Newer
Older
## Re-build the package ####
devtools::document()
Sys.setenv('_R_CHECK_SYSTEM_CLOCK_' = 0)
devtools::check()
devtools::build() # build package and tar ball
devtools::build_manual(path = "../ume/inst") # build pdf documentation
# devtools::build_vignettes(pkg = "ume")
packageVersion("ume")
# Check package for suitability in CRAN
#system("cd D:/_AWI/_Daten/_git")
system("R CMD check --as-cran D:/_AWI/_Daten/_git/ume_1.0.3.tar.gz")
# Copy tar ball and pdf to AWI server ####
file.copy(
from = paste0(getwd(), "_", utils::packageVersion("ume"), ".tar.gz"),
to = paste0(
r"(\\smb.isibhv.dmawi.de\projects-noreplica\p_ume\UME\ume_)",
utils::packageVersion("ume"),
".tar.gz"
),
overwrite = T
)
file.copy(
from = paste0(getwd(), "/inst/ume_", utils::packageVersion("ume"), ".pdf"),
to = paste0(
r"(\\smb.isibhv.dmawi.de\projects-noreplica\p_ume\UME\ume_)",
utils::packageVersion("ume"),
".pdf"
),
overwrite = T
)
# Overview on package functions ####
all_funs <- ls("package:ume", all.names = TRUE)
all_funs
# List exported functions by reading the NAMESPACE file
exported_funs <- grep("^export\\(", readLines(system.file("NAMESPACE", package = "ume")), value = TRUE)
exported_funs <- gsub("export\\(|\\)", "", exported_funs)
# Identify non-exported functions
non_exported_funs <- setdiff(all_funs, exported_funs)
non_exported_funs
# Install ume package ####
# In case you already installed a previous version of ume:
rm(list = ls()) # Bereinige die Arbeitsumgebung
detach("package:ume", unload = TRUE)
.rs.restartR()
update.packages(ask = FALSE)
install.packages("remotes")
remotes::install_github("pmbrophy/mzDataTable")
# Install package with pre-built molecular formula libraries:
devtools::install_gitlab(
repo = 'bkoch/ume',
host = "https://gitlab.awi.de",
build_vignettes = TRUE,
force = FALSE,
dependencies = TRUE,
upgrade = "ask"
)
utils::install.packages(
"\\\\smb.isibhv.dmawi.de\\projects-noreplica\\p_ume\\UME\\ume_1.0.3.tar.gz",
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
repos = NULL,
type = "source"
)
# Install molecular formula library package:
devtools::install_gitlab(
repo = 'bkoch/ume.formulas',
host = "https://gitlab.awi.de",
build_vignettes = TRUE,
build_manual = TRUE,
dependencies = TRUE,
force = TRUE
)
library(ume)
packageVersion("ume")
vignette("ume")
news(package = "ume")
library(data.table)
library(plotly)
mfd_filt_cal <-
ume::process_orbi_data(
fn = fn[1:2],
auto_calibrate = TRUE,
calibr_list = "marine_dom",
c_iso_check = TRUE,
formula_library = ume.formulas::lib_02,
pol = "neg",
ma_dev = 2,
msg = TRUE
)
## Test existing UME tools:
## Settings ####
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
WD <-
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
WD
source("../mcupw.R")
# Application: Fjord demo peaklist ####
library(ume)
vignette("ume")
library(ume.formulas) # only if not already installed
library(data.table)
library(plotly)
# Select peaklist from Fjord demo set ####
pl <- ume.formulas::ume_test_fjords
check_peaklist(pl)
data(package = "ume.formulas", lib_02)
# Recalibrate spectra ####
out <-
ume::calc_recalibrate_ms(
pl = pl,
calibr_list = "cal_SRFA_OL_neg",
pol = "neg",
ma_dev = 1,
msg = TRUE
)
summary(out)
out$cal_stats
plot(out$cal_stats[, .(median_ma_before, median_ma_after)])
out$fig_hist_before
out$fig_hist_after
out$fig_box_before
out$fig_box_after
# Assign formulas ####
pl <- ume.formulas::ume_test_fjords
mfd <- ume_assign_formulas(
pl = pl
,
formula_library = ume.formulas::lib_02
,
pol = "neg"
,
ma_dev = 0.5
,
msg = FALSE
)
mfd <- calc_norm_int(mfd, normalization = "sum_ubiq")
calc_data_summary(mfd)
names(mfd)
mfd_filt <- ume::ume_filter_formulas(
mfd = mfd
# , select_file_ids = c("Nsea_a", "Nsea_b", "Nsea_c")
,
remove_blank_list = c("Blank")
,
normalization = "sum_ubiq"
,
norm_int_max = 0.9
# , select_category = c("marine_dom")
,
exclude_category = c("surfactant")
# , c_iso_check = T
# , n_iso_check = T
# , s_iso_check = T
# , ma_dev = 0.2
# , dbe_max = 2
# , dbe_o_min = 0
,
dbe_o_max = 10
,
p_min = 0,
p_max = 0
,
s_min = 0,
s_max = 1
,
n_min = 0,
n_max = 2
# , norm_int_min = 0.8
# , n_rank = 400
# , oc_min = 0, oc_max = 2.5
# , hc_min = 0, hc_max = 3
# , nc_min = 0, nc_max = 2
# , mz_min = 200, mz_max = 650
,
msg = TRUE
)
mfd_filt[, max(norm_int)]
uplot.vk(mfd_filt[ai > -2], z_var = "ai")
mfd_filt[n_occurrence_orig == 12, .(sum(i_magnitude), .N), file_id]
calc_data_summary(mfd_filt)
names(mfd_filt)
mfd_filt[, .N, .(file_id, int_ref)]
# Alternative using pipe operator:
mf_data_demo |>
eval_isotopes(remove_isotopes = T) |>
calc_eval_params() |>
add_known_mf() |>
order_columns()
# Alternative: using pipe operator.
mfd_filt <- ume.formulas::ume_test_fjords |>
ume_assign_formulas(
formula_library = ume.formulas::lib_02,
pol = "neg",
ma_dev = 0.5,
msg = T
) |>
filter_mf_data(
remove_blank_list = "Blank"
,
exclude_category = c("surfactant")
,
p_max = 0,
n_max = 2,
s_max = 1,
dbe_o_max = 10,
msg = T
) |>
calc_norm_int(normalization = "sum_ubiq", msg = T)
filter_int(norm_int_max = 3, msg = T)
dim(mfd_filt)
## Benchmarking and memory issues ####
# https://stackoverflow.com/a/45458117
# http://adv-r.had.co.nz/Profiling.html
# http://adv-r.had.co.nz/memory.html#memory-profiling
devtools::install_github("hadley/lineprof")
library(lineprof)
library(microbenchmark)
library(data.table)
ume.formulas::lib_02[, .N, s]
# benchmarking
microbenchmark(
mfd <-
ume::assign_formulas(
pl = ume.formulas::ume_test_fjords,
formula_library = lib_02,
pol = "neg",
ma_dev = 0.2,
msg = F
),
mfd_old <-
ume::assign_formulas_old(
pl = ume.formulas::ume_test_fjords,
formula_library = lib_02,
pol = "neg",
ma_dev = 0.2,
msg = F
),
times = 3
)
identical(mfd, mfd_old)
# Memory issues
l_new <-
lineprof(
assign_formulas(
pl = ume.formulas::ume_test_fjords,
formula_library = lib_02,
pol = "neg",
ma_dev = 0.2,
msg = T
)
)
l_new
shine(l_new)
l_old <-
lineprof(
assign_formulas_old(
pl = ume.formulas::ume_test_fjords,
formula_library = lib_02,
pol = "neg",
ma_dev = 0.2,
msg = T
)
)
l_old
shine(l_old)
## Memory issues: column formats (factor data type is smaller than character)
ls("package:ume")
pl <- ume.formulas::ume_test_fjords
pl[, file_id := as.factor(file_id)] # to do: check with Fabian and Marlo a general concept on "file_id"
str(pl)
sapply(pl, object.size)
object.size(pl)
# eval_isotopes
ume_new <- lineprof(
ume_assign_formulas(
pl = ume.formulas::ume_test_fjords
,
formula_library = ume.formulas::lib_02
,
pol = "neg"
,
ma_dev = 0.2
,
msg = T
)
)
## Improve documentation ####
## https://roxygen2.r-lib.org/articles/rd.html
? assign_formulas
browser(devtools::check())
ume::chec# For UME package development ####
ume::load_mzml()
library("devtools") # For package building
library(roxygen2) # For package building
require(usethis) # For package building
library(testthat)
library(utils)
#library(available) # Checks if a package name already exists or name is problematic
# roxygenize
roxygen2::roxygenise()
# Call packages from tidyverse (contains pipe operator %>%, stringr, tibble, purrr (for map() function)):
library(magrittr) # pipe operator
#library(EnvStats)
tools::package_dependencies("ume", check = FALSE, depLevel = "Depends")
library(tidyverse)
avail_pks <- available.packages()
deps <-
tools::package_dependencies(packages = avail_pks[1:200, "Package"],
recursive = TRUE)
#argg <- c(as.list(environment()), list(...)) # This yields all function arguments and values in ellipsis
#names(inargs) # names of all arguments in ellipsis
#names(args) # names of all arguments in ellipsis
# box-plot in plotly ####
summary(out)
plot_ly(
data = out$pl,
y = ~ ppm,
type = "box"
,
color = ~ as.factor(calibration)
)
x <- fig_old %>% add_boxplot(fig_new)
x
y <-
x %>% add_annotations(
text = "Test",
x = 0.2,
y = 0.2,
xref = "paper",
yref = "paper",
align = "left",
bgcolor = "lightgrey",
opacity = 0.8
)
y
fig2 <-
plot_ly(mf_data_demo[, .(nsp_type, ppm)],
x = ~ nsp_type,
y = ~ ppm,
type = "box") %>%
layout(
title = "Avg. mass error for NSP combinations",
xaxis = list(title = "All combinations of N, S, and P atoms"),
yaxis = list(title = "Mass accuracy (ppm)")
)
fig2
# MAINTENANCE ####
# Update known_mf ####
library(RMySQL)
library(data.table)
library(sam)
ch <- sam::f_sam_connect(user, pw)
known_mf <-
data.table(DBI::dbGetQuery(ch, "SELECT * FROM MarChem.tab_ume_info_known_mf"))
DBI::dbDisconnect(ch)
usethis::use_data(known_mf,
overwrite = TRUE,
version = 3,
compress = "xz")
save(known_mf, file = "data/known_mf.rda", version = 2)
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
known_mf <- ume::known_mf
load("data/known_mf.rda")
known_mf
## New functions:_________________________ ####
## Intensity significance threshold (IST; provided by Maria da Silva) ####
## Taken from RMD file (email)
# Filter formulas shared in the repeated measurements
formulas.pool.shared <-
formulas.pool[occurrence_count == max(formulas.pool$occurrence_count)]
length(unique(formulas.pool.shared$cf_id))
length(unique(formulas.pool.shared$measurement_id))
#scale rank in the samples and repeated measurements
formulas.pool.shared <-
formulas.pool.shared %>% .[, `:=`(peak_intensity_rank, rank(peak_intensity,
ties.method = "min")), by = "measurement_id"] %>% .[,
`:=`(measurement_rank,
rank(peak_intensity_rank, ties.method = "min")),
by = "cf_id"]
formulas.smp.shared <-
formulas.smp.shared %>% .[, `:=`(peak_intensity_rank, rank(peak_intensity,
ties.method = "min")), by = "measurement_id"] %>% .[,
`:=`(measurement_rank,
rank(peak_intensity_rank, ties.method = "min")),
by = "cf_id"]
#Function to test significance
#define groups
Groups.qc <-
data.table(Measurement = unique(formulas.pool.shared$measurement_name) ,
Group = "QC")
#calculate average spectra
LM_stats.intensity_average(formulas.pool.shared, Groups.qc, suffix = "qc")
#plot
var_tic <- plot_RI_repro_TIC(formulas.qc.avg)
var_bp <- plot_RI_repro_BP(formulas.qc.avg)
plot_intensityErrorDistribution(formulas.qc.avg, "peak_relint_tic")
plot_intensityErrorDistribution(formulas.qc.avg, "peak_relint_bp")
#rsd from average spectra
formulas.qc.avg[, peak_relint_tic_rsd := peak_relint_tic_sd / peak_relint_tic]
formulas.qc.avg[, peak_relint_bp_rsd := peak_relint_bp_sd / peak_relint_bp]
##scale ranks
formulas.pool.shared <- formulas.pool.shared %>%
group_by(measurement_id) %>%
mutate(xnorm = (peak_intensity_rank - min(peak_intensity_rank)) / (max(peak_intensity_rank) - min(peak_intensity_rank)))
formulas.smp.shared <- formulas.smp.shared %>%
group_by(measurement_id) %>%
mutate(xnorm = (peak_intensity_rank - min(peak_intensity_rank)) / (max(peak_intensity_rank) - min(peak_intensity_rank)))
#take scaled ranks across measurements
x <-
dcast(formulas.pool.shared,
formula_mass ~ measurement_id,
value.var = "xnorm")
#calculate mean rank
rowMean <- apply(x[, -1], 1, FUN = mean)
#take max rank
rowMax <- apply(x[, -1], 1, FUN = max)
#calculate median rank
rowMedian <- apply(x[, -1], 1, FUN = median)
z <- melt(x, "formula_mass")
#create function for confidence interval
cof_int <- function(n, q, z) {
j <- n * q - z * sqrt(n * q * (1 - q))
k <- n * q + z * sqrt(n * q * (1 - q))
return(list(j, k))
}
#find ith confidence interval
cof_int(10, 0.68, 1)
#find values for confidence interval
a <- 1
lower_limit <- vector()
upper_limit <- vector()
for (i in seq(1, nrow(x))) {
row_mass <- sort(x[i, -1])
lower_limit[a] <- nth(row_mass, 6)
upper_limit[a] <- nth(row_mass, 9)
a <- a + 1
}
#find rank threshold
limits <-
as.data.frame(cbind(lower_limit, upper_limit, rowMean, x[, 1]))
limits$iqr <- upper_limit - lower_limit
limits$rsd <- (limits$iqr / limits$rowMean)
colnames(limits) <-
c("lower_limit",
"upper_limit",
"peak_intensity_rank",
"formula_mass",
"iqr",
"rsd")
#plot rank
var_rank <-
ggplot(limits,
aes(y = rsd * 100, x = peak_intensity_rank, color = measurement_name)) +
geom_point(alpha = 0.1, color = "#1b9e77") +
geom_smooth(method = "gam", color = "red") +
labs(title = "", y = "RSD Normalized Intensity [%]", x = "mean ranked intensity") +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(
var_tic,
var_bp,
var_rank,
labels = c("A", "B", "C"),
ncol = 3,
nrow = 1
)
#adjust model
gam.mdl_tic <-
gam((peak_relint_tic_sd / peak_relint_tic) ~ s(peak_relint_tic),
data = formulas.qc.avg)
gam.mdl_bp <-
gam((peak_relint_bp_sd / peak_relint_bp) ~ s(peak_relint_bp), data = formulas.qc.avg)
gam.mdl_rank <- gam(rsd ~ s(peak_intensity_rank), data = limits)
#predict values
df_mdl_int_tic <-
data.frame(
"measurement_id" = formulas.smp.shared$measurement_id,
"cf_id" = formulas.smp.shared$cf_id,
"peak_relint_tic" = formulas.smp.shared$peak_relint_tic
)
df_mdl_int_tic$predict_cv <-
predict.gam(gam.mdl_tic, df_mdl_int_tic)
df_mdl_int_tic$sd <-
df_mdl_int_tic$peak_relint_tic * df_mdl_int_tic$predict_cv
df_mdl_int_bp <-
data.frame(
"measurement_id" = formulas.smp.shared$measurement_id,
"cf_id" = formulas.smp.shared$cf_id,
"peak_relint_bp" = formulas.smp.shared$peak_relint_bp
)
df_mdl_int_bp$predict_cv <- predict.gam(gam.mdl_bp, df_mdl_int_bp)
df_mdl_int_bp$sd <-
df_mdl_int_bp$peak_relint_bp * df_mdl_int_bp$predict_cv
df_mdl_int_rank <-
data.frame(
"measurement_id" = formulas.smp.shared$measurement_id,
"cf_id" = formulas.smp.shared$cf_id,
"peak_intensity_rank" = formulas.smp.shared$xnorm,
"peak_rank_orig" = formulas.smp.shared$peak_intensity_rank
)
df_mdl_int_rank$predict_cv <-
predict.gam(gam.mdl_rank, df_mdl_int_rank)
df_mdl_int_rank$sd <-
df_mdl_int_rank$peak_rank_orig * df_mdl_int_rank$predict_cv
#transform data
df_mdl_int_tic <- as.data.table(df_mdl_int_tic)
df_mdl_int_bp <- as.data.table(df_mdl_int_bp)
df_mdl_int_rank <- as.data.table(df_mdl_int_rank)
#find lowest intensity across samples for each cf_id
low_mdl_tic <-
df_mdl_int_tic[, .(min(peak_relint_tic),
mean(peak_relint_tic),
sd(peak_relint_tic)), by = "cf_id"]
low_mdl_bp <-
df_mdl_int_bp[, .(min(peak_relint_bp),
mean(peak_relint_bp),
sd(peak_relint_bp)), by = "cf_id"]
low_mdl_rank <-
df_mdl_int_rank[, .(min(peak_intensity_rank),
mean(peak_rank_orig),
sd(peak_rank_orig)), by = "cf_id"]
#rename columns
colnames(low_mdl_tic) <-
c("cf_id",
"peak_relint_tic",
"peak_relint_tic_mean",
"peak_relint_tic_sd")
colnames(low_mdl_bp) <-
c("cf_id",
"peak_relint_bp",
"peak_relint_bp_mean",
"peak_relint_bp_sd")
colnames(low_mdl_rank) <-
c(
"cf_id",
"peak_intensity_rank",
"peak_intensity_rank_mean",
"peak_intensity_rank_sd"
)
#calculate cv
low_mdl_tic$calculated_cv <-
low_mdl_tic$peak_relint_tic_sd / low_mdl_tic$peak_relint_tic_mean
low_mdl_bp$calculated_cv <-
low_mdl_bp$peak_relint_bp_sd / low_mdl_bp$peak_relint_bp_mean
low_mdl_rank$calculated_cv <-
low_mdl_rank$peak_intensity_rank_sd / low_mdl_rank$peak_intensity_rank_mean
#find modeled cv for lowest intensity
low_mdl_tic$predict_cv <- predict.gam(gam.mdl_tic, low_mdl_tic)
low_mdl_bp$predict_cv <- predict.gam(gam.mdl_bp, low_mdl_bp)
low_mdl_rank$predict_cv <- predict.gam(gam.mdl_rank, low_mdl_rank)
#find formulas that are excluded by intensity threshold
n_excluded_tic <- low_mdl_tic[predict_cv < calculated_cv]
n_excluded_bp <- low_mdl_bp[predict_cv < calculated_cv]
n_excluded_rank <- low_mdl_rank[predict_cv < calculated_cv]
## Statistics: Cluster, MDS, PCA aus UltraMassExplorer ####
# Spreadsheet 09: "Statistics" ========================================
# _____________________________________________________________
# Cluster analysis and multi-dimensional scaling ####
# _____________________________________________________________
um_plot.cluster <- function(df, grp1)
{
print("***************************************************")
print("Plotting cluster diagram & multi-dimensional scaling...")
df_pivot <-
dcast(
df,
get(grp1) ~ mf,
value.var = ri_stats,
fun = mean,
fill = 0
)
max_char <-
max(nchar(as.character(df_pivot[, grp1]))) # Determine the length of axis label
df_pivot <-
data.frame(df_pivot) # convert data.table to dataframe
rownames(df_pivot) = df_pivot[, 1] # create rownames from "grp1"
df_pivot[, 1] = NULL # delete grp1 names
if (length(unique(df$file_id)) > 2) {
par(mfrow = c(1, 2))
par(mar = c(max_char + 1, 7, 4, 2)) # optimize margins according to length of labels
d <-
vegdist(df_pivot, method = "bray") * 100 # scale distances to 100 instead 1
h <- hclust(d, method = "average")
plot(
as.dendrogram(h),
cex = 1,
horiz = F,
nodePar = NULL,
ylab = "Bray-Curtis dissimilarity"
) # plot cluster
# nodePar <- list(lab.cex = 0.6, pch = c(NA, 19), cex = 0.8, col = "blue")
# plot(hcd, type = "rectangle", nodePar = nodePar, xlab = "Height", horiz = TRUE) # Clustering as tree
#cut = rect.hclust(hc,k=3)
#plot(h, ylab="Bray-Curtis Similarity", main="", sub = "", xlab="", axes = FALSE, hang = -1)
#lines(x = c(0,0), y = c(0,100), type = "n") # force extension of y axis
#axis(side = 2, at = seq(0,100,10), labels = seq(100,0,-10), ylab="Bray-Curtis Similarity")
# improvements:
# http://www.sthda.com/english/wiki/beautiful-dendrogram-visualizations-in-r-5-must-known-methods-unsupervised-machine-learning
# p <- ggdendro::ggdendrogram(h, rotate = FALSE, size = 2)
# ggplotly(p)
# _____________________________________________________________
# Multi-dimensional scaling
# _____________________________________________________________
mds <- metaMDS(d, k = 2)
fig <- ordiplot(mds, type = "none", display = "sites")
points(fig, "sites", pch = "+", col = "black")
orditorp(
mds,
display = "sites",
cex = 1.2,
air = .7,
col = "grey"
)
}
if (length(unique(df$file_id)) <= 2) {
print("Statistical evaluation requires more than 2 samples in analysis!")
}
}
# _____________________________________________________________
# Principal component analysis (PCA) ####
# https://sites.google.com/site/mb3gustame/reference/dissimilarity
# _____________________________________________________________
um_plot.pca <- function(df, grp1)
{
df_pivot <-
dcast(
df,
get(grp1) ~ mf,
value.var = ri_stats,
fun = mean,
fill = 0
)
max_char <-
max(nchar(as.character(df_pivot[, grp1]))) # Determine the length of axis label
df_pivot <-
data.frame(df_pivot) # convert data.table to dataframe
rownames(df_pivot) = df_pivot[, 1] # create rownames from "grp1"
df_pivot[, 1] = NULL # delete grp1 names
# Remove columns with zero variance (PCA won't work with those)
# https://stackoverflow.com/questions/40315227/how-to-solve-prcomp-default-cannot-rescale-a-constant-zero-column-to-unit-var
df_pivot <- df_pivot[, which(apply(df_pivot, 2, var) != 0)]
pca <- prcomp(df_pivot, scale. = T, rank. = 5)
#summary(pca)
#head(pca,2)
#eigen(pca)
t_score <- data.frame(pca$x) # show all scores
t_score$files <- rownames(pca$x) # add file id as columnm
t_score <- data.table(t_score) # convert to data.table
#wcsv(t_score, "PC_scores.csv")
# plot(pca) # Scree-Plot: how much variance in which PC?
par(mfrow = c(1, 2))
# plot rotated data ("scores") of samples, only component 1 vs 2
plot(
t_score[, 1:2],
pch = 16,
lwd = 2,
xlab = paste("PC1", " (", signif(pca$sdev[1] ^ 2 / sum(pca$sdev ^
2) * 100, 3), "%)", sep = ""),
ylab = paste("PC2", " (", signif(pca$sdev[2] ^ 2 / sum(pca$sdev ^
2) * 100, 3), "%)", sep = ""),
main = "",
xlim = c(min(t_score$PC1), max(t_score$PC1) * 1.5)
)
#points(pca$x[,c(1,2)], pch = 5, cex = 2, lwd = 2, col = "blue") # add points to an existing plot
text(
t_score[, 1:2],
t_score$files,
offset = 1,
pos = 4,
col = "red"
) # add text to a plot
#biplot(pca)
#scores(pca)
#res_cov2 <- cov(df_pivot) # calculate covariance matrix
#eigen(res_cov2) # eigenvalues and eigenvectors (careful this costs computing power!!!)
t <- data.frame(pca[2])
colnames(t) <- colnames(pca$x)
t$mf <- row.names(t)
t <- data.table(t)
df <- t[df, on = "mf"] # add principal components info
df <- df[!is.na(PC1)]
ume::uplot.vk(df,
col = "redblue",
col_bar = T,
z_var = "PC1")
return(pca)
}
ri_stats = "norm_int"
um_plot.cluster(df = mfd_filt, grp1 = "sample_tag",)