Repertoire overlap

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)

p1 <- vis(imm_ov1)
p2 <- vis(imm_ov2, .text.size = 2)

p1 + p2

vis(imm_ov1, "heatmap2")

You can easily change the number of significant digits:

p1 <- vis(imm_ov2, .text.size = 2.5, .signif.digits = 1)
p2 <- vis(imm_ov2, .text.size = 2, .signif.digits = 2)

p1 + p2

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 -18.7767715 -18.360817
## A2-i131  29.9586985  -7.870441
## A2-i133  28.1148594  22.629093
## A2-i132 -44.3435640   6.221812
## A4-i191  13.8586515   7.452149
## A4-i192 -14.0065477  27.068830
## MS1      -8.8469009  -8.151574
## MS2      -0.9712073  -1.297017
## MS3     -10.4398629   4.894354
## MS4       0.5131505  10.471309
## MS5      18.5153823 -12.628029
## MS6       6.4241122 -30.429669
# "tsne" - t-Stochastic Neighbor Embedding
repOverlapAnalysis(imm_ov1, "tsne")
##               DimI      DimII
## A2-i129   55.91219 -26.522331
## A2-i131 -278.35982   8.060975
## A2-i133   41.90810   2.479033
## A2-i132   77.49466  27.360227
## A4-i191   48.16214  32.946045
## A4-i192   51.44503 -44.376283
## MS1       36.31622 -30.328954
## MS2       65.69934  23.357776
## MS3       49.31653 -30.642152
## MS4       57.66025  24.905259
## MS5     -277.51354  15.474082
## MS6       71.95892  -2.713676
## attr(,"class")
## [1] "immunr_tsne" "matrix"      "array"
# Visualise the results
repOverlapAnalysis(imm_ov1, "mds") %>% vis()

# 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 -18.7767715 -18.360817
## A2-i131  29.9586985  -7.870441
## A2-i133  28.1148594  22.629093
## A2-i132 -44.3435640   6.221812
## A4-i191  13.8586515   7.452149
## A4-i192 -14.0065477  27.068830
## MS1      -8.8469009  -8.151574
## MS2      -0.9712073  -1.297017
## MS3     -10.4398629   4.894354
## MS4       0.5131505  10.471309
## MS5      18.5153823 -12.628029
## MS6       6.4241122 -30.429669
# "tsne" - t-Stochastic Neighbor Embedding
repOverlapAnalysis(imm_ov1, "tsne")
##                DimI      DimII
## A2-i129 -169.162899  122.43644
## A2-i131  -33.450388 -479.33235
## A2-i133  -21.925144  119.44208
## A2-i132  188.786772  180.58777
## A4-i191  105.531122   48.43325
## A4-i192 -253.408806   84.13795
## MS1     -172.083783   23.48401
## MS2      161.133177  126.28609
## MS3     -184.760923   86.31360
## MS4      128.370622  102.12880
## MS5        3.457591 -473.82621
## MS6      247.512659   59.90858
## attr(,"class")
## [1] "immunr_tsne" "matrix"      "array"
# Visualise the results
repOverlapAnalysis(imm_ov1, "mds") %>% vis()

# Clusterise the MDS resulting components using K-means
repOverlapAnalysis(imm_ov1, "mds+kmeans") %>% vis()

Public repertoire

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       1
##     2:                   TGTGCCAGCAGCTTCCAAGAGACCCAGTACTTC       7      NA
##     3:                   TGTGCCAGCAGTTACCAAGAGACCCAGTACTTC       7       1
##     4:                   TGCGCCAGCAGCTTCCAAGAGACCCAGTACTTC       6       2
##     5:                      TGTGCCAGCAGCCAAGAGACCCAGTACTTC       6       4
##    ---                                                                    
## 75101:             TGTGCTTCACAACTCTTATTGGACGAGACCCAGTACTTC       1      NA
## 75102: TGTGCTTCACAAGCCCTACAGGGCACTTTCCATAATTCACCCCTCCACTTT       1      NA
## 75103:                   TGTGCTTCAGGGCGGGCCTACGAGCAGTACTTC       1      NA
## 75104:             TGTGCTTCCGCCGGACCGGACCGGGAGACCCAGTACTTC       1      NA
## 75105:                TGTGCTTGCGGGACAGATAACTATGGCTACACCTTC       1      NA
##        A2-i131 A2-i133 A2-i132 A4-i191 A4-i192 MS1 MS2 MS3 MS4 MS5 MS6
##     1:      NA       1       1      NA       1  NA  NA   1   1   1   1
##     2:       1       1       2       1      NA   1  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:       2      NA       2       3       1  NA  NA  NA  NA   4  NA
##    ---                                                                
## 75101:       1      NA      NA      NA      NA  NA  NA  NA  NA  NA  NA
## 75102:      NA      NA      NA      NA      NA  NA  NA  NA  NA   1  NA
## 75103:      NA      NA      NA      NA      NA   1  NA  NA  NA  NA  NA
## 75104:      NA       1      NA      NA      NA  NA  NA  NA  NA  NA  NA
## 75105:      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       1      NA       2       1
##     2:     CASSDSSGGANEQFF  TRBV6-4       6       1       1       2      NA
##     3:         CASSFQETQYF  TRBV5-1       6       3      NA       1       1
##     4:         CASSLGETQYF TRBV12-4       6       2      NA      NA       4
##     5:     CASSDSGGSYNEQFF  TRBV6-4       5      NA      NA      NA       3
##    ---                                                                     
## 74440:     CTSSRPTQGAYEQYF  TRBV7-2       1      NA      NA      NA      NA
## 74441:    CTSSSRAGAGTDTQYF  TRBV7-2       1      NA      NA      NA      NA
## 74442: CTSSYPGLAGLKRKETQYF  TRBV7-2       1      NA      NA      NA       1
## 74443:    CTSSYRQRPYQETQYF  TRBV7-2       1      NA      NA      NA      NA
## 74444:      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:       3      NA  NA  NA   2  NA  NA  12
##     3:      NA      NA  NA   1  NA   1  NA   1
##     4:       3      NA   1  NA  NA  NA   2   1
##     5:      NA       1   1  NA   1  NA  NA   1
##    ---                                        
## 74440:      NA      NA  NA  NA  NA  NA  NA   1
## 74441:      NA      NA  NA  NA   1  NA  NA  NA
## 74442:      NA      NA  NA  NA  NA  NA  NA  NA
## 74443:      NA      NA  NA  NA   1  NA  NA  NA
## 74444:      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