioCCP_Nx3"
396 tool_info["mode_description"] = "Computes the expected value and the standard deviation of "*
397 "the minimal plant library size for full coverage of all triple combinations of "*
398 "gene knockouts in a multiplex CRISPR/Cas experiment (E[Nx,3] and σ[Nx,3]) using BioCCP"
399
400 E_sim, sd_sim = BioCCP_Nₓ₃(x, g, r, t, f, e, E)
401 out_dict["E_sim"] = E_sim
402 out_dict["sd_sim"] = sd_sim
403
404 elseif command_args["M"] == 4
405 tool_info["mode"] = "BioCCP_Px1"
406 tool_info["mode_description"] = "Computes the probability of full coverage of "*
407 "all single gene knockouts (Px,1) for an experiment with given "*
408 "plant library size using BioCCP"
409
410 if s != nothing && MN != nothing
411 plant_library_sizes = N:s:MN
412 else
413 plant_library_sizes = N
414 end
415 PT = []
416 global N_95_P = nothing
417 for N in plant_library_sizes
418 Pr = BioCCP_Pₓ₁(x, N, g, r, t, f, e, E)
419 push!(PT, Pr)
420 if Pr < 0.95
421 N_95_P = N
422 end
423 end
424 # P = BioCCP_Pₓ₁(x, N, g, r, t, f, e, E)
425 out_dict["P_sim"] = PT
426 out_dict["N_95_P"] = N_95_P
427 out_dict["pls"] = plant_library_sizes
428
429 elseif command_args["M"] == 5
430 tool_info["mode"] = "BioCCP_Px2"
431 tool_info["mode_description"] = "Computes the probability of full coverage of "*
432 "all pairwise combinations of gene knockouts (Px,2) "*
433 "for an experiment with given plant library size using BioCCP"
434
435 if s != nothing && MN != nothing
436 plant_library_sizes = N:s:MN
437 else
438 plant_library_sizes = N
439 end
440 PT = []
441 global N_95_P = nothing
442 for N in plant_library_sizes
443 Pr = BioCCP_Pₓ₂(x, N, g, r, t, f, e, E)
444 push!(PT, Pr)
445 if Pr < 0.95
446 N_95_P = N
447 end
448 end
449 # P = BioCCP_Pₓ₂(x, N, g, r, t, f, e, E)
450 out_dict["P_sim"] = PT
451 out_dict["N_95_P"] = N_95_P
452 out_dict["pls"] = plant_library_sizes
453
454 elseif command_args["M"] == 6
455 tool_info["mode"] = "BioCCP_Px3"
456 tool_info["mode_description"] = "Computes the probability of full coverage of all "*
457 "triple combinations of gene knockouts (Px,3) for an experiment "*
458 "with given plant library size using BioCCP"
459
460 if s != nothing && MN != nothing
461 plant_library_sizes = N:s:MN
462 else
463 plant_library_sizes = N
464 end
465 PT = []
466 global N_95_P = nothing
467 for N in plant_library_sizes
468 Pr = BioCCP_Pₓ₃(x, N, g, r, t, f, e, E)
469 push!(PT, Pr)
470 if Pr < 0.95
471 N_95_P = N
472 end
473 end
474 # P = BioCCP_Pₓ₃(x, N, g, r, t, f, e, E)
475 out_dict["P_sim"] = PT
476 out_dict["N_95_P"] = N_95_P
477 out_dict["pls"] = plant_library_sizes
478
479 elseif command_args["M"] == 7
480 tool_info["mode"] = "BioCCP_γx1"
481 tool_info["mode_description"] = "Computes the expected coverage of all "*
482 "single gene knockouts (E[γx,1]) for an experiment "*
483 "with given plant library size using BioCCP"
484 if s != nothing && MN != nothing
485 plant_library_sizes = N:s:MN
486 else
487 plant_library_sizes = N
488 end
489 exp_cov = []
490 global N_95 = nothing
491 for N in plant_library_sizes
492 E_cov = BioCCP_γₓ₁(x, N, g, r, t, f, e, E)
493 push!(exp_cov, E_cov)
494 if E_cov < 0.95
495 N_95 = N
496 end
497 end
498 # E_sim, sd_sim = BioCCP_γₓ₁(x, N, g, r, t, f, e, E)
499 out_dict["E_cov"] = exp_cov
500 out_dict["N_95"] = N_95
501 out_dict["pls"] = plant_library_sizes
502
503 elseif command_args["M"] == 8
504 tool_info["mode"] = "BioCCP_γx2"
505 tool_info["mode_description"] = "Computes the expected coverage of all "*
506 "pairwise combinations of gene knockouts (E[γx,2]) for an experiment with "*
507 "given plant library size using BioCCP"
508
509 if s != nothing && MN != nothing
510 plant_library_sizes = N:s:MN
511 else
512 plant_library_sizes = N
513 end
514 exp_cov = []
515 global N_95 = nothing
516 for N in plant_library_sizes
517 E_cov = BioCCP_γₓ₂(x, N, g, r, t, f, e, E)
518 push!(exp_cov, E_cov)
519 if E_cov < 0.95
520 N_95 = N
521 end
522 end
523 # E_sim, sd_sim = BioCCP_γₓ₂(x, N, g, r, t, f, e, E)
524 out_dict["E_cov"] = exp_cov
525 out_dict["N_95"] = N_95
526 out_dict["pls"] = plant_library_sizes
527
528 elseif command_args["M"] == 9
529 tool_info["mode"] = "BioCCP_γx3"
530 tool_info["mode_description"] = "Computes the expected coverage of all "*
531 "triple combinations of gene knockouts (E[γx,3]) for an experiment with "*
532 "given plant library size using BioCCP"
533
534 if s != nothing && MN != nothing
535 plant_library_sizes = N:s:MN
536 else
537 plant_library_sizes = N
538 end
539 exp_cov = []
540 global N_95 = nothing
541 for N in plant_library_sizes
542 E_cov = BioCCP_γₓ₃(x, N, g, r, t, f, e, E)
543 push!(exp_cov, E_cov)
544 if E_cov < 0.95
545 N_95 = N
546 end
547 end
548 # E_sim, sd_sim = BioCCP_γₓ₃(x, N, g, r, t, f, e, E)
549 out_dict["E_cov"] = exp_cov
550 out_dict["N_95"] = N_95
551 out_dict["pls"] = plant_library_sizes
552
553 end
554 end
555 end
556
557 println(parsed_args)
558 println("Parsed args:")
559 for (key,val) in parsed_args
560 println(" $key => $(repr(val))")
561 end
562 println()
563 println("Command: ", parsed_args["%COMMAND%"])
564 # h1 = histogram(grna_dict["p_gRNA_reads"], label="",
565 # xlabel="Number of reads per gRNA",
566 # linecolor="white",
567 # normalize=:probability,
568 # xtickfontsize=10,ytickfontsize=10,
569 # color=:mediumturquoise, size=(600,350), bins = 25,
570 # ylabel="Relative frequency",
571 # title="gRNA frequency distribution")
572
573 # h2 = histogram(grna_dict["p_gRNA_edit"],
574 # normalize = :probability,
575 # linecolor = "white",
576 # label="",
577 # color=:turquoise4,
578 # xtickfontsize=10,ytickfontsize=10, xlim = (0, 1),
579 # xticks=(0:0.1:1),
580 # bins = 150,
581 # xlabel="gRNA editing efficiency",
582 # ylabel="Relative frequency",
583 # title="gRNA genome editing effiency distribution")
584
585 # p_plot = plot(plant_library_sizes, Pₓ₂, label="Pₓ₂",
586 # title="Probability of full combinatorial coverage with respect to plant library size",
587 # xlabel="N", ylabel="Pₓₖ",
588 # xticks = (0:500:50000, string.(0:500:50000)),
589 # size=(900,400), color=:turquoise4, linewidth=2)
590 # hline!([0.95], linestyle=:dash, color=:grey, label="Pₓₖ = 0.95", legend=:bottomright)
591
592 # exp_plot = plot(plant_library_sizes, expected_γₓ₂,
593 # label="E[γₓ₂]", title="Expected combinatorial coverage w.r.t. plant library size",
594 # xlabel="N", ylabel="E[γₓₖ]",
595 # xticks = (0:500:50000, string.(0:500:50000)),
596 # size=(800,400), color=:turquoise4, linewidth=2)
597 # hline!([0.95], linestyle=:dash, color=:grey, label="E[γₓₖ] = 0.95", legend=:bottomright)
598
599
600 ENV["GKSwstype"]="nul"
601 weave(string(@__DIR__) * "/report.jmd",
602 args = (parsed_args = parsed_args,
603 tool_info = tool_info,
604 args_info = args_info,
605 grna_dict = grna_dict,
606 #h1 = h1, h2 = h2,
607 output = out_dict);
608 doctype = "md2html", out_path = :pwd)
609 ENV["GKSwstype"]="gksqt"
610 end
611
612 main(ARGS)