vignettes/web_only/v4_overlap.Rmd
v4_overlap.Rmd
Repertoire overlap is the most common approach to measure repertoire similarity. It is achieved by computation of specific statistics on clonotypes shared between given repertoires, also called “public” clonotypes. immunarch
provides several indices: - number of public clonotypes (.method = "public"
) - a classic measure of overlap similarity.
overlap coefficient (.method = "overlap"
) - a normalised measure of overlap similarity. It is defined as the size of the intersection divided by the smaller of the size of the two sets.
Jaccard index (.method = "jaccard"
) - it measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets.
Tversky index (.method = "tversky"
) - an asymmetric similarity measure on sets that compares a variant to a prototype. If using default arguments, it’s similar to Dice’s coefficient.
cosine similarity (.method = "cosine"
) - a measure of similarity between two non-zero vectors
Morisita’s overlap index (.method = "morisita"
) - a statistical measure of dispersion of individuals in a population. It is used to compare overlap among samples.
incremental overlap - overlaps of the N most abundant clonotypes with incrementally growing N (.method = "inc+METHOD"
, e.g., "inc+public"
or "inc+morisita"
).
The function that includes described methods is repOverlap
. Again the output is easily visualised when passed to vis()
function that does all the work:
imm_ov1 = repOverlap(immdata$data, .method = "public", .verbose = F)
imm_ov2 = repOverlap(immdata$data, .method = "morisita", .verbose = F)
grid.arrange(vis(imm_ov1), vis(imm_ov2, .text.size=2), ncol = 2)
You can easily change the number of significant digits:
grid.arrange(vis(imm_ov2, .text.size=2.5, .signif.digits=1), vis(imm_ov2, .text.size=2, .signif.digits=2), ncol = 2)
To analyse the computed overlap measures function apply repOverlapAnalysis
.
# Apply different analysis algorithms to the matrix of public clonotypes:
# "mds" - Multi-dimensional Scaling
repOverlapAnalysis(imm_ov1, "mds")
## Standard deviations (1, .., p=4):
## [1] 0 0 0 0
##
## Rotation (n x k) = (12 x 2):
## [,1] [,2]
## A2-i129 -20.2308709 22.431389
## A2-i131 8.3055445 -26.779321
## A2-i133 45.9341813 -6.893304
## A2-i132 -55.0903957 -18.572513
## A4-i191 23.7461189 -1.118162
## A4-i192 -4.4041243 38.028858
## MS1 -19.5494165 -12.836320
## MS2 -1.9063188 -6.075283
## MS3 -9.8321059 11.217724
## MS4 0.9127103 1.154627
## MS5 5.6552254 -27.415676
## MS6 26.4594518 26.857981
## DimI DimII
## A2-i129 66.63816 -114.58184
## A2-i131 13.03745 -30.09978
## A2-i133 -49.17857 80.77521
## A2-i132 -41.13009 87.09387
## A4-i191 -45.49009 96.35028
## A4-i192 63.34893 -123.78398
## MS1 74.31461 -120.62902
## MS2 -52.80716 89.85864
## MS3 67.77735 -118.54062
## MS4 -48.48749 88.83537
## MS5 11.26974 -26.67354
## MS6 -59.29284 91.39541
## attr(,"class")
## [1] "matrix" "immunr_tsne"
# Apply different analysis algorithms to the matrix of public clonotypes:
# "mds" - Multi-dimensional Scaling
repOverlapAnalysis(imm_ov1, "mds")
## Standard deviations (1, .., p=4):
## [1] 0 0 0 0
##
## Rotation (n x k) = (12 x 2):
## [,1] [,2]
## A2-i129 -20.2308709 22.431389
## A2-i131 8.3055445 -26.779321
## A2-i133 45.9341813 -6.893304
## A2-i132 -55.0903957 -18.572513
## A4-i191 23.7461189 -1.118162
## A4-i192 -4.4041243 38.028858
## MS1 -19.5494165 -12.836320
## MS2 -1.9063188 -6.075283
## MS3 -9.8321059 11.217724
## MS4 0.9127103 1.154627
## MS5 5.6552254 -27.415676
## MS6 26.4594518 26.857981
## DimI DimII
## A2-i129 -247.9701 -56.01593
## A2-i131 229.6228 350.57072
## A2-i133 -322.4567 -194.78082
## A2-i132 214.2786 -53.78506
## A4-i191 127.0437 -21.76749
## A4-i192 -166.0795 -31.99389
## MS1 -245.8420 28.87181
## MS2 146.5506 -109.38137
## MS3 -225.5800 -26.96249
## MS4 155.5883 -72.40200
## MS5 195.0063 351.17525
## MS6 139.8378 -163.52873
## attr(,"class")
## [1] "matrix" "immunr_tsne"
# Clusterise the MDS resulting components using K-means
vis(repOverlapAnalysis(imm_ov1, "mds+kmeans"))
In order to build a massive table with all clonotypes from the list of repertoires use the pubRep
function.
# Pass "nt" as the second parameter to build the public repertoire table using CDR3 nucleotide sequences
pr.nt = pubRep(immdata$data, "nt", .verbose = F)
pr.nt
## CDR3.nt Samples A2-i129
## 1: TGCGCCAGCAGCTTGGAAGAGACCCAGTACTTC 8 2
## 2: TGTGCCAGCAGCTTCCAAGAGACCCAGTACTTC 7 NA
## 3: TGTGCCAGCAGTTACCAAGAGACCCAGTACTTC 7 1
## 4: TGCGCCAGCAGCTTCCAAGAGACCCAGTACTTC 6 2
## 5: TGTGCCAGCAGCCAAGAGACCCAGTACTTC 6 5
## ---
## 86979: TGTGCTTCACAACTCTTATTGGACGAGACCCAGTACTTC 1 NA
## 86980: TGTGCTTCACAAGCCCTACAGGGCACTTTCCATAATTCACCCCTCCACTTT 1 NA
## 86981: TGTGCTTCAGGGCGGGCCTACGAGCAGTACTTC 1 NA
## 86982: TGTGCTTCCGCCGGACCGGACCGGGAGACCCAGTACTTC 1 NA
## 86983: TGTGCTTGCGGGACAGATAACTATGGCTACACCTTC 1 NA
## A2-i131 A2-i133 A2-i132 A4-i191 A4-i192 MS1 MS2 MS3 MS4 MS5 MS6
## 1: NA 2 1 NA 1 NA NA 1 1 1 1
## 2: 1 1 2 1 NA 2 NA NA 2 NA 1
## 3: 1 1 NA 1 1 1 NA 2 NA NA NA
## 4: NA 1 1 NA NA NA 1 NA 1 NA 1
## 5: 3 NA 2 3 2 NA NA NA NA 5 NA
## ---
## 86979: 1 NA NA NA NA NA NA NA NA NA NA
## 86980: NA NA NA NA NA NA NA NA NA 1 NA
## 86981: NA NA NA NA NA 1 NA NA NA NA NA
## 86982: NA 1 NA NA NA NA NA NA NA NA NA
## 86983: NA NA NA NA 1 NA NA NA NA NA NA
# Pass "aa+v" as the second parameter to build the public repertoire table using CDR3 aminoacid sequences and V alleles
# In order to use only CDR3 aminoacid sequences, just pass "aa"
pr.aav = pubRep(immdata$data, "aa+v", .verbose = F)
pr.aav
## CDR3.aa V.name Samples A2-i129 A2-i131 A2-i133 A2-i132
## 1: CASSLEETQYF TRBV5-1 8 2 NA 3 1
## 2: CASSDSSGGANEQFF TRBV6-4 6 1 1 2 NA
## 3: CASSDSSGSTDTQYF TRBV6-4 6 NA NA NA 4
## 4: CASSFQETQYF TRBV5-1 6 3 NA 1 1
## 5: CASSLGETQYF TRBV12-4 6 2 NA NA 4
## ---
## 86181: CTSSRPTQGAYEQYF TRBV7-2 1 NA NA NA NA
## 86182: CTSSSRAGAGTDTQYF TRBV7-2 1 NA NA NA NA
## 86183: CTSSYPGLAGLKRKETQYF TRBV7-2 1 NA NA NA 1
## 86184: CTSSYRQRPYQETQYF TRBV7-2 1 NA NA NA NA
## 86185: CTSSYSTSGVGQFF TRBV7-2 1 NA NA NA NA
## A4-i191 A4-i192 MS1 MS2 MS3 MS4 MS5 MS6
## 1: NA 2 NA NA 1 1 1 1
## 2: 5 NA NA NA 2 NA NA 15
## 3: 1 1 NA NA 1 1 NA 2
## 4: NA NA NA 1 NA 1 NA 1
## 5: 3 NA 1 NA NA NA 2 3
## ---
## 86181: NA NA NA NA NA NA NA 1
## 86182: NA NA NA NA 1 NA NA NA
## 86183: NA NA NA NA NA NA NA NA
## 86184: NA NA NA NA 1 NA NA NA
## 86185: NA NA NA NA NA 1 NA NA
# You can also pass the ".coding" parameter to filter out all noncoding sequences first:
pr.aav.cod = pubRep(immdata$data, "aa+v", .coding=T)
# Create a public repertoire with coding-only sequences using both CDR3 amino acid sequences and V genes
pr = pubRep(immdata$data, "aa+v", .coding = T, .verbose = F)
# Apply the filter subroutine to leave clonotypes presented only in healthy individuals
pr1 = pubRepFilter(pr, immdata$meta, c(Status = "C"))
# Apply the filter subroutine to leave clonotypes presented only in diseased individuals
pr2 = pubRepFilter(pr, immdata$meta, c(Status = "MS"))
# Divide one by another
pr3 = pubRepApply(pr1, pr2)
# Plot it
p = ggplot() + geom_jitter(aes(x = "Treatment", y = Result), data=pr3)
p