-
Notifications
You must be signed in to change notification settings - Fork 3
/
differential_expression.Rmd
1403 lines (1203 loc) · 59.4 KB
/
differential_expression.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
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
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
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
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
---
title: "Differential expression across all donors"
author: "Davis J. McCarthy"
site: workflowr::wflow_site
---
Here, we will lok at differential expresion between clones across all lines (
i.e. donors) at the gene and gene set levels.
## Load libraries, data and DE results
```{r setup, include=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
library(tidyverse)
library(scater)
library(ggridges)
library(GenomicRanges)
library(RColorBrewer)
library(edgeR)
library(ggrepel)
library(ggcorrplot)
library(rlang)
library(limma)
library(org.Hs.eg.db)
library(ggforce)
library(superheat)
library(viridis)
library(IHW)
library(cowplot)
library(broom)
options(stringsAsFactors = FALSE)
```
Load the genewise differential expression results produced with the edgeR
quasi-likelihood F test and gene set enrichment results produced with camera.
```{r load-de}
params <- list()
params$callset <- "filt_lenient.cell_coverage_sites"
load(file.path("data/human_c6_v5p2.rdata"))
load(file.path("data/human_H_v5p2.rdata"))
load(file.path("data/human_c2_v5p2.rdata"))
de_res <- readRDS(paste0("data/de_analysis_FTv62/",
params$callset,
".de_results_unstimulated_cells.rds"))
```
Load SingleCellExpression objects with data used for differential expression
analyses.
```{r load-sce}
fls <- list.files("data/sces")
fls <- fls[grepl(params$callset, fls)]
donors <- gsub(".*ce_([a-z]+)_.*", "\\1", fls)
sce_unst_list <- list()
for (don in donors) {
sce_unst_list[[don]] <- readRDS(file.path("data/sces",
paste0("sce_", don, "_with_clone_assignments.", params$callset, ".rds")))
cat(paste("reading", don, ": ", ncol(sce_unst_list[[don]]), "cells.\n"))
}
```
The starting point for differential expression analysis was a set of
`r length(donors)` donors, of which `r length(names(de_res$dge_list))` donors
had enough cells assigned to clones to conduct DE testing.
Summarise cell assignment information.
```{r assignments}
assignments_lst <- list()
for (don in donors) {
assignments_lst[[don]] <- as_data_frame(
colData(sce_unst_list[[don]])[,
c("donor_short_id", "highest_prob",
"assigned", "total_features",
"total_counts_endogenous", "num_processed")])
}
assignments <- do.call("rbind", assignments_lst)
```
`r round(100 * mean(assignments$assigned != "unassigned"))`% of cells from these
donors are assigned with confidence to a clone.
Load donor info including evidence for selection dynamics in donors.
```{r load-donor-info}
df_donor_info <- read.table("data/donor_info_070818.txt")
```
## Genewise DE results
We first look at differential expression at the level of individual genes.
```{r genewise-de}
fdr_thresh <- 1
df_de_all_unst <- data_frame()
for (donor in names(de_res[["qlf_list"]])) {
tmp <- de_res[["qlf_list"]][[donor]]$table
tmp$gene <- rownames(de_res[["qlf_list"]][[donor]]$table)
ihw_res <- ihw(PValue ~ logCPM, data = tmp, alpha = 0.05)
tmp$FDR <- adj_pvalues(ihw_res)
tmp <- tmp[tmp$FDR <= fdr_thresh,]
if (nrow(tmp) > 0.5) {
tmp[["donor"]] <- donor
df_de_all_unst <- bind_rows(df_de_all_unst, tmp)
}
}
df_ncells_de <- assignments %>% dplyr::filter(assigned != "unassigned",
donor_short_id %in% names(de_res$qlf_list)) %>%
group_by(donor_short_id) %>%
summarise(n_cells = n())
colnames(df_ncells_de)[1] <- "donor"
fdr_thresh <- 0.1
df_de_sig_unst <- data_frame()
for (donor in names(de_res[["qlf_list"]])) {
tmp <- de_res[["qlf_list"]][[donor]]$table
tmp$gene <- rownames(de_res[["qlf_list"]][[donor]]$table)
ihw_res <- ihw(PValue ~ logCPM, data = tmp, alpha = 0.05)
tmp$FDR <- adj_pvalues(ihw_res)
tmp <- tmp[tmp$FDR < fdr_thresh,]
if (nrow(tmp) > 0.5) {
tmp[["donor"]] <- donor
df_de_sig_unst <- bind_rows(df_de_sig_unst, tmp)
}
}
df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
summarise(count = n()) %>%
ggplot(aes(x = n_donors, y = count)) +
geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1),
colour = "gray50") +
geom_point(size = 3) +
scale_y_log10(breaks = c(10, 100, 1000)) +
scale_x_continuous(breaks = 0:11) +
coord_cartesian(ylim = c(1, 2000)) +
theme_classic(20) +
xlab("Number of lines in which significant (FDR < 10%)") +
ylab("Number of genes") +
ggtitle("edgeR QL F test DE results")
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes.png",
height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes.pdf",
height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes.svg",
height = 7, width = 10)
p1 <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
summarise(count = n()) %>%
ggplot(aes(x = n_donors, y = count)) +
geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1),
colour = "gray50") +
geom_point(size = 3) +
scale_x_continuous(breaks = 0:11) +
#coord_cartesian(ylim = c(1, 2200)) +
theme_classic(16) +
xlab("Number of lines significant (FDR < 10%)") +
ylab("Number of genes")
p1
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_linscale.png",
height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_linscale.pdf",
height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_linscale.svg",
height = 7, width = 5.5)
```
```{r, fig.height=14, fig.width=12}
p2 <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% dplyr::arrange(gene, n_donors) %>% ungroup() %>%
dplyr::mutate(gene = gsub("ENSG.*_", "", gene)) %>%
dplyr::filter(n_donors > 7.5) %>%
ggplot(aes(y = n_donors, x = reorder(gene, n_donors, max))) +
geom_point(alpha = 0.7, size = 4) +
scale_y_continuous(breaks = 7:11) +
ggthemes::scale_colour_tableau() +
coord_flip() +
theme_bw(16) +
xlab("Gene") + ylab("Number of lines significant")
p2
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_topgenes.png",
height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_topgenes.pdf",
height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_topgenes.svg",
height = 7, width = 5.5)
#cowplot::plot_grid(p1, p2, rel_heights = c(0.4, 0.6))
df_donor_n_de <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
group_by(donor) %>%
summarise(count = n())
no_de_donor <- unique(df_de_all_unst[["donor"]])[!(unique(df_de_all_unst[["donor"]]) %in% df_donor_n_de[["donor"]])]
df_donor_n_de <- rbind(df_donor_n_de, data_frame(donor = no_de_donor, count = 0))
```
Permute gene labels to get a null distribution.
```{r permute-de}
df_de_nsig <- df_de_all_unst %>% dplyr::filter(FDR < 0.1) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
group_by(donor) %>%
summarise(n_sig = n())
df_nsig_ncells_de <- full_join(df_ncells_de, df_de_nsig)
df_nsig_ncells_de$n_sig[is.na(df_nsig_ncells_de$n_sig)] <- 0
permute_gene_labels <- function(gene_names, n_de) {
sampled_genes <- c()
for (i in seq_along(n_de))
sampled_genes <- c(sampled_genes, sample(gene_names, size = n_de[i]))
tab <- table(table(sampled_genes))
df <- data_frame(n_donors = 1:11, n_genes = 0)
df[names(tab), 2] <- tab
df
}
n_perm <- 1000
df_perm <- list()
for (i in seq_len(n_perm))
df_perm[[i]] <- permute_gene_labels(rownames(de_res$qlf_list$vass$table),
df_nsig_ncells_de[["n_sig"]])
df_perm <- do.call("rbind", df_perm)
df_perm <- dplyr::mutate(df_perm, data_type = "permuted")
df_perm %>% group_by(n_donors) %>% summarise(min = min(n_genes),
median = median(n_genes),
mean = mean(n_genes),
max = max(n_genes))
ppp <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
summarise(n_genes = n()) %>% dplyr::mutate(data_type = "observed") %>%
ggplot(aes(x = n_donors, y = n_genes)) +
# geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1),
# colour = "gray50") +
# geom_hline(yintercept = 0, linetype = 2) +
geom_hline(yintercept = 0) +
geom_boxplot(aes(group = n_donors, y = n_genes, colour = data_type),
fill = "gray80", data = df_perm, show.legend = FALSE) +
geom_point(aes(colour = data_type), shape = 17, size = 5) +
scale_x_continuous(breaks = 0:11) +
scale_y_sqrt(breaks = c(0, 10, 100, 500, 1000, 2000, 3000),
labels = c(0, 10, 100, 500, 1000, 2000, 3000),
limits = c(0, 4500)) +
scale_colour_manual(name = '',
values = c("observed" = "black", "permuted" = "gray50"),
labels = c("observed", "permuted")) +
coord_cartesian(ylim = c(0, 4500)) +
theme_bw(18) +
theme(legend.position = c(0.8, 0.88),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()) +
guides(colour = guide_legend(override.aes = list(shape = c(17, 19))),
fill = FALSE, boxplot = FALSE) +
xlab("Number of lines significant (FDR < 10%)") +
ylab("Number of genes")
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm.png",
height = 7, width = 10, plot = ppp)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm.pdf",
height = 7, width = 10, plot = ppp)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm.svg",
height = 7, width = 10, plot = ppp)
df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
summarise(n_genes = n()) %>% dplyr::mutate(data_type = "observed") %>%
ggplot(aes(x = n_donors, y = n_genes)) +
# geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1),
# colour = "gray50") +
# geom_hline(yintercept = 0, linetype = 2) +
geom_hline(yintercept = 0) +
geom_boxplot(aes(group = n_donors, y = n_genes, colour = data_type),
fill = "gray80", data = df_perm, show.legend = FALSE) +
geom_point(aes(colour = data_type), shape = 17, size = 5) +
scale_x_continuous(breaks = 0:11) +
scale_y_sqrt(breaks = c(0, 10, 100, 500, 1000, 2000, 3000),
labels = c(0, 10, 100, 500, 1000, 2000, 3000),
limits = c(0, 4500)) +
scale_colour_manual(name = '',
values = c("observed" = "black", "permuted" = "gray50"),
labels = c("observed", "permuted")) +
coord_cartesian(ylim = c(0, 4500)) +
theme(legend.position = c(0.8, 0.88),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()) +
guides(colour = guide_legend(override.aes = list(shape = c(17, 19))),
fill = FALSE, boxplot = FALSE) +
xlab("Number of lines significant (FDR < 10%)") +
ylab("Number of genes")
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm_skinny.png",
height = 5.5, width = 6.5)
```
Look at recurrently DE genes.
```{r recurrent-de}
df_gene_n_de <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
group_by(gene) %>%
summarise(count = n()) %>%
dplyr::arrange(desc(count)) %>%
dplyr::mutate(ensembl_gene_id = gsub("_.*", "", gene),
hgnc_symbol = gsub(".*_", "", gene))
df_gene_n_de <- left_join(
df_gene_n_de,
dplyr::select(de_res$qlf_pairwise$joxm$clone2_clone1$table,
ensembl_gene_id, hgnc_symbol, entrezid)
)
df_gene_n_de <- dplyr::mutate(
df_gene_n_de,
cell_cycle_growth = (entrezid %in%
c(Hs.H$HALLMARK_G2M_CHECKPOINT,
Hs.H$HALLMARK_MITOTIC_SPINDLE,
Hs.H$HALLMARK_E2F_TARGETS)),
myc = (entrezid %in% c(Hs.H$HALLMARK_MYC_TARGETS_V1,
Hs.H$HALLMARK_MYC_TARGETS_V2)),
emt = (entrezid %in% c(Hs.H$HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION))
)
df_gene_n_de %>% dplyr::filter(count >= 8) %>%
DT::datatable(.)
```
----------
## Camera results
First, aggregate gene set enrichment results across all donors.
```{r agg-camera-res}
fdr_thresh <- 1
df_camera_all_unst <- data_frame()
for (geneset in names(de_res[["camera"]])) {
for (donor in names(de_res[["camera"]][[geneset]])) {
for (coeff in names(de_res[["camera"]][[geneset]][[donor]])) {
for (stat in names(de_res[["camera"]][[geneset]][[donor]][[coeff]])) {
tmp <- de_res[["camera"]][[geneset]][[donor]][[coeff]][[stat]]
tmp <- tmp[tmp$FDR <= fdr_thresh,]
if (nrow(tmp) > 0.5) {
tmp[["collection"]] <- geneset
tmp[["geneset"]] <- rownames(tmp)
tmp[["coeff"]] <- coeff
tmp[["donor"]] <- donor
tmp[["stat"]] <- stat
df_camera_all_unst <- bind_rows(df_camera_all_unst, tmp)
}
}
}
}
}
fdr_thresh <- 0.05
df_camera_sig_unst <- data_frame()
for (geneset in names(de_res[["camera"]])) {
for (donor in names(de_res[["camera"]][[geneset]])) {
for (coeff in names(de_res[["camera"]][[geneset]][[donor]])) {
for (stat in names(de_res[["camera"]][[geneset]][[donor]][[coeff]])) {
tmp <- de_res[["camera"]][[geneset]][[donor]][[coeff]][[stat]]
tmp <- tmp[tmp$FDR <= fdr_thresh,]
if (nrow(tmp) > 0.5) {
tmp[["collection"]] <- geneset
tmp[["geneset"]] <- rownames(tmp)
tmp[["coeff"]] <- coeff
tmp[["donor"]] <- donor
tmp[["stat"]] <- stat
df_camera_sig_unst <- bind_rows(df_camera_sig_unst, tmp)
}
}
}
}
}
df_camera_sig_unst <- dplyr::mutate(
df_camera_sig_unst,
contrast = gsub("_", " - ", coeff),
msigdb_collection = plyr::mapvalues(collection, from = c("c2", "c6", "H"), to = c("MSigDB curated (c2)", "MSigDB oncogenic (c6)", "MSigDB Hallmark")))
df_camera_all_unst <- dplyr::mutate(
df_camera_all_unst,
contrast = gsub("_", " - ", coeff),
msigdb_collection = plyr::mapvalues(collection, from = c("c2", "c6", "H"), to = c("MSigDB curated (c2)", "MSigDB oncogenic (c6)", "MSigDB Hallmark")))
```
We now have a dataframe for significant (FDR <5%) results from the camera
gene set enrichment results.
```{r}
head(df_camera_sig_unst)
```
And a dataframe with all results.
```{r}
head(df_camera_all_unst)
```
For now, focus on gene set enrichment results computed using log-fold change
statistics for pairwise comparisons of clones estimated from the edgeR QL-F
models.
We can look at all significant results summarised by donor, geneset and pairwise
contrast of clones.
```{r camera-allcontr-allsets-sig-bydonor-pvals, fig.height=17, fig.width=11}
df_camera_sig_unst %>%
dplyr::filter(stat == "logFC") %>%
dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
ggplot(aes(y = -log10(PValue), x = donor, colour = contrast)) +
geom_sina(alpha = 0.7) +
facet_grid(contrast ~ msigdb_collection) +
scale_colour_brewer(palette = "Accent") +
coord_flip() + theme_bw()
```
Similarly, we can look at all results summarised by donor, geneset and pairwise
contrast of clones.
```{r camera-allcontr-allsets-all-bydonor-fdr, fig.height=17, fig.width=11}
df_camera_all_unst %>%
dplyr::filter(stat == "logFC") %>%
dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
ggplot(aes(y = -log10(FDR), x = donor, colour = contrast)) +
geom_sina(alpha = 0.7) +
geom_hline(yintercept = -log10(0.05), linetype = 2, colour = "firebrick") +
facet_grid(contrast ~ msigdb_collection, scales = "free_x") +
scale_colour_brewer(palette = "Accent") +
coord_flip() + theme_bw()
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_all_results.png",
height = 16, width = 14)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_all_results.pdf",
height = 16, width = 14)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_all_results.svg",
height = 16, width = 14)
```
We can check the number of significant gene sets for each donor, for each MSigDB
gene set collection.
```{r camera-nsig-donor}
df_camera_sig_unst %>%
dplyr::filter(stat == "logFC") %>%
dplyr::filter(FDR < 0.05) %>%
group_by(donor, msigdb_collection) %>%
summarise(n_sig = n()) %>% print(n = Inf)
```
We can look at the number of significant gene sets for each donor.
```{r alldonors_camera_enrichment_by_donor_simple, fig.height=12, fig.width=8}
## simpler version
df_camera_all_unst %>%
dplyr::filter(stat == "logFC") %>%
group_by(donor, msigdb_collection) %>%
summarise(n_sig = sum(FDR < 0.05)) %>% ungroup() %>%
dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
ggplot(aes(y = n_sig, x = donor)) +
geom_point(alpha = 1, size = 4) +
facet_wrap(~ msigdb_collection, scales = "free_x") +
scale_fill_brewer(palette = "Accent") +
coord_flip() +
theme_bw(16) +
xlab("Donor") + ylab("Number of significant genesets (FDR < 5%)") +
ggtitle("Camera MSigDB gene set enrichment by line")
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple.png",
height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple.pdf",
height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple.svg",
height = 7, width = 10)
```
We can look at the effect of the the number of cells for each donor on the
DE results obtained.
```{r alldonors_camera_enrichment_by_donor_simple_size_by_ncells, fig.height=12, fig.width=8}
ncells_by_donor <- rep(NA, length(sce_unst_list))
names(ncells_by_donor) <- names(sce_unst_list)
for (don in names(sce_unst_list))
ncells_by_donor[don] <- ncol(sce_unst_list[[don]])
df_camera_all_unst %>%
dplyr::filter(stat == "logFC") %>%
group_by(donor, msigdb_collection) %>%
summarise(n_sig = sum(FDR < 0.05)) %>% ungroup() -> df_to_plot
df_to_plot <- inner_join(df_to_plot,
data_frame(donor = names(ncells_by_donor),
ncells = ncells_by_donor))
df_to_plot %>%
dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
ggplot(aes(y = n_sig, x = donor, size = ncells)) +
geom_point(alpha = 1) +
facet_wrap(~ msigdb_collection, scales = "free_x") +
scale_fill_brewer(palette = "Accent") +
coord_flip() +
theme_bw(16) +
xlab("Donor") + ylab("Number of significant genesets (FDR < 5%)") +
ggtitle("Camera MSigDB gene set enrichment by line")
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple_size_by_ncells.png",
height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple_size_by_ncells.pdf",
height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple_size_by_ncells.svg",
height = 7, width = 10)
```
### Hallmark gene set
Focus now on looking at DE results for the MSigDB Hallmark gene set (50 of the
best-characterised gene sets as determined by MSigDB).
Look at the gene sets that are found to be enriched in multiple donors.
```{r alldonors_camera_enrichment_H_by_geneset, fig.height=7, fig.width=10}
## Hallmark geneset
df_camera_sig_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC") %>%
group_by(geneset) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
ggplot(aes(y = n_donors, x = reorder(geneset, n_donors, max))) +
geom_point(alpha = 0.7, size = 4) +
ggthemes::scale_colour_tableau() +
coord_flip() +
theme_bw(14) +
xlab("Gene set") + ylab("Number of lines significant")
ggsave("figures/differential_expression/alldonors_camera_enrichment_H_by_geneset.png",
height = 7, width = 9.5)
ggsave("figures/differential_expression/alldonors_camera_enrichment_H_by_geneset.pdf",
height = 7, width = 9.5)
ggsave("figures/differential_expression/alldonors_camera_enrichment_H_by_geneset.svg",
height = 7, width = 9.5)
## number of donors with at least one significant geneset
tmp <- df_camera_sig_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC") %>%
group_by(geneset)
unique(tmp[["donor"]])
```
`r length(unique(tmp[["donor"]]))` donors have at least one significantly
enriched Hallmark gene set.
For gene sets related directly to cell cycle and growth, we see contrasts being
both up- and down- regulated, but for EMT, coagulation and angiogenesis pathways,
we only see these down-regulated.
```{r alldonors_camera_enrichment_H_by_geneset_by_dir, fig.height=10, fig.width=16}
df_camera_sig_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC") %>%
group_by(geneset, Direction) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>%
summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
ggplot(aes(y = n_donors, x = reorder(geneset, n_donors, max),
colour = Direction)) +
geom_point(alpha = 0.7, size = 4, position = position_dodge(width = 0.5)) +
ggthemes::scale_colour_tableau() +
coord_flip() +
theme_bw(16) +
xlab("Gene set") + ylab("Number of lines significant") +
ggtitle("Camera MSigDB Hallmark gene set enrichment")
```
## Heatmap of results for camera Hallmark geneset testing
We can get an overview of all the Hallmark gene set results by producing a
heatmap, first showing just the significant (FDR < 5%) results across all
donors and pairwise contrasts of clones.
```{r top_genesets_H_direction_heatmap, fig.height=7, fig.width=12}
repeated_sig_H_genesets <- df_camera_sig_unst %>%
dplyr::filter(collection == "H", stat == "logFC") %>%
group_by(geneset) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% dplyr::arrange(n_donors) %>%
dplyr::filter(n_donors > 0.5)
repeated_sig_H_genesets_vec <- unique(repeated_sig_H_genesets[["geneset"]])
repeated_sig_H_genesets_vec <- gsub("_", " ", gsub("HALLMARK_", "",
repeated_sig_H_genesets_vec))
df_4_heatmap <- df_camera_sig_unst %>%
dplyr::filter(collection == "H", stat == "logFC") %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
dplyr::mutate(geneset =
factor(geneset, levels = repeated_sig_H_genesets_vec)) %>%
dplyr::filter(geneset %in% repeated_sig_H_genesets_vec) %>%
dplyr::mutate(id = paste0(donor, ": ", contrast))
div_lines <- gsub(": c.*", "",
sort(unique(paste0(df_4_heatmap[["donor"]], ": ",
df_4_heatmap[["contrast"]])))) %>% table %>% cumsum + 0.5
df_4_heatmap %>%
ggplot(aes(x = id, y = geneset, fill = Direction)) +
geom_tile() +
geom_vline(xintercept = div_lines, colour = "gray70") +
scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap.png", height = 6, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap.pdf", height = 6, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap.svg", height = 6, width = 12)
```
We can do the same for all results for the gene sets that are significantly
enriched in at least two donors.
```{r top_genesets_H_direction_heatmap_all_contrasts, fig.height=9, fig.width=18}
df_camera_all_unst %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
dplyr::mutate(geneset =
factor(geneset, levels = repeated_sig_H_genesets_vec)) %>%
dplyr::filter(geneset %in% repeated_sig_H_genesets_vec) %>%
dplyr::mutate(id = paste0(donor, ": ", contrast)) ->
df_4_heatmap_all
df_4_heatmap_all <- dplyr::mutate(
df_4_heatmap_all,
minlog10P = cut(-log10(PValue), breaks = c(0, 1, 2, 3, 4, 5, 30)))
div_lines_all <- gsub(": c.*", "",
sort(unique(paste0(df_4_heatmap_all[["donor"]], ": ",
df_4_heatmap_all[["contrast"]])))) %>% table %>% cumsum + 0.5
pp <- df_4_heatmap_all %>%
ggplot(aes(x = id, y = geneset, fill = Direction, alpha = minlog10P)) +
geom_tile() +
geom_point(alpha = 1, data = df_4_heatmap, pch = 19, size = 0.5, show.legend = FALSE) +
geom_vline(xintercept = div_lines_all, colour = "gray70") +
scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
scale_alpha_discrete(name = "-log10(P)") +
ylab("Gene set") +
xlab("Line and clone comparison") +
theme(axis.text.x = element_text(angle = 60, hjust = 1),
legend.position = "right")
pp
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts.png", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts.pdf", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts.svg", height = 9, width = 20)
```
We can also add a panel to this figure showing the number of donors in which
each of these gene sets is significantly enriched.
```{r top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors, fig.height=9, fig.width=20}
## Hallmark geneset
pp_nsig <- df_camera_sig_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC") %>%
group_by(geneset) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
ggplot(aes(y = n_donors, x = reorder(geneset, n_donors, max))) +
geom_hline(yintercept = 0, colour = "gray50") +
geom_segment(aes(xend = reorder(geneset, n_donors, max), yend = 0),
colour = "gray50") +
geom_point(size = 4, colour = "gray30", alpha = 1) +
ggthemes::scale_colour_tableau() +
coord_flip() +
xlab("Gene set") + ylab("Number of lines significant") +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank())
prow <- plot_grid(pp + theme(legend.position = "none"),
pp_nsig, align = 'h', rel_widths = c(7, 1))
lgnd <- get_legend(pp)
plot_grid(prow, lgnd, rel_widths = c(3, .3))
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors.png", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors.pdf", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors.svg", height = 9, width = 20)
```
However, the plot above is very complicated, so we may want to focus just on the
lines for which there are multiple clones that show differing behaviour amongst
each other. To simplify, let us just look at 12 donors that have significant
geneset enrichment for at least 2 contrasts and just look at the 9 gene sets
that are significant in at least three lines.
```{r top_genesets_H_direction_heatmap_all_contrasts, fig.height=9, fig.width=18}
repeated_sig_H_genesets3 <- df_camera_sig_unst %>%
dplyr::filter(collection == "H", stat == "logFC") %>%
group_by(geneset) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% dplyr::arrange(n_donors) %>%
dplyr::filter(n_donors > 2.5)
repeated_sig_H_genesets_vec3 <- unique(repeated_sig_H_genesets3[["geneset"]])
repeated_sig_H_genesets_vec3 <- gsub("_", " ", gsub("HALLMARK_", "",
repeated_sig_H_genesets_vec3))
selected_donors <- c("fawm", "fikt", "hipn", "ieki", "laey", "lexy", "qayj",
"qonc", "rozh", "ualf", "wahn", "zoxy")
df_4_heatmap_filt <- df_camera_all_unst %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
dplyr::mutate(geneset =
factor(geneset, levels = repeated_sig_H_genesets_vec3)) %>%
dplyr::filter(geneset %in% repeated_sig_H_genesets_vec3,
donor %in% selected_donors) %>%
dplyr::mutate(id = paste0(donor, ": ", contrast))
df_4_heatmap_filt_sig <- df_camera_sig_unst %>%
dplyr::filter(collection == "H", stat == "logFC") %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
dplyr::mutate(geneset =
factor(geneset, levels = repeated_sig_H_genesets_vec3)) %>%
dplyr::filter(geneset %in% repeated_sig_H_genesets_vec3,
donor %in% selected_donors) %>%
dplyr::mutate(id = paste0(donor, ": ", contrast))
df_4_heatmap_filt <- dplyr::mutate(
df_4_heatmap_filt,
minlog10P = cut(-log10(PValue), breaks = c(0, 1, 2, 3, 4, 5, 30)))
div_lines_filt <- gsub(": c.*", "",
sort(unique(paste0(df_4_heatmap_filt[["donor"]], ": ",
df_4_heatmap_filt[["contrast"]])))) %>%
table %>% cumsum + 0.5
pp_filt <- df_4_heatmap_filt %>%
ggplot(aes(x = id, y = geneset, fill = Direction, alpha = minlog10P)) +
geom_tile() +
geom_point(alpha = 1, data = df_4_heatmap_filt_sig, pch = 19, size = 0.5,
show.legend = FALSE) +
geom_vline(xintercept = div_lines_filt, colour = "gray70") +
scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
scale_alpha_discrete(name = "-log10(P)") +
ylab("Gene set") +
xlab("Line and clone comparison") +
theme(axis.text.x = element_text(angle = 60, hjust = 1),
legend.position = "right")
pp_filt
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_filt_contrasts.png", plot = pp_filt, height = 5, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_filt_contrasts.pdf", plot = pp_filt, height = 5, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_filt_contrasts.svg", plot = pp_filt, height = 5, width = 12)
```
## Correlation of gene set results and genes contained
Let's look at the correlation between gene set results (Spearman correlation of
signed -log10(P-values) from _camera_ tests) and compare to the proportion of
genes overlapping between pairs of gene sets.
```{r corr-maps, fig.height=9, fig.width=13}
repeated_sig_H_genesets_vec2 <- paste0("HALLMARK_",
gsub(" ", "_", repeated_sig_H_genesets_vec))
## all results
df_H_pvals <- df_camera_all_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC", geneset %in% repeated_sig_H_genesets_vec2) %>%
dplyr::mutate(donor_coeff = paste(donor, coeff, sep = "."),
sign = ifelse(Direction == "Down", -1, 1),
signed_P = sign * -log10(PValue)) %>%
dplyr::select(geneset, donor_coeff, signed_P) %>%
tidyr::spread(key = donor_coeff, value = signed_P)
mat_H_pvals <- as.matrix(df_H_pvals[, -1])
rownames(mat_H_pvals) <- gsub("_", " ", gsub("HALLMARK_", "", df_H_pvals[[1]]))
cor_H_pvals <- cor(t(mat_H_pvals), method = "spearman")
p.mat <- cor_pmat(t(mat_H_pvals))
ggcorrplot(cor_H_pvals, hc.order = TRUE, p.mat = p.mat, insig = "blank") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
hclust_cor <- hclust(as.dist(1 - cor_H_pvals))
corrplot1 <- ggcorrplot(cor_H_pvals[hclust_cor$order, hclust_cor$order],
p.mat = p.mat[hclust_cor$order, hclust_cor$order], insig = "blank") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
mat_H_gene_overlap <- matrix(nrow = nrow(cor_H_pvals), ncol = ncol(cor_H_pvals),
dimnames = dimnames(cor_H_pvals))
for (i in seq_along(repeated_sig_H_genesets_vec2)) {
for (j in seq_along(repeated_sig_H_genesets_vec2)) {
gs1 <- paste0("HALLMARK_", gsub(" ", "_", rownames(mat_H_gene_overlap)[i]))
gs2 <- paste0("HALLMARK_", gsub(" ", "_", rownames(mat_H_gene_overlap)[j]))
mat_H_gene_overlap[i, j] <- mean(Hs.H[[gs1]] %in% Hs.H[[gs2]])
}
}
corrplot2 <- ggcorrplot(mat_H_gene_overlap[hclust_cor$order, hclust_cor$order]) +
scale_fill_gradient(name = "Gene set\noverlap", low = "white", high = "black") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
corrplot2
corrplot3 <- corrplot2 + theme(axis.text.y = element_blank())
```
```{r corr-plot-combined, fig.height=9, fig.width=20}
plot_grid(corrplot1 + theme(plot.margin = unit(c(0,0,0,0), "cm")),
corrplot3 + theme(plot.margin = unit(c(0,0,0,0), "cm")),
align = "h", axis = "b", rel_widths = c(0.58, 0.42))
ggsave("figures/differential_expression/top_genesets_H_corrplots.png", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_corrplots.pdf", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_corrplots.svg", height = 9, width = 20)
```
Plot gene set correlation with the number of donors in which each gene set is
significant.
```{r, fig.height=7, fig.width=12}
pp_nsig <- df_camera_sig_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC") %>%
group_by(geneset) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
dplyr::mutate(geneset = factor(
geneset, levels = rownames(mat_H_gene_overlap)[hclust_cor$order])) %>%
ggplot(aes(y = n_donors, x = geneset)) +
geom_hline(yintercept = 0, colour = "gray50") +
geom_segment(aes(xend = geneset, yend = 0),
colour = "gray50") +
geom_point(size = 4, colour = "gray30", alpha = 1) +
ggthemes::scale_colour_tableau() +
coord_flip() +
xlab("Gene set") + ylab("Number of lines\nsignificant") +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank())
ggdraw() +
draw_plot(corrplot1 + theme(legend.position = "top"),
x = 0, y = 0, width = 0.8, scale = 1) +
draw_plot(pp_nsig,
x = 0.685, y = 0.25, width = 0.25, height = 0.6445)
ggsave("figures/differential_expression/top_genesets_H_corrplot_with_nsig_donor.png",
height = 7, width = 12)
ggsave("figures/differential_expression/top_genesets_H_corrplot_with_nsig_donor.pdf",
height = 7, width = 12)
```
## Linking DE to selection
```{r}
df_donor_info <- read.table("data/donor_info_070818.txt")
df_donor_info <- as_data_frame(df_donor_info)
df_donor_info$donor <- df_donor_info$donor_short
df_ncells_de <- assignments %>% dplyr::filter(assigned != "unassigned",
donor_short_id %in% names(de_res$qlf_list)) %>%
group_by(donor_short_id) %>%
summarise(n_cells = n())
colnames(df_ncells_de)[1] <- "donor"
df_prop_assigned <- assignments %>%
dplyr::filter(donor_short_id %in% names(de_res$qlf_list)) %>%
group_by(donor_short_id) %>%
summarise(prop_assigned = mean(assigned != "unassigned"))
colnames(df_prop_assigned)[1] <- "donor"
df_nvars_by_cat <- readr::read_tsv("output/nvars_by_category_by_donor.tsv")
df_nvars_by_cat_wd <- tidyr::spread(
df_nvars_by_cat[, 1:3], consequence, n_vars_all_genes)
df_nvars_by_cat_wd <- left_join(
summarise(group_by(df_nvars_by_cat, donor), nvars_all = sum(n_vars_all_genes)),
df_nvars_by_cat_wd
)
df_nvars_by_cat_wd <- df_nvars_by_cat %>%
dplyr::filter(consequence %in% c("missense", "splicing", "nonsense")) %>%
group_by(donor) %>%
summarise(nvars_misnonspli = sum(n_vars_all_genes)) %>%
left_join(., df_nvars_by_cat_wd)
df_donor_info <- left_join(df_ncells_de, df_donor_info)
df_donor_info <- left_join(df_prop_assigned, df_donor_info)
df_donor_info <- left_join(df_donor_n_de, df_donor_info)
df_donor_info$n_de_genes <- df_donor_info$count
df_donor_info <- left_join(df_donor_info, df_nvars_by_cat_wd)
nbglm_nde <- MASS::glm.nb(n_de_genes ~ n_cells, data = df_donor_info)
df_nbglm_nde <- broom::augment(nbglm_nde) %>%
left_join(df_donor_info)
## n_de vs n_cells
df_nbglm_nde %>%
dplyr::mutate(selection = factor(
selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = n_cells, y = n_de_genes, fill = selection)) +
geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
geom_point(size = 3, shape = 21) +
ylab("Number of DE genes") +
xlab("Number of cells") +
scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))
ggsave("figures/differential_expression/n_de_genes_vs_n_cells.png",
height = 5.5, width = 5.5)
## selection, n_de resid boxplot
df_nbglm_nde %>%
dplyr::mutate(selection = factor(
selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = selection, y = .resid)) +
geom_violin(aes(fill = selection), alpha = 0.7) +
geom_boxplot(outlier.alpha = 0, width = 0.2) +
ggbeeswarm::geom_quasirandom(aes(fill = selection), size = 3, shape = 21) +
ylab("Number of DE genes (residual from NB GLM)") +
xlab("Inferred selection status") +
scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
coord_flip()
ggsave("figures/differential_expression/n_de_resid_selection_boxplot.png",
height = 4.5, width = 6.5)
summary(lm(.resid ~ selection, data = df_nbglm_nde))
## selection, n_de (sqrt scale) boxplot
df_nbglm_nde %>%
dplyr::mutate(selection = factor(
selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = selection, y = n_de_genes)) +
geom_violin(aes(fill = selection), alpha = 0.7) +
geom_boxplot(outlier.alpha = 0, width = 0.2) +
ggbeeswarm::geom_quasirandom(aes(fill = selection), size = 3, shape = 21) +
ylab("Number of DE genes") +
xlab("Inferred selection status") +
scale_y_sqrt(breaks = c(0, 100, 500, 1000, 1500, 2000, 2500)) +
scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
coord_flip()
ggsave("figures/differential_expression/n_de_sqrt_selection_boxplot.png",
height = 5.5, width = 6.5)
## n_de (resids) vs goodness of fit cumul. mutation model
df_nbglm_nde %>%
dplyr::mutate(selection = factor(
selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = rsq_ntrtestr, y = .resid, fill = selection)) +
geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
geom_point(size = 3, shape = 21) +
ylab("Number of DE genes (residual from NB GLM)") +
xlab("Goodness of fit: cumulative mutations") +
scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))
ggsave("figures/differential_expression/n_de_resid_selection_vs_gof_cumul_mut_model.png",
height = 6.5, width = 5.5)
## n_de (sqrt scale) vs goodness of fit cumul. mutation model
df_nbglm_nde %>%
dplyr::mutate(selection = factor(
selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = rsq_ntrtestr, y = n_de_genes, fill = selection)) +
geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
geom_point(size = 3, shape = 21) +
ylab("Number of DE genes") +
xlab("Goodness of fit: cumulative mutations") +
scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))
ggsave("figures/differential_expression/n_de_sqrt_selection_vs_gof_cumul_mut_model.png",
height = 5.5, width = 6.5)
## n_de (resids) vs goodness of fit NB model