Exploring simple somatic mutations

library(GAMBLR.open)
suppressMessages(library(tidyverse))

In general, the experimental data available through GAMBLR.open is obtained using one of the get_ family of functions. These require that you specify which samples you want data from, which you accomplish by providing a metadata table that has been subset to just the samples you require. The metadata for the full set of samples available in GAMBLR.data can be obtained using get_gambl_metadata. You can subset this table using dplyr::filter. Here, we’ll focus on all DLBCL and FL samples. In many of the examples for GAMBLR.open and other packages in the GAMBLR family you will see check_and_clean_metadata. This is currently required due to the existence of near duplicate rows in the metadata. These duplicated rows exist because some samples were part of more than one study and each row refers to one of those studies. A call to check_and_clean_metadata as in this example will remove the duplicated rows, which ensures your analyses will not include duplicated data.

my_meta <- get_gambl_metadata(seq_type_filter = c("genome","capture")) %>%
  dplyr::filter(
    pathology %in% c("FL", "DLBCL")
  ) 
#How many rows for each pathology and seq_type?
group_by(my_meta, seq_type, pathology) %>% 
  count() %>% kableExtra::kable(format="html")

seq_type	pathology	n
capture	DLBCL	1783
genome	DLBCL	534
genome	FL	219

my_meta = check_and_clean_metadata(my_meta,duplicate_action = "keep_first")

#How many rows remain?
group_by(my_meta, seq_type, pathology) %>% 
  count() %>% kableExtra::kable(format="html")

seq_type	pathology	n
capture	DLBCL	1783
genome	DLBCL	529
genome	FL	219

length(unique(my_meta$sample_id))

[1] 2531

nrow(my_meta)

[1] 2531

This shows that the rows in our metadata represent unique samples so we can proceed. Retrieving simple somatic mutations (SSMs) in a MAF-like format can be done a variety of ways. If your analysis is focusing on protein-coding alterations, then get_coding_ssm should meet your needs.

# retrieve MAF for all exome (capture) samples
capture_coding <- get_coding_ssm(
  these_samples_metadata = my_meta,
  projection = "grch37",
  include_silent = TRUE,
  this_seq_type = "capture"
)

nrow(capture_coding)

[1] 29515

# retrieve MAF for all genome samples
genome_coding <- get_coding_ssm(
  these_samples_metadata = my_meta,
  projection = "grch37",
  include_silent = TRUE,
  this_seq_type = "genome"
)

num_genome_coding_rows = nrow(genome_coding)
genome_coding_sample = unique(genome_coding$Tumor_Sample_Barcode)
num_genome_coding_sample = length(genome_coding_sample)

A total of 10971 mutations in coding regions from 588 samples were retrieved with get_coding_ssm.

To access additional mutations in non-coding regions, you can use get_ssm_by_samples if you desire all available mutations or get_ssm_by_regions if you want more control over which regions the mutations correspond to.

Note

GAMBLR.data, and therefore GAMBLR.open does not contain genome-wide mutations from very many samples due to data sharing restrictions. Instead, for most samples the only non-coding mutations included are those within the regions commonly affected by aberrant somatic hypermutation (aSHM).

#retrieve genome-wide mutations for all genomes
genome_all = get_ssm_by_samples(these_samples_metadata = my_meta,
                                this_seq_type="genome")

num_genome_all_rows = nrow(genome_all)
genome_all_sample = unique(genome_all$Tumor_Sample_Barcode)
num_genome_all_sample = length(genome_all_sample)

A total of 270867 genome-wide mutations from 594 samples were retrieved with get_ssm_by_samples.

These two approaches give us mutations from a different number of samples. We can delve into this a bit by focusing on the differences.

genome_all_only = genome_all_sample[!genome_all_sample %in% genome_coding_sample]
g_u = length(genome_all_only)

There are 6 sample_id that have mutations in the genome-wide result but not in coding space.

filter(my_meta, sample_id %in% genome_all_only) %>% 
    dplyr::select(sample_id,cohort,pairing_status, pathology, patient_id, study) %>%
    kableExtra::kable(format="html")

sample_id	cohort	pairing_status	pathology	patient_id	study
05-24561T	DLBCL_Marra	matched	DLBCL	05-24561	FL_Dreval
14-13938T	FL_GenomeCanada	matched	FL	14-13938	FL_Dreval
14-33798_tumorA	DLBCL_LSARP_Trios	matched	DLBCL	14-33798	DLBCL_Hilton
FL2004T1	FL_Kridel	matched	FL	06-25647	FL_Dreval
HTMCP-01-07-00336-01A-01E	DLBCL_HTMCP	matched	DLBCL	HTMCP-01-07-00336	DLBCL_Thomas
SP59300	DLBCL_ICGC	matched	DLBCL	DO27777	FL_Dreval

Since the non-coding mutations we get from get_ssm_by_samples are restricted to known B-cell lymphoma genes and regions affected by aSHM, we should be able to obtain most of these with a call to get_ssm_by_regions as long as the regions we request include all aSHM sites.

ashm_genome_maf = get_ssm_by_regions(these_samples_metadata = my_meta,
                              this_seq_type = "genome",
                              streamlined = F)
coding_counted = group_by(genome_coding,Tumor_Sample_Barcode) %>%
  summarise(coding=n())
ashm_counted = group_by(ashm_genome_maf,Tumor_Sample_Barcode) %>%
    summarise(ashm=n())

genome_all_counted = group_by(genome_all,Tumor_Sample_Barcode) %>%
    summarise(all=n())

count_compare = left_join(genome_all_counted,ashm_counted)
count_compare = left_join(count_compare,coding_counted) %>%
  arrange(desc(all))

count_compare = left_join(count_compare,
                          select(my_meta,Tumor_Sample_Barcode,cohort))

count_compare %>% kableExtra::kable(format="html")

Tumor_Sample_Barcode	all	ashm	coding	cohort
SU-DHL-4	32824	156	363	DLBCL_cell_lines
OCI-Ly3	31532	117	353	DLBCL_cell_lines
OCI-Ly10	30051	126	376	DLBCL_cell_lines
SU-DHL-10	26855	57	290	DLBCL_cell_lines
DOHH-2	22089	48	234	DLBCL_cell_lines
HTMCP-01-06-00206-01A-01D	2832	194	73	DLBCL_HTMCP
HTMCP-01-06-00105-01A-01D	2511	97	65	DLBCL_HTMCP
SP192997	1901	87	49	DLBCL_ICGC
SP116697	1576	419	94	DLBCL_ICGC
13-26835_tumorA	1145	698	120	DLBCL_LSARP_Trios
01-16433_tumorB	1051	92	36	DLBCL_LSARP_Trios
SP193546	985	657	49	DLBCL_ICGC
16-16192T	926	351	46	DLBCL_GenomeCanada
HTMCP-01-06-00611-01A-01D	806	512	86	DLBCL_HTMCP
13-38657_tumorB	767	489	77	DLBCL_LSARP_Trios
SP193375	733	381	24	DLBCL_ICGC
13-38657_tumorA	720	458	67	DLBCL_LSARP_Trios
FL1019T1	712	524	45	FL_Kridel
10-31625T	694	219	27	DLBCL_GenomeCanada
HTMCP-01-06-00594-01A-01D	694	409	102	DLBCL_HTMCP
09-37629T	692	260	42	DLBCL_GenomeCanada
06-11677_tumorA	688	420	61	DLBCL_LSARP_Trios
00-14595_tumorC	679	348	50	DLBCL_LSARP_Trios
00-14595_tumorD	679	367	40	DLBCL_LSARP_Trios
07-35482T	678	54	13	DLBCL_Marra
13-26835_tumorD	668	331	67	DLBCL_LSARP_Trios
SP116668	666	194	46	DLBCL_ICGC
HTMCP-01-10-00160-01A-01D	643	185	35	DLBCL_HTMCP
SP116670	639	414	70	DLBCL_ICGC
13-26835_tumorB	614	320	61	DLBCL_LSARP_Trios
SP124969	591	344	44	DLBCL_ICGC
SP59304	582	208	39	DLBCL_ICGC
HTMCP-01-06-00146-01A-01D	580	236	30	DLBCL_HTMCP
HTMCP-01-06-00175-01A-01D	577	285	34	DLBCL_HTMCP
SP116676	571	258	30	DLBCL_ICGC
HTMCP-01-15-00370-01A-01E	559	144	27	DLBCL_HTMCP
17-40409_tumorB	557	252	31	DLBCL_LSARP_Trios
17-40409_tumorA	555	252	30	DLBCL_LSARP_Trios
16-11636T	554	257	56	DLBCL_GenomeCanada
HTMCP-01-06-00306-01A-01D	546	329	59	DLBCL_HTMCP
SP59452	539	295	38	DLBCL_ICGC
SP59448	525	236	30	DLBCL_ICGC
02-28397_tumorA	524	296	41	DLBCL_LSARP_Trios
SP59368	509	275	25	DLBCL_ICGC
14-25466T	490	203	40	DLBCL_GenomeCanada
11-13204_tumorB	488	107	38	DLBCL_LSARP_Trios
11-13204_tumorA	475	107	27	DLBCL_LSARP_Trios
03-23488_tumorA	467	258	35	DLBCL_LSARP_Trios
HTMCP-01-06-00497-01A-01D	467	208	21	DLBCL_HTMCP
SP116610	461	270	31	DLBCL_ICGC
09-12737T	456	207	12	DLBCL_Marra
SP192993	455	246	26	DLBCL_ICGC
HTMCP-01-20-00272-01A-01E	453	175	15	DLBCL_HTMCP
03-33266_tumorB	450	281	47	DLBCL_LSARP_Trios
07-41887_tumorA	441	271	39	DLBCL_LSARP_Trios
16-23208T	439	206	22	DLBCL_GenomeCanada
13-30451T	437	180	24	DLBCL_GenomeCanada
16-18029T	437	210	59	DLBCL_GenomeCanada
FL1019T2	437	289	30	FL_Kridel
99-27137T	429	121	19	DLBCL_Marra
14-35026T	428	104	23	DLBCL_GenomeCanada
06-14634T	417	169	12	DLBCL_Marra
11-21727T	415	122	23	DLBCL_Gascoyne
05-15635_tumorA	414	234	52	DLBCL_LSARP_Trios
14-41461T	411	227	25	DLBCL_GenomeCanada
15-16885T	404	184	20	FL_GenomeCanada
SP192765	403	206	37	DLBCL_ICGC
HTMCP-01-06-00307-01A-01D	400	119	26	DLBCL_HTMCP
13-40370T	394	231	22	FL_GenomeCanada
15-21654T	392	191	18	DLBCL_GenomeCanada
05-17793T	391	127	29	DLBCL_Gascoyne
07-41887_tumorB	391	228	35	DLBCL_LSARP_Trios
FL3020T1	389	182	25	FL_Kridel
HTMCP-01-06-00634-01A-01D	389	201	43	DLBCL_HTMCP
09-16981T	387	80	29	DLBCL_Gascoyne
FL1003T2	386	94	11	FL_Kridel
09-15842_tumorB	382	148	27	DLBCL_LSARP_Trios
POG707T	382	173	16	POG
14-32442T	378	215	32	DLBCL_GenomeCanada
SP193816	377	255	33	FL_ICGC
07-32561_tumorB	375	93	21	DLBCL_LSARP_Trios
01-14774_tumorA	374	138	33	DLBCL_LSARP_Trios
09-31008_tumorA	373	226	30	DLBCL_LSARP_Trios
07-31833T	369	184	31	DLBCL_Gascoyne
14-27873T	369	206	46	DLBCL_GenomeCanada
06-11677_tumorB	365	238	22	DLBCL_LSARP_Trios
HTMCP-01-15-00366-01A-01E	365	209	32	DLBCL_HTMCP
08-17645_tumorB	364	196	35	DLBCL_LSARP_Trios
LY_RELY_128_tumorA	363	181	28	DLBCL_LSARP_Trios
09-15842_tumorA	361	145	25	DLBCL_LSARP_Trios
SP193967	361	192	35	DLBCL_ICGC
16-27074_tumorB	357	135	24	DLBCL_LSARP_Trios
09-12864T	354	75	20	DLBCL_Gascoyne
16-27074_tumorA	353	133	23	DLBCL_LSARP_Trios
SP116690	352	109	21	DLBCL_ICGC
16-16723T	350	130	20	DLBCL_GenomeCanada
SP193229	349	190	13	FL_ICGC
SP192970	348	119	19	DLBCL_ICGC
15-43657T	346	130	22	DLBCL_GenomeCanada
09-31601_tumorA	342	149	21	DLBCL_LSARP_Trios
15-11617T	338	110	28	DLBCL_GenomeCanada
15-36675T	337	170	35	FL_GenomeCanada
SP124975	335	218	43	DLBCL_ICGC
10-36955_tumorA	333	62	23	DLBCL_LSARP_Trios
10-36955_tumorB	332	58	19	DLBCL_LSARP_Trios
09-31008_tumorB	329	209	24	DLBCL_LSARP_Trios
SP193934	329	109	18	DLBCL_ICGC
HTMCP-01-06-00036-01E	327	174	36	DLBCL_HTMCP
HTMCP-01-06-00136-01A-01D	327	181	23	DLBCL_HTMCP
HTMCP-01-06-00563-01A-01D	326	189	30	DLBCL_HTMCP
03-23488_tumorB	318	171	27	DLBCL_LSARP_Trios
09-33003_tumorB	318	124	32	DLBCL_LSARP_Trios
14-37722T	317	145	28	DLBCL_GenomeCanada
HTMCP-01-06-00253-01A-01D	317	206	28	DLBCL_HTMCP
SP124957	317	181	23	DLBCL_ICGC
04-24937T	313	160	30	DLBCL_GenomeCanada
09-21480T	311	107	14	DLBCL_Gascoyne
16-32248_tumorB	311	114	22	DLBCL_LSARP_Trios
08-17645_tumorA	309	186	28	DLBCL_LSARP_Trios
19-16466_tumorA	306	165	14	DLBCL_LSARP_Trios
FL1003T1	306	69	10	FL_Kridel
SP124979	306	127	15	FL_ICGC
06-23907T	305	55	11	DLBCL_Marra
15-41277T	304	162	24	FL_GenomeCanada
HTMCP-01-10-00778-01A-01D	304	181	30	DLBCL_HTMCP
SP193025	302	137	15	DLBCL_ICGC
SP192815	300	115	19	DLBCL_ICGC
09-31601_tumorB	298	84	24	DLBCL_LSARP_Trios
FL1010T2	297	118	27	FL_Kridel
HTMCP-01-06-00314-01A-01D	295	65	16	DLBCL_HTMCP
SP124971	294	101	19	DLBCL_ICGC
SP192856	294	81	31	DLBCL_ICGC
SP192988	291	163	17	FL_ICGC
03-33266_tumorA	289	121	16	DLBCL_LSARP_Trios
05-21634T	289	139	25	DLBCL_Gascoyne
10-39294_tumorB	288	74	25	DLBCL_LSARP_Trios
FL3011T1	285	155	19	FL_Kridel
SP116645	285	121	24	FL_ICGC
LY_RELY_128_tumorB	284	125	17	DLBCL_LSARP_Trios
17-36275T	283	81	20	DLBCL_GenomeCanada
06-19919T	282	98	9	DLBCL_Marra
FL1002T2	280	125	18	FL_Kridel
09-41082T	278	75	2	DLBCL_Marra
02-15745_tumorB	276	86	7	DLBCL_LSARP_Trios
05-18426T	276	119	15	DLBCL_Gascoyne
SP116659	276	124	11	DLBCL_ICGC
SP192767	276	69	21	DLBCL_ICGC
SP116657	274	59	19	DLBCL_ICGC
05-15635_tumorB	272	124	15	DLBCL_LSARP_Trios
FL2002T1	270	168	26	FL_Kridel
LY_RELY_028_tumorB	270	142	27	DLBCL_LSARP_Trios
01-14774_tumorB	267	83	21	DLBCL_LSARP_Trios
SP193420	267	191	11	DLBCL_ICGC
14-20552_tumorB	266	132	27	DLBCL_LSARP_Trios
15-38154T	266	69	15	DLBCL_GenomeCanada
15-26538T	263	52	16	DLBCL_GenomeCanada
HTMCP-01-06-00185-01A-01D	262	120	25	DLBCL_HTMCP
SP116683	262	138	7	FL_ICGC
02-15745_tumorD	261	66	21	DLBCL_LSARP_Trios
10-39294_tumorA	260	60	20	DLBCL_LSARP_Trios
18-19313_tumorB	259	122	23	DLBCL_LSARP_Trios
15-31924T	257	85	28	DLBCL_GenomeCanada
SP59400	256	77	16	DLBCL_ICGC
08-29440_tumorB	255	127	28	DLBCL_LSARP_Trios
18-19313_tumorA	254	120	23	DLBCL_LSARP_Trios
95-32141_tumorA	254	84	22	DLBCL_LSARP_Trios
SP59348	254	154	16	FL_ICGC
08-15460T	253	127	40	DLBCL_Gascoyne
HTMCP-01-06-00242-01A-01D	252	147	30	DLBCL_HTMCP
SP116648	252	59	18	DLBCL_ICGC
SP193766	252	112	16	MALY_Other_ICGC
08-15460_tumorB	251	125	20	DLBCL_LSARP_Trios
14-35632T	251	99	15	DLBCL_GenomeCanada
02-15745_tumorC	248	71	20	DLBCL_LSARP_Trios
SP59456	248	133	26	DLBCL_ICGC
17-36275_tumorB	243	65	11	DLBCL_LSARP_Trios
SP192882	242	75	17	FL_ICGC
SP124959	241	108	9	DLBCL_ICGC
SP192811	241	141	15	FL_ICGC
08-29440_tumorA	240	130	28	DLBCL_LSARP_Trios
15-34472T	240	77	23	DLBCL_GenomeCanada
97-18502_tumorB	240	53	15	DLBCL_LSARP_Trios
FL1018T2	240	146	15	FL_Kridel
SP192800	239	107	17	DLBCL_ICGC
SP194228	238	129	13	DLBCL_ICGC
16-43741_tumorA	237	120	18	DLBCL_LSARP_Trios
FL1002T1	237	101	13	FL_Kridel
LY_RELY_116_tumorA	237	90	20	DLBCL_LSARP_Trios
06-24255_tumorD	236	103	23	DLBCL_LSARP_Trios
06-15256T	235	100	7	DLBCL_Marra
15-30123T	235	71	14	FL_GenomeCanada
16-27413T	235	120	17	DLBCL_GenomeCanada
SP116663	235	77	18	DLBCL_ICGC
10-36955_tumorD	234	62	17	DLBCL_LSARP_Trios
14-24534_tumorB	234	56	16	DLBCL_LSARP_Trios
09-33003T	232	71	38	DLBCL_Marra
14-20962T	232	114	14	DLBCL_GenomeCanada
04-38964T	230	97	16	FL_GenomeCanada
SP124973	229	117	18	DLBCL_ICGC
05-32947T	227	20	8	DLBCL_Marra
HTMCP-01-06-00299-01A-01D	227	123	18	DLBCL_HTMCP
SP59412	226	58	13	DLBCL_ICGC
SP193005	225	87	17	DLBCL_ICGC
FL1020T1	224	96	33	FL_Kridel
SP193512	224	94	17	DLBCL_ICGC
FL1011T1	223	26	9	FL_Kridel
SP194195	223	124	24	DLBCL_ICGC
02-24492_tumorA	222	127	17	DLBCL_LSARP_Trios
07-40648_tumorA	219	81	21	DLBCL_LSARP_Trios
FL1018T1	218	140	14	FL_Kridel
FL3003T1	216	71	14	FL_Kridel
SP193725	215	88	21	DLBCL_ICGC
SP194143	215	77	11	DLBCL_ICGC
99-13280T	214	46	23	DLBCL_Gascoyne
FL1020T2	214	94	31	FL_Kridel
SP116649	214	14	8	FL_ICGC
15-18916T	213	91	19	FL_GenomeCanada
HTMCP-01-16-00265-01A-01E	212	125	18	DLBCL_HTMCP
14-33436T	211	114	17	DLBCL_GenomeCanada
HTMCP-01-01-00451-01A-01D	211	66	20	DLBCL_HTMCP
15-24306T	210	91	18	DLBCL_GenomeCanada
00-15201_tumorA	208	83	18	DLBCL_LSARP_Trios
07-40648_tumorB	207	85	22	DLBCL_LSARP_Trios
05-25674T	206	90	7	DLBCL_Marra
10-10826T	204	110	9	DLBCL_GenomeCanada
SP193976	203	96	29	DLBCL_ICGC
HTMCP-01-06-00443-01A-01D	202	58	17	DLBCL_HTMCP
SP193017	202	28	13	FL_ICGC
08-19764T	201	51	10	DLBCL_Gascoyne
92-38267_tumorB	201	76	24	DLBCL_LSARP_Trios
SP116706	201	81	21	FL_ICGC
06-24255_tumorC	196	89	16	DLBCL_LSARP_Trios
04-14093_tumorB	195	38	6	DLBCL_LSARP_Trios
FL1017T2	195	82	25	FL_Kridel
04-14093_tumorA	193	36	6	DLBCL_LSARP_Trios
04-21856_tumorB	193	95	19	DLBCL_LSARP_Trios
11-34915T	193	128	14	FL_GenomeCanada
15-13383_tumorB	193	88	13	DLBCL_LSARP_Trios
14-20552_tumorA	192	94	22	DLBCL_LSARP_Trios
14-38639T	192	123	22	FL_GenomeCanada
15-43891T	192	87	15	DLBCL_GenomeCanada
95-32141_tumorB	192	68	16	DLBCL_LSARP_Trios
SP116726	191	59	17	DLBCL_ICGC
SP192833	191	55	8	DLBCL_ICGC
06-30025T	190	35	11	DLBCL_Marra
10-36955_tumorC	190	58	13	DLBCL_LSARP_Trios
01-16433_tumorC	189	82	14	DLBCL_LSARP_Trios
14-29443_tumorB	189	47	11	DLBCL_LSARP_Trios
FL1004T2	189	68	22	FL_Kridel
HTMCP-01-06-00419-01B-01D	189	107	19	DLBCL_HTMCP
13-26601T	187	46	10	DLBCL_GenomeCanada
14-29443_tumorA	187	60	8	DLBCL_LSARP_Trios
FL1004T1	186	80	23	FL_Kridel
16-20119T	184	122	17	FL_GenomeCanada
11-35935T	183	42	5	DLBCL_GenomeCanada
17-45529_tumorB	183	30	16	DLBCL_LSARP_Trios
SP59352	183	71	14	FL_ICGC
96-11779T	182	69	13	FL_GenomeCanada
FL3001T1	180	82	9	FL_Kridel
17-23504T	179	70	12	DLBCL_GenomeCanada
15-39521T	178	96	18	FL_GenomeCanada
02-28397_tumorB	177	56	13	DLBCL_LSARP_Trios
04-21856_tumorA	177	87	16	DLBCL_LSARP_Trios
14-11427T	177	71	22	FL_GenomeCanada
SP192798	177	73	12	DLBCL_ICGC
06-22057T	176	50	5	DLBCL_Marra
13-34919T	176	37	7	FL_GenomeCanada
14-23891T	176	45	15	DLBCL_GenomeCanada
SP116686	176	94	6	MALY_Other_ICGC
SP124977	176	54	16	DLBCL_ICGC
15-15757T	175	54	18	DLBCL_GenomeCanada
FL1007T2	175	54	13	FL_Kridel
07-25012T	172	48	13	DLBCL_Marra
SP193910	172	81	13	FL_ICGC
13-27960T	171	115	19	FL_GenomeCanada
16-13732T	170	40	6	DLBCL_GenomeCanada
FL1006T2	169	78	14	FL_Kridel
SP116701	169	50	9	DLBCL_ICGC
SP59460	168	54	15	DLBCL_ICGC
04-24061_tumorB	163	16	11	DLBCL_LSARP_Trios
89-62169T	163	87	15	DLBCL_Gascoyne
FL1016T2	163	76	15	FL_Kridel
SP116718	163	71	8	FL_ICGC
07-25994_tumorB	162	90	20	DLBCL_LSARP_Trios
FL3013T1	162	80	4	FL_Kridel
SP192850	161	39	14	DLBCL_ICGC
07-25994_tumorC	160	88	21	DLBCL_LSARP_Trios
17-45529_tumorA	160	26	13	DLBCL_LSARP_Trios
SP116630	160	47	14	DLBCL_ICGC
SP116606	159	58	5	FL_ICGC
FL1010T1	158	58	8	FL_Kridel
SP193258	158	80	16	FL_ICGC
12-32967T	157	87	9	FL_GenomeCanada
SP193543	157	48	13	FL_ICGC
FL3009T1	156	81	9	FL_Kridel
15-13383T	154	75	28	DLBCL_GenomeCanada
15-36416T	154	67	14	FL_GenomeCanada
15-33862T	153	66	20	FL_GenomeCanada
SP192940	153	33	17	DLBCL_ICGC
94-15772_tumorA	152	43	6	DLBCL_LSARP_Trios
SP59312	152	42	10	DLBCL_ICGC
02-13135T	150	49	11	DLBCL_Gascoyne
19-13976_tumorA	150	48	3	DLBCL_LSARP_Trios
19-13976_tumorB	150	48	3	DLBCL_LSARP_Trios
96-31596T	150	67	11	DLBCL_GenomeCanada
SP116674	150	76	14	FL_ICGC
FL3008T1	149	78	15	FL_Kridel
FL3014T1	148	58	9	FL_Kridel
13-26597T	147	35	7	FL_GenomeCanada
14-34508T	147	80	16	FL_GenomeCanada
HTMCP-01-06-00310-01B-01D	147	59	17	DLBCL_HTMCP
SP193326	147	47	14	FL_ICGC
16-10805T	146	78	14	FL_GenomeCanada
SP124963	146	93	14	FL_ICGC
14-11247T	144	37	17	DLBCL_GenomeCanada
14-13959T	144	33	8	DLBCL_GenomeCanada
16-32417T	144	90	10	FL_GenomeCanada
FL3016T1	144	79	12	FL_Kridel
HTMCP-01-06-00227-01A-01D	144	57	14	DLBCL_HTMCP
SP192870	144	65	19	DLBCL_ICGC
01-28152_tumorB	143	37	14	DLBCL_LSARP_Trios
SP194108	143	71	25	FL_ICGC
00-15201_tumorB	142	44	15	DLBCL_LSARP_Trios
15-14583T	142	50	11	FL_GenomeCanada
15-16852T	142	45	14	FL_GenomeCanada
99-13520T	142	44	20	FL_GenomeCanada
10-27154T	141	39	12	DLBCL_Gascoyne
14-36022T	141	57	14	DLBCL_GenomeCanada
SP193364	141	57	5	FL_ICGC
05-22052T	140	51	14	DLBCL_Gascoyne
05-32150_tumorB	140	36	11	DLBCL_LSARP_Trios
15-37079T	140	36	12	FL_GenomeCanada
14-28286T	139	85	12	FL_GenomeCanada
14-41250T	139	61	15	FL_GenomeCanada
FL1013T2	139	29	6	FL_Kridel
SP193040	137	57	7	FL_ICGC
SP193914	137	36	11	DLBCL_ICGC
14-24907T	136	54	11	FL_GenomeCanada
17-33596_tumorA	136	23	3	DLBCL_LSARP_Trios
HTMCP-01-06-00121-01A-01D	136	21	6	DLBCL_HTMCP
SP193950	136	76	19	FL_ICGC
FL1016T1	135	67	12	FL_Kridel
14-29644T	134	68	14	FL_GenomeCanada
15-10535T	134	41	15	DLBCL_GenomeCanada
15-15253T	134	84	14	FL_GenomeCanada
16-29329T	134	60	16	DLBCL_GenomeCanada
SP124967	134	66	10	FL_ICGC
FL1012T2	133	16	3	FL_Kridel
92-38267_tumorA	132	40	12	DLBCL_LSARP_Trios
SP116616	132	35	8	FL_ICGC
15-24058T	131	33	8	DLBCL_GenomeCanada
SP194077	131	56	16	FL_ICGC
04-24061_tumorA	130	15	7	DLBCL_LSARP_Trios
13-19570T	130	67	15	FL_GenomeCanada
SP116618	130	50	11	DLBCL_ICGC
SP193954	130	66	9	FL_ICGC
14-16707T	129	38	3	DLBCL_GenomeCanada
17-33596_tumorB	129	18	5	DLBCL_LSARP_Trios
LY_RELY_109_tumorB	129	82	18	DLBCL_LSARP_Trios
01-16433_tumorA	128	33	6	DLBCL_LSARP_Trios
06-34043T	128	28	10	DLBCL_Marra
06-30145T	127	24	4	DLBCL_Marra
14-15505T	127	64	12	FL_GenomeCanada
13-31210T	126	56	11	DLBCL_GenomeCanada
15-12532T	126	46	8	FL_GenomeCanada
03-10440_tumorB	125	54	10	DLBCL_LSARP_Trios
14-10498_tumorB	125	41	11	DLBCL_LSARP_Trios
14-34800T	125	53	9	FL_GenomeCanada
17-12136T	125	76	10	DLBCL_GenomeCanada
FL1017T1	125	62	17	FL_Kridel
14-24648_tumorA	124	30	5	DLBCL_LSARP_Trios
16-19402T	124	52	10	FL_GenomeCanada
SP193186	124	60	18	FL_ICGC
14-24648_tumorB	122	31	6	DLBCL_LSARP_Trios
FL1015T2	122	61	11	FL_Kridel
HTMCP-01-02-00013-01A-01D	122	40	12	DLBCL_HTMCP
FL3019T1	121	66	13	FL_Kridel
14-35472_tumorB	120	23	9	DLBCL_LSARP_Trios
14-32185T	119	48	14	FL_GenomeCanada
FL3006T1	118	47	9	FL_Kridel
05-24395T	117	25	4	DLBCL_Marra
95-32814T	117	43	4	DLBCL_Marra
HTMCP-01-02-00017-01A-01D	117	27	18	DLBCL_HTMCP
14-33262T	115	39	13	DLBCL_GenomeCanada
01-23117_tumorB	114	27	13	DLBCL_LSARP_Trios
14-30670T	114	42	4	FL_GenomeCanada
16-31791T	114	40	8	DLBCL_GenomeCanada
FL3004T1	114	46	5	FL_Kridel
14-37865T	113	41	7	FL_GenomeCanada
SP193120	113	56	11	FL_ICGC
SP193945	113	18	9	MALY_Other_ICGC
10-40676T	112	44	6	DLBCL_GenomeCanada
FL1009T1	112	42	10	FL_Kridel
SP116635	112	52	8	DLBCL_ICGC
06-25674T	111	32	4	DLBCL_Marra
07-30628T	111	31	6	DLBCL_Marra
SP116654	111	55	17	FL_ICGC
13-43956T	110	56	13	FL_GenomeCanada
01-28152_tumorA	109	32	12	DLBCL_LSARP_Trios
14-34590T	109	47	3	FL_GenomeCanada
16-18623T	109	46	7	DLBCL_GenomeCanada
FL1006T1	109	51	13	FL_Kridel
FL1013T1	109	28	5	FL_Kridel
05-24904T	108	27	6	DLBCL_Marra
SP116604	108	45	6	FL_ICGC
FL1008T2	107	32	16	FL_Kridel
FL3010T1	107	56	12	FL_Kridel
SP116709	107	47	11	DLBCL_ICGC
98-22532T	106	35	8	DLBCL_Marra
FL1012T1	106	17	5	FL_Kridel
SP116688	106	30	19	DLBCL_ICGC
SP124981	106	31	3	DLBCL_ICGC
SP193855	106	20	11	FL_ICGC
06-22314_tumorB	105	17	7	DLBCL_LSARP_Trios
15-30563T	105	50	9	FL_GenomeCanada
01-23117_tumorA	103	33	9	DLBCL_LSARP_Trios
05-24006T	103	41	8	DLBCL_Marra
14-32922T	103	35	10	FL_GenomeCanada
HTMCP-01-01-00003-01D-03D	103	32	12	DLBCL_HTMCP
HTMCP-01-06-00422-01A-01D	103	51	13	DLBCL_HTMCP
05-32150T	102	32	12	DLBCL_Gascoyne
15-13365T	102	36	8	DLBCL_GenomeCanada
SP59416	101	39	8	FL_ICGC
05-23110T	100	19	9	DLBCL_Marra
05-12939T	97	19	4	DLBCL_Marra
15-42543T	97	54	7	FL_GenomeCanada
16-30371T	97	48	7	FL_GenomeCanada
00-26427_tumorC	96	24	14	DLBCL_LSARP_Trios
03-10440_tumorA	96	52	10	DLBCL_LSARP_Trios
05-25439T	96	10	4	DLBCL_Marra
06-10398T	95	29	5	DLBCL_Marra
11-28845T	95	37	12	FL_GenomeCanada
FL3015T1	95	34	14	FL_Kridel
SP194083	94	56	5	FL_ICGC
13-22818T	92	26	9	DLBCL_GenomeCanada
SP193655	92	45	6	FL_ICGC
SP194212	92	31	6	FL_ICGC
07-34776T	91	20	13	FL_GenomeCanada
14-26632T	91	35	11	FL_GenomeCanada
15-29858T	91	26	8	DLBCL_GenomeCanada
15-37466T	91	46	9	FL_GenomeCanada
04-14066_tumorB	90	26	8	DLBCL_LSARP_Trios
06-16716T	90	14	2	DLBCL_Marra
06-22314_tumorA	89	18	6	DLBCL_LSARP_Trios
16-27229T	89	47	9	FL_GenomeCanada
01-20260T	88	31	12	FL_GenomeCanada
15-14453T	88	44	8	FL_GenomeCanada
97-18502_tumorA	88	32	7	DLBCL_LSARP_Trios
SP116638	88	40	6	FL_ICGC
81-52884T	87	14	2	DLBCL_Marra
SP193744	87	27	6	FL_ICGC
FL2005T1	86	39	3	FL_Kridel
HTMCP-01-01-00012-01A-01D	86	39	2	DLBCL_HTMCP
SP194134	86	15	11	FL_ICGC
SP59360	86	6	6	DLBCL_ICGC
02-20170T	85	14	10	DLBCL_Marra
SP193925	85	36	9	FL_ICGC
04-29264T	84	39	3	DLBCL_Marra
13-40593T	84	42	8	FL_GenomeCanada
14-11009T	84	27	7	FL_GenomeCanada
15-10675T	84	39	13	FL_GenomeCanada
15-29305T	84	26	12	FL_GenomeCanada
15-39657T	84	32	8	FL_GenomeCanada
FL2003T1	84	9	4	FL_Kridel
SP116723	84	39	12	FL_ICGC
14-11777T	83	31	13	FL_GenomeCanada
14-32899T	83	13	10	FL_GenomeCanada
SP116720	83	26	7	FL_ICGC
09-11467T	82	36	7	DLBCL_Gascoyne
FL1007T1	82	33	8	FL_Kridel
SP59340	82	34	9	FL_ICGC
06-24915T	81	17	2	DLBCL_Marra
14-35472_tumorA	81	17	9	DLBCL_LSARP_Trios
SP124984	81	39	5	FL_ICGC
SP194216	81	37	7	DLBCL_ICGC
FL2006T1	80	35	10	FL_Kridel
HTMCP-01-06-00255-01A-01D	80	30	10	DLBCL_HTMCP
10-39333T	79	27	4	FL_GenomeCanada
FL2008T1	79	13	9	FL_Kridel
04-14066_tumorA	77	22	7	DLBCL_LSARP_Trios
94-15772_tumorB	77	26	3	DLBCL_LSARP_Trios
SP59300	77	12	NA	DLBCL_ICGC
14-13213T	76	41	7	FL_GenomeCanada
SP193808	75	14	6	FL_ICGC
SP194043	75	32	8	FL_ICGC
SP59356	75	27	6	FL_ICGC
02-22991T	74	16	5	DLBCL_Marra
15-40296T	74	35	7	FL_GenomeCanada
14-10498_tumorA	72	27	5	DLBCL_LSARP_Trios
SP193205	72	30	7	FL_ICGC
SP193570	72	20	4	FL_ICGC
SP193720	72	37	7	FL_ICGC
14-27524T	71	29	5	FL_GenomeCanada
SP194080	71	19	5	DLBCL_ICGC
SP59308	71	39	8	FL_ICGC
SP116624	70	30	1	DLBCL_ICGC
14-16281T	69	18	5	DLBCL_GenomeCanada
14-35030T	69	19	11	FL_GenomeCanada
16-37777T	69	34	7	FL_GenomeCanada
FL3007T1	69	17	7	FL_Kridel
SP59420	69	27	3	FL_ICGC
15-17849T	68	8	4	FL_GenomeCanada
16-13504T	68	34	5	FL_GenomeCanada
SP193965	68	28	3	FL_ICGC
SP194173	68	25	5	FL_ICGC
SP59316	68	37	5	FL_ICGC
FL1008T1	67	27	14	FL_Kridel
FL2001T1	67	34	5	FL_Kridel
FL3005T1	67	36	7	FL_Kridel
FL3017T1	67	28	7	FL_Kridel
06-11535T	66	14	6	DLBCL_Marra
SP194053	66	23	6	DLBCL_ICGC
02-15630_tumorA	65	19	5	DLBCL_LSARP_Trios
SP193347	65	5	6	FL_ICGC
SP193650	65	31	9	FL_ICGC
HTMCP-01-06-00232-01A-01D	64	16	6	DLBCL_HTMCP
00-26427_tumorA	63	10	5	DLBCL_LSARP_Trios
13-29091T	61	18	7	FL_GenomeCanada
14-33798_tumorB	61	13	4	DLBCL_LSARP_Trios
SP116627	61	17	7	DLBCL_ICGC
SP193057	61	27	4	FL_ICGC
SP193354	61	29	5	FL_ICGC
SP193528	61	19	2	DLBCL_ICGC
06-33777T	60	14	5	DLBCL_Marra
14-34708T	59	18	3	FL_GenomeCanada
FL3002T1	59	23	10	FL_Kridel
SP59464	59	23	5	FL_ICGC
02-15630_tumorB	58	15	2	DLBCL_LSARP_Trios
SP193093	58	21	8	FL_ICGC
SP59280	58	10	13	DLBCL_ICGC
SP59292	58	22	8	FL_ICGC
09-31233T	57	22	4	DLBCL_GenomeCanada
16-32248_tumorA	57	20	5	DLBCL_LSARP_Trios
FL1014T1	57	13	8	FL_Kridel
FL3018T1	57	29	5	FL_Kridel
HTMCP-01-15-00367-01A-01E	57	14	3	DLBCL_HTMCP
08-25894T	56	17	1	DLBCL_Marra
14-13480T	56	12	5	FL_GenomeCanada
SP194238	56	25	10	FL_ICGC
FL1001T2	55	10	8	FL_Kridel
14-29140T	54	19	3	FL_GenomeCanada
FL2007T1	54	22	7	FL_Kridel
SP192804	54	13	6	FL_ICGC
FL1015T1	53	22	7	FL_Kridel
16-17861T	52	7	6	DLBCL_GenomeCanada
15-14813T	51	5	9	FL_GenomeCanada
SP59320	51	12	4	FL_ICGC
SP59432	51	17	6	FL_ICGC
02-18356_tumorA	50	3	5	DLBCL_LSARP_Trios
HTMCP-01-06-00500-01A-01D	50	15	7	DLBCL_HTMCP
SP193467	50	19	5	FL_ICGC
SP193684	50	12	3	DLBCL_ICGC
SP193801	50	23	4	FL_ICGC
SP193828	50	15	7	FL_ICGC
FL3012T1	49	11	7	FL_Kridel
SP193992	49	21	5	FL_ICGC
SP194065	49	13	8	FL_ICGC
02-18356_tumorB	48	6	7	DLBCL_LSARP_Trios
FL1005T2	48	NA	5	FL_Kridel
SP116703	47	15	4	FL_ICGC
03-34157T	45	23	4	FL_GenomeCanada
SP194205	45	12	7	FL_ICGC
SP116622	44	23	3	FL_ICGC
SP194234	43	7	8	DLBCL_ICGC
SP59324	42	4	1	DLBCL_ICGC
SP192863	41	12	7	FL_ICGC
FL1005T1	40	1	6	FL_Kridel
SP116672	40	12	9	FL_ICGC
HTMCP-01-06-00485-01A-01D	39	11	12	DLBCL_HTMCP
SP116608	39	5	1	FL_ICGC
SP193300	38	9	4	DLBCL_ICGC
12-29259T	37	9	4	DLBCL_Gascoyne
14-25416T	36	15	3	FL_GenomeCanada
FL2004T1	36	8	NA	FL_Kridel
SP193993	36	12	6	FL_ICGC
10-11584_tumorB	34	21	6	DLBCL_LSARP_Trios
SP193777	34	9	6	FL_ICGC
SP59424	32	6	1	FL_ICGC
FL1001T1	31	5	5	FL_Kridel
SP59372	27	5	3	DLBCL_ICGC
SP116679	26	5	6	FL_ICGC
SP59376	26	6	5	DLBCL_ICGC
HTMCP-01-07-00336-01A-01E	25	2	NA	DLBCL_HTMCP
SP59380	25	6	2	FL_ICGC
HTMCP-01-06-00606-01A-01D	24	7	5	DLBCL_HTMCP
SP59436	24	8	3	FL_ICGC
14-33798_tumorA	23	1	NA	DLBCL_LSARP_Trios
SP124983	17	3	1	FL_ICGC
14-13938T	15	5	NA	FL_GenomeCanada
04-28140T	14	5	1	DLBCL_Gascoyne
10-11584_tumorA	13	6	2	DLBCL_LSARP_Trios
05-24561T	10	1	NA	DLBCL_Marra
HTMCP-01-06-00526-01A-01D	10	5	1	DLBCL_HTMCP
SP193450	6	4	3	FL_ICGC

Note

From this output we can see that there are actually genome-wide mutation calls for a few samples. All these samples are cell lines.

my_meta = dplyr::filter(my_meta,
                       !cohort %in% "DLBCL_cell_lines") 
genome_coding <- get_coding_ssm(
  these_samples_metadata = my_meta,
  projection = "grch37",
  include_silent = TRUE,
  this_seq_type = "genome"
)

Coding and non-coding mutations

For a high-level overview of what genes the mutations are subset to and their overall mutation incidence in these samples, we can use the GAMBLR.viz function prettyGeneCloud. This function will automatically remove non-coding variants from your data as a convenience feature. We can get around that by assigning the Variant_Classification column for all mutations to imply they are Missense mutations.

fake_maf = mutate(genome_all,Variant_Classification = "Missense_Mutation")

prettyGeneCloud(fake_maf,
zoomout = 0.2,these_genes= unique(genome_all$Hugo_Symbol))

prettyGeneCloud(genome_all,
zoomout = 0.4,these_genes= unique(genome_all$Hugo_Symbol))

You will notice that many genes that were prominent in the first cloud are much smaller in the second one. This can be explained by the overwhelming fraction of their mutations representing non-coding variants. This is confirmed by counting up the mutations by Variant_Classification, as demonstrated below.

filter(genome_all,
  Hugo_Symbol %in% c("BRINP3","PTPRD","DOCK1","UNC5C")) %>% 
  group_by(Hugo_Symbol,
           Variant_Classification) %>% 
  count() %>%
  kableExtra::kable(format="html")

Hugo_Symbol	Variant_Classification	n
BRINP3	3'Flank	47
BRINP3	3'UTR	4
BRINP3	5'Flank	43
BRINP3	5'UTR	1
BRINP3	Frame_Shift_Ins	1
BRINP3	Intron	2665
BRINP3	Missense_Mutation	13
BRINP3	Nonsense_Mutation	2
BRINP3	Silent	5
DOCK1	3'Flank	29
DOCK1	3'UTR	2
DOCK1	5'Flank	21
DOCK1	Intron	1947
DOCK1	Missense_Mutation	15
DOCK1	Silent	7
DOCK1	Splice_Region	3
DOCK1	Splice_Site	1
PTPRD	3'Flank	19
PTPRD	3'UTR	20
PTPRD	5'Flank	25
PTPRD	5'UTR	4
PTPRD	Frame_Shift_Del	1
PTPRD	Intron	11582
PTPRD	Missense_Mutation	17
PTPRD	Nonsense_Mutation	3
PTPRD	Silent	7
PTPRD	Splice_Region	2
PTPRD	Splice_Site	3
UNC5C	3'Flank	15
UNC5C	3'UTR	25
UNC5C	5'Flank	33
UNC5C	Intron	1907
UNC5C	Missense_Mutation	12
UNC5C	Silent	5
UNC5C	Splice_Region	1

aSHM targets

Rather than completely ignoring non-coding variants, we can use this approach to gain an overview of the frequency of mutations in regions that have been identified as targets of aSHM.

# re-run with the cell lines removed
ashm_genome_maf = get_ssm_by_regions(these_samples_metadata = my_meta,
                              this_seq_type = "genome",
                              streamlined = F)
prettyGeneCloud(mutate(ashm_genome_maf,
                Variant_Classification = "Missense_Mutation"),
                zoomout = 0.4,
                these_genes= unique(ashm_genome_maf$Hugo_Symbol))

# re-run with the cell lines removed
ashm_genome_streamlined = get_ssm_by_regions(these_samples_metadata = my_meta,
                              this_seq_type = "genome",
                              streamlined = TRUE,
                              use_name_column = TRUE)
#add columns to force prettyGeneCloud to include everything 
ashm_genome_streamlined = mutate(ashm_genome_streamlined,
                            Hugo_Symbol = region_name,
                            Variant_Classification= "Missense_Mutation",
                            Tumor_Sample_Barcode = sample_id)

prettyGeneCloud(ashm_genome_streamlined,
                zoomout = 0.3,
                these_genes= unique(ashm_genome_streamlined$Hugo_Symbol))

Summarizing with ggplot2

Word clouds are not useful for communicating the relationship between numeric values. We’ll continue using ggplot2 instead.

ashm_genome_freq = mutate(ashm_genome_streamlined,
                          gene = str_remove(Hugo_Symbol,"-.+")) %>%
                   group_by(gene) %>%
                   summarise(num_mutations=n()) %>% 
                   arrange(desc(num_mutations))
ashm_genome_freq$gene = factor(ashm_genome_freq$gene,
                              levels = rev(unique(ashm_genome_freq$gene)))
p = ggplot(ashm_genome_freq,aes(y=gene,x=num_mutations)) + 
    geom_col() +
    theme_Morons(base_size=4)
p

As you can see, the total number of coding + non-coding mutations affecting each of these genes among these samples is quite variable. BCL2, IGLL5, BCL6, PAX5 etc are the most heavily affected.

Building a MAF summary from scratch

Many analyses will probably focus on mutations in protein-coding space and their predicted effect on proteins. For the rest of this tutorial, we’ll delve into this with the mutations we obtained at the start using get_coding_ssm. Here, we’ll work towards reproducing the output of maftools::plotmafSummary, working on one panel at a time.

make_panel1 = function(maf_data,base_size=7,title=""){
vc_counted  = maf_data %>% 
  group_by(Variant_Classification) %>% 
  count() %>% 
  arrange(n)
vc_counted$Variant_Classification = factor(
        vc_counted$Variant_Classification,
        levels=unique(vc_counted$Variant_Classification)
    )
mut_cols = get_gambl_colours("mutation")
p1 = ggplot(vc_counted,
            aes(x=n,
                y=Variant_Classification,
                fill=Variant_Classification)) + 
  geom_col() + scale_fill_manual(values=mut_cols)+
  theme_Morons(base_size = base_size,
  my_legend_position = "none")  +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
    axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
        ) +
  ggtitle(title)
p1
}
make_panel1(genome_coding, title="Genomes, coding regions")

make_panel1(genome_all, title="Genomes, all regions")

make_panel1(capture_coding, title="Exomes")

make_panel2 = function(maf_data,base_size=7,title=""){
type_counted  = maf_data %>% 
  group_by(Variant_Type) %>% 
  count() %>% 
  arrange(n)
type_counted$Variant_Type = factor(
        type_counted$Variant_Type,
        levels=unique(type_counted$Variant_Type)
    )

mut_cols = c(SNP="purple1",INS="yellow3",DEL="lightblue",DNP="orange","TNP"="lightgreen")
p2 =ggplot(type_counted,aes(x=n,y=Variant_Type,fill=Variant_Type)) + 
  geom_col() + scale_fill_manual(values=mut_cols)+
  theme_Morons(base_size = base_size,my_legend_position = "none")  +
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
    axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
        ) +
  ggtitle(title)
  p2
}
make_panel2(genome_coding, title="Genomes, coding regions")

make_panel2(genome_all, title="Genomes, all regions")

make_panel2(capture_coding, title = "Exomes")

make_panel3 = function(maf_data,base_size=7,title=""){
  

comp = function(base){
  chartr("ACTG", "TGAC",base)
}
maf_data = mutate(maf_data,
                       class = case_when(
                         Reference_Allele %in% c("T","C") ~ 
                           paste0(Reference_Allele,
                                  ">",
                                  Tumor_Seq_Allele2),
                         TRUE ~ paste0(comp(Reference_Allele),
                                       ">",
                                       comp(Tumor_Seq_Allele2)))
                       )

class_counted = maf_data %>% dplyr::filter(Variant_Type == "SNP") %>%
  group_by(class) %>% count()
class_counted = mutate(class_counted,class = factor(class,levels=c("C>A","C>G","C>T","T>C","T>A","T>G")))
mut_cols = get_gambl_colours("rainfall")
p3 = ggplot(class_counted,aes(x=n,y=class,fill=class)) + 
  geom_col() + scale_fill_manual(values=mut_cols)+
  theme_Morons(base_size = base_size,my_legend_position = "none")  + 
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
    axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
        ) +
  ggtitle(title)
p3
}
make_panel3(genome_coding, title="Genomes, coding regions")

make_panel3(genome_all, title="Genomes, all regions")

make_panel3(capture_coding, title = "Exomes")

make_panel4 = function(maf_data,base_size=7,title=""){
  

type_counted  = maf_data %>% 
  group_by(Tumor_Sample_Barcode,Variant_Classification) %>% 
  count() %>% 
  arrange(desc(n))
type_counted$Tumor_Sample_Barcode = factor(type_counted$Tumor_Sample_Barcode,
                                            levels=unique(type_counted$Tumor_Sample_Barcode))

mut_cols = get_gambl_colours("mutation")
p4 = ggplot(type_counted,aes(x=Tumor_Sample_Barcode,y=n,fill=Variant_Classification)) + 
  geom_col() +
  scale_fill_manual(values=mut_cols) +
  
  theme_Morons(base_size = base_size,my_legend_position = "none") + 
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
    axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
        ) +
  ggtitle(title)
  

p4
}
make_panel4(genome_coding, title="Genomes, coding regions")

make_panel4(genome_all, title="Genomes, all regions")

make_panel4(capture_coding, title = "Exomes")

library(ggbeeswarm)
make_panel5 = function(maf_data,base_size=7,point_size=0.5,title=""){
  mut_cols = get_gambl_colours()
  type_counted  = maf_data %>% 
  group_by(Tumor_Sample_Barcode,Variant_Classification) %>% 
  count() %>% 
  arrange(desc(n))
vc_counted  = maf_data %>% 
  group_by(Variant_Classification) %>% 
  count() %>% 
  arrange(n)
vc_counted$Variant_Classification = factor(vc_counted$Variant_Classification,
                                             levels=unique(vc_counted$Variant_Classification))
type_counted$Variant_Classification = factor(type_counted$Variant_Classification,
                                             levels=rev(unique(vc_counted$Variant_Classification)))
p5 = ggplot(type_counted,aes(x=Variant_Classification,y=n,colour=Variant_Classification)) + 
  geom_quasirandom(size=point_size) +
  scale_colour_manual(values=mut_cols) +
  scale_y_log10() +
  theme_Morons(base_size = base_size,my_legend_position = "none")  +
  theme(axis.title.y =element_blank(),
        axis.text.x =element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x=element_blank()) +
  ggtitle(title)
p5
}
make_panel5(genome_coding, title="Genomes, coding regions")

make_panel5(genome_all, title="Genomes, all regions")

make_panel5(capture_coding, title="Exomes")

make_panel6 = function(maf_data,base_size=7,top=10,title=""){
  type_counted  = maf_data %>% 
  group_by(Hugo_Symbol,Variant_Classification) %>% 
  count() %>% 
  arrange(n)

top_n = group_by(type_counted,Hugo_Symbol) %>%
  summarise(total=sum(n)) %>%
  arrange(desc(total)) %>%
  slice_head(n=top) %>%
  pull(Hugo_Symbol)
mut_cols = get_gambl_colours()
some_type_counted = dplyr::filter(type_counted,Hugo_Symbol %in% top_n)
some_type_counted$Hugo_Symbol = factor(some_type_counted$Hugo_Symbol,
                                            levels=rev(top_n))
p6 = 
  ggplot(some_type_counted,aes(y=Hugo_Symbol,x=n,fill=Variant_Classification)) + 
  geom_col() +
  scale_fill_manual(values=mut_cols) +
  theme_Morons(base_size = base_size,my_legend_position = "none")  +
  theme(axis.text.x=element_blank(),
        axis.title.y = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x=element_blank()) +
  ggtitle(title)

p6
}
make_panel6(genome_coding,base_size=6, title="Genome, coding regions")

make_panel6(genome_all, title="Genomes, all regions")

make_panel6(capture_coding, title = "Exomes")

library(cowplot)
bs = 8
ps =0.1
p1 = make_panel1(genome_coding,base_size = bs,title="Variant Classification")
p2 = make_panel2(genome_coding,base_size = bs,title="Variant Type")
p3 = make_panel3(genome_coding,base_size = bs,title="SNV Class")
p4 = make_panel4(genome_coding,base_size = bs,title="Variants per sample")
p5 = make_panel5(genome_coding,base_size = bs,point_size=ps,title="Variant Classification Summary")
p6 = make_panel6(genome_coding,base_size = bs, title="Top 10 genes")
all_p = cowplot::plot_grid(p1,p2,p3,p4,p5,p6,nrow = 2,ncol=3)
all_p

Happy GAMBLing!

  /$$$$$$     /$$$$$$    /$$      /$$   /$$$$$$$    /$$        .:::::::
 /$$__  $$   /$$__  $$  | $$$    /$$$  | $$__  $$  | $$        .::    .::
| $$  \__/  | $$  \ $$  | $$$$  /$$$$  | $$  \ $$  | $$        .::    .::
| $$ /$$$$  | $$$$$$$$  | $$ $$/$$ $$  | $$$$$$$   | $$   <-   .: .::
| $$|_  $$  | $$__  $$  | $$  $$$| $$  | $$__  $$  | $$        .::  .::
| $$  \ $$  | $$  | $$  | $$\  $ | $$  | $$  \ $$  | $$        .::    .::
|  $$$$$$/  | $$  | $$  | $$ \/  | $$  | $$$$$$$/  | $$$$$$$$  .::      .::
 \______/   |__/  |__/  |__/     |__/  |_______/   |________/
 ~GENOMIC~~~~~~~~~~~~~OF~~~~~~~~~~~~~~~~~B-CELL~~~~~~~~~~~~~~~~~~IN~~~~~~
 ~~~~~~~~~~~~ANALYSIS~~~~~~MATURE~~~~~~~~~~~~~~~~~~~LYMPHOMAS~~~~~~~~~~R~