• 1 Description & importing data
  • 2 Summary statistics
  • 3 Clustering
    • 3.1 PD-1+TIGIT+
      • 3.1.1 Patient - P5
      • 3.1.2 Patient - P6
      • 3.1.3 Patient - P7
      • 3.1.4 Patient - P8
      • 3.1.5 Patient - P14
      • 3.1.6 Patient - P15
      • 3.1.7 Patient - P16
      • 3.1.8 Patient - P18
      • 3.1.9 Patient - P19
      • 3.1.10 Patient - P21
      • 3.1.11 Patient - P22
      • 3.1.12 Patient - P23
    • 3.2 PD-1+
      • 3.2.1 Patient - P5
      • 3.2.2 Patient - P6
      • 3.2.3 Patient - P7
      • 3.2.4 Patient - P8
      • 3.2.5 Patient - P14
      • 3.2.6 Patient - P15
      • 3.2.7 Patient - P16
      • 3.2.8 Patient - P18
      • 3.2.9 Patient - P19
      • 3.2.10 Patient - P21
      • 3.2.11 Patient - P22
      • 3.2.12 Patient - P23
    • 3.3 TIGIT+
      • 3.3.1 Patient - P5
      • 3.3.2 Patient - P6
      • 3.3.3 Patient - P7
      • 3.3.4 Patient - P8
      • 3.3.5 Patient - P14
      • 3.3.6 Patient - P15
      • 3.3.7 Patient - P16
      • 3.3.8 Patient - P18
      • 3.3.9 Patient - P19
      • 3.3.10 Patient - P21
      • 3.3.11 Patient - P22
      • 3.3.12 Patient - P23
    • 3.4 PD-1-TIGIT-
      • 3.4.1 Patient - P5
      • 3.4.2 Patient - P6
      • 3.4.3 Patient - P7
      • 3.4.4 Patient - P8
      • 3.4.5 Patient - P14
      • 3.4.6 Patient - P15
      • 3.4.7 Patient - P16
      • 3.4.8 Patient - P18
      • 3.4.9 Patient - P19
      • 3.4.10 Patient - P21
      • 3.4.11 Patient - P22
      • 3.4.12 Patient - P23
    • 3.5 Summary

Last updated: 2020-01-14

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Rmd 49f6c61 ValentinVoillet 2020-01-14 Edits .Rmd (TCR-seq_EDA_2)
Rmd 99d00e5 ValentinVoillet 2020-01-14 Edits .Rmd (TCR-seq_EDA_2)

File creation: January, 12th 2020
Update: January, 14th 2020

1 Description & importing data

RNA was extracted from 12 patients. Alignment and quantification of TCR sequences have been performed by QIAGEN

  • 12 patients: P5, P6, P7, P8, P14, P15, P16, P18, P19, P21, P22 and P23;

  • Four time points: T0, M1, M2 & M6;

  • One treatment: anti-PD1;

  • Four fractions: PD-1+TIGIT+, PD-1+, TIGIT+ and PD-1-TIGIT-;

  • Two outcomes: NR and R;

  • Three batches.

QC have already been performed - please look at the TCR-QC section. Fourteen samples have been removed.

###--- Importation
#- TCR.pData
here("output", "TCR_pData.rds") %>%
  readRDS() -> TCR.pData
# pData (patient/fraction w/ at least T0 & M1)
TCR.pData %>% select(patient.id, time.point, fraction, outcome) %>% View("pData")
TCR.pData <- TCR.pData[c(1:39, 41:43, 45:120, 123:125, 127, 130, 132, 135:146, 148:154, 157:158), ] 
TCR.pData %>%
  arrange(patient.id) %>%
  select(patient.id, time.point, fraction, outcome) %>% 
  View("pData")

#- TCR.exprs
here("output", "TCR_count.rds") %>%
  readRDS() %>%
  filter(QIAGEN.id %in% TCR.pData$QIAGEN.id) -> TCR.exprs

2 Summary statistics

The clonality score is derived from the Shannon entropy, which is calculated from the frequencies of all productive sequences divided by the logarithm of the total number of unique productive sequences. This normalized entropy value is then inverted (1 - normalized entropy) to produce the clonality metric.
The Gini coefficient is an alternative metric used to calculate repertoire diversity.
Both Gini coefficient and clonality are reported on a scale from 0 to 1 where 0 indicates all sequences have the same frequency and 1 indicates the repertoire is dominated by a single sequence.

###--- Extracting productive sequences - aggregate samples having the same cdr3aa, cdr3nt and V/J-regions
TCR.exprs %>%
  group_by(QIAGEN.id, chain, cdr3nt, cdr3aa, `V-region`, `J-region`) %>%
  summarise(frequency = sum(freq.after.filtering), 
            `UMIs with >= 1 reads` = sum(`UMIs with >= 1 reads`),
            `UMIs with >= 2 reads` = sum(`UMIs with >= 2 reads`),
            `UMIs with >= 3 reads` = sum(`UMIs with >= 3 reads`),
            `UMIs with >= 4 reads` = sum(`UMIs with >= 4 reads`),
            `UMIs with >= 5 reads` = sum(`UMIs with >= 5 reads`),
            `UMIs with >= 6 reads` = sum(`UMIs with >= 6 reads`),
            `UMIs with >= 7 reads` = sum(`UMIs with >= 7 reads`),
            total.reads = sum(`# reads`),
            total.UMIs = sum(total.UMIs)) %>%
  arrange(QIAGEN.id, chain, desc(`UMIs with >= 1 reads`)) -> productive.aa

###--- Summary statistics
productive.aa %>%
  filter(chain == "TRAC" | chain == "TRBC") %>% 
  group_by(QIAGEN.id, chain) %>%
  summarise(total.reads = sum(total.reads),
            total.UMIs = sum(total.UMIs),
            unique.productive = n(),
            entropy = -sum(frequency * log2(frequency), na.rm = TRUE),
            gini.coef = ineq::Gini(frequency)) %>%
  mutate(clonality = 1 - round(entropy / log2(unique.productive), digits = 6)) -> dt
dt <- merge(dt, TCR.pData[, c("QIAGEN.id", "sample.number", "sample.id", "treatment", "batch", "patient.id", "time.point", "fraction.desc", "outcome")], by = "QIAGEN.id")

3 Clustering

Within each fraction, let"s look at sequences (both TRAC and TRBC). We declare as emerging, expanding, emerging-contracting, expanding-contracting, contracting-expanding, contracting sequences (regarding to M1) that are declared as significant (Fisher exact test to calculate differential abundance of each TRBC (or TRAC) between two time points –total.UMIs value is used) in the following contrasts T0 vs M1. Other sequences are declared as non-expanding/contracting.

3.1 PD-1+TIGIT+

3.1.1 Patient - P5

3.1.2 Patient - P6

3.1.3 Patient - P7

3.1.4 Patient - P8

3.1.5 Patient - P14

3.1.6 Patient - P15

3.1.7 Patient - P16

3.1.8 Patient - P18

3.1.9 Patient - P19

3.1.10 Patient - P21

3.1.11 Patient - P22

No PD-1+TIGIT+ sample (T0 & M1).

3.1.12 Patient - P23

No PD-1+TIGIT+ sample (T0 & M1).

3.2 PD-1+

3.2.1 Patient - P5

3.2.2 Patient - P6

No PD-1+ sample (T0 & M1).

3.2.3 Patient - P7

3.2.4 Patient - P8

3.2.5 Patient - P14

3.2.6 Patient - P15

3.2.7 Patient - P16

3.2.8 Patient - P18

3.2.9 Patient - P19

3.2.10 Patient - P21

3.2.11 Patient - P22

3.2.12 Patient - P23

No PD-1+ sample (T0 & M1).

3.3 TIGIT+

3.3.1 Patient - P5

3.3.2 Patient - P6

3.3.3 Patient - P7

3.3.4 Patient - P8

3.3.5 Patient - P14

3.3.6 Patient - P15

3.3.7 Patient - P16

3.3.8 Patient - P18

3.3.9 Patient - P19

3.3.10 Patient - P21

3.3.11 Patient - P22

No TIGIT+ sample (T0 & M1).

3.3.12 Patient - P23

3.4 PD-1-TIGIT-

3.4.1 Patient - P5

3.4.2 Patient - P6

3.4.3 Patient - P7

3.4.4 Patient - P8

3.4.5 Patient - P14

3.4.6 Patient - P15

3.4.7 Patient - P16

3.4.8 Patient - P18

3.4.9 Patient - P19

3.4.10 Patient - P21

3.4.11 Patient - P22

3.4.12 Patient - P23

No PD-1-TIGIT- sample (T0 & M1).

3.5 Summary

TRAC

Table - # of patients per fraction

ABCDEFGHIJ0123456789
fraction
<chr>
NR
<int>
R
<int>
PD-1-TIGIT-74
PD-1+64
PD-1+TIGIT+74
TIGIT+84
4 rows

Table - NR - # of patients per cluster

ABCDEFGHIJ0123456789
fraction
<chr>
emerging
<int>
expanding
<int>
contracting
<int>
non-expanding/contracting
<int>
PD-1-TIGIT-7777
PD-1+6366
PD-1+TIGIT+6566
TIGIT+7677
4 rows

Table - R - # of patients per cluster

ABCDEFGHIJ0123456789
fraction
<chr>
emerging
<int>
expanding
<int>
contracting
<int>
non-expanding/contracting
<int>
PD-1-TIGIT-4444
PD-1+4444
PD-1+TIGIT+4444
TIGIT+4444
4 rows

TRBC

Table - # of patients per fraction

ABCDEFGHIJ0123456789
fraction
<chr>
NR
<int>
R
<int>
PD-1-TIGIT-74
PD-1+64
PD-1+TIGIT+74
TIGIT+84
4 rows

Table - NR - # of patients per cluster

ABCDEFGHIJ0123456789
fraction
<chr>
emerging
<int>
expanding
<int>
contracting
<int>
non-expanding/contracting
<int>
PD-1-TIGIT-6666
PD-1+6666
PD-1+TIGIT+5555
TIGIT+6566
4 rows

Table - R - # of patients per cluster

ABCDEFGHIJ0123456789
fraction
<chr>
emerging
<int>
expanding
<int>
contracting
<int>
non-expanding/contracting
<int>
PD-1-TIGIT-4444
PD-1+4444
PD-1+TIGIT+4444
TIGIT+4444
4 rows

sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

Random number generation:
 RNG:     Mersenne-Twister 
 Normal:  Inversion 
 Sample:  Rounding 
 
locale:
[1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8

attached base packages:
[1] parallel  grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] broom_0.5.3          doMC_1.3.6           iterators_1.0.12    
 [4] foreach_1.4.7        ComplexHeatmap_2.2.0 gridExtra_2.3       
 [7] VennDiagram_1.6.20   futile.logger_1.4.3  here_0.1            
[10] data.table_1.12.8    janitor_1.2.0        forcats_0.4.0       
[13] stringr_1.4.0        dplyr_0.8.3          purrr_0.3.3         
[16] readr_1.3.1          tidyr_1.0.0          tibble_2.1.3        
[19] ggplot2_3.2.1        tidyverse_1.3.0     

loaded via a namespace (and not attached):
 [1] nlme_3.1-143         fs_1.3.1             lubridate_1.7.4     
 [4] RColorBrewer_1.1-2   httr_1.4.1           rprojroot_1.3-2     
 [7] tools_3.6.2          backports_1.1.5      R6_2.4.1            
[10] DBI_1.1.0            lazyeval_0.2.2       colorspace_1.4-1    
[13] GetoptLong_0.1.8     withr_2.1.2          tidyselect_0.2.5    
[16] compiler_3.6.2       git2r_0.26.1         cli_2.0.1           
[19] rvest_0.3.5          formatR_1.7          xml2_1.2.2          
[22] labeling_0.3         scales_1.1.0         digest_0.6.23       
[25] rmarkdown_2.0        pkgconfig_2.0.3      htmltools_0.4.0     
[28] dbplyr_1.4.2         rlang_0.4.2          GlobalOptions_0.1.1 
[31] readxl_1.3.1         rstudioapi_0.10      farver_2.0.2        
[34] shape_1.4.4          generics_0.0.2       jsonlite_1.6        
[37] magrittr_1.5         Rcpp_1.0.3           munsell_0.5.0       
[40] fansi_0.4.1          lifecycle_0.1.0      stringi_1.4.5       
[43] whisker_0.4          yaml_2.2.0           plyr_1.8.5          
[46] promises_1.1.0       crayon_1.3.4         lattice_0.20-38     
[49] haven_2.2.0          cowplot_1.0.0        circlize_0.4.8      
[52] hms_0.5.3            zeallot_0.1.0        knitr_1.26          
[55] pillar_1.4.3         rjson_0.2.20         reshape2_1.4.3      
[58] codetools_0.2-16     futile.options_1.0.1 reprex_0.3.0        
[61] glue_1.3.1           evaluate_0.14        lambda.r_1.2.4      
[64] modelr_0.1.5         png_0.1-7            vctrs_0.2.1         
[67] httpuv_1.5.2         cellranger_1.1.0     gtable_0.3.0        
[70] clue_0.3-57          assertthat_0.2.1     xfun_0.12           
[73] later_1.0.0          viridisLite_0.3.0    ineq_0.2-13         
[76] workflowr_1.6.0      cluster_2.1.0