Data filtering

In many research cases, you could want to filter your data by metadata, clonotypes parameters or genes, so for this purpose, you can use the repFilter function.

Methods for filtering data

repFilter has 3 parameters: .method, .query and .match.

Due to the ambiguity of ways to extract required data, repFilter has the following methods:

  • .method = "by.meta" - filters data using information from meta data.

  • .method = "by.repertoire" or "by.rep" - filters data using information about repertoire statistics.

  • .method = "by.clonotype" or "by.cl" - filters data using information about clonotype sequences.

Parameter .query is a list specifying the condition for filtering. Elements in the list are conditions, names of elements - columns chosen for filtering. Names in elements should not be repeated in .query. If you need to write a complex condition, you can call the function multiple times.

Parameter .match denotes the type of match between condition and value to filter:

  • exact - looks for exact match.
  • substring- looks for substring.
  • startswith - looks for string starting with the some pattern

Parameter .match can be applied to both by.meta and by.clonotype

Load the package into the R environment:

For testing purposes we will use scdata and immdata datasets from Immunarch library. Load them by executing the following command:

data(immdata)

Look at meta data from immdata datasets:

#look at the metadata
immdata$meta
## # A tibble: 12 × 6
##    Sample  ID    Sex     Age Status Lane 
##    <chr>   <chr> <chr> <dbl> <chr>  <chr>
##  1 A2-i129 C1    M        11 C      A    
##  2 A2-i131 C2    M         9 C      A    
##  3 A2-i133 C4    M        16 C      A    
##  4 A2-i132 C3    F         6 C      A    
##  5 A4-i191 C8    F        22 C      B    
##  6 A4-i192 C9    F        24 C      B    
##  7 MS1     MS1   M        12 MS     C    
##  8 MS2     MS2   M        30 MS     C    
##  9 MS3     MS3   M         8 MS     C    
## 10 MS4     MS4   F        14 MS     C    
## 11 MS5     MS5   F        15 MS     C    
## 12 MS6     MS6   F        15 MS     C
#look at samples name in data
names(immdata$data)
##  [1] "A2-i129" "A2-i131" "A2-i133" "A2-i132" "A4-i191" "A4-i192" "MS1"    
##  [8] "MS2"     "MS3"     "MS4"     "MS5"     "MS6"

How to write conditions for filtering

Method by.meta

Example 1

Use include and exclude options to select a subset of samples or repertoires that match given filter criterion. These options accept strings.

Let’s filter your data by metadata immdata datasets when Status is C and look at new meta data:

repFilter(immdata, .method = "by.meta", .query = list(Status = include("C")))$meta
## # A tibble: 6 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A2-i129 C1    M        11 C      A    
## 2 A2-i131 C2    M         9 C      A    
## 3 A2-i133 C4    M        16 C      A    
## 4 A2-i132 C3    F         6 C      A    
## 5 A4-i191 C8    F        22 C      B    
## 6 A4-i192 C9    F        24 C      B

Note that we filter both metadata and data:

# filtered data
names(repFilter(immdata, .method = "by.meta", .query = list(Status = include("C")))$data)
## [1] "A2-i129" "A2-i131" "A2-i133" "A2-i132" "A4-i191" "A4-i192"

Filter out samples where Lane is not A:

repFilter(immdata, .method = "by.meta", .query = list(Lane = exclude("A")))$meta
## # A tibble: 8 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A4-i191 C8    F        22 C      B    
## 2 A4-i192 C9    F        24 C      B    
## 3 MS1     MS1   M        12 MS     C    
## 4 MS2     MS2   M        30 MS     C    
## 5 MS3     MS3   M         8 MS     C    
## 6 MS4     MS4   F        14 MS     C    
## 7 MS5     MS5   F        15 MS     C    
## 8 MS6     MS6   F        15 MS     C
names(repFilter(immdata, .method = "by.meta", .query = list(Lane = exclude("A")))$data)
## [1] "A4-i191" "A4-i192" "MS1"     "MS2"     "MS3"     "MS4"     "MS5"    
## [8] "MS6"

Filter for samples where Lane is B or C:

repFilter(immdata, .method = "by.meta", .query = list(Lane = include("B", "C")))$meta
## # A tibble: 8 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A4-i191 C8    F        22 C      B    
## 2 A4-i192 C9    F        24 C      B    
## 3 MS1     MS1   M        12 MS     C    
## 4 MS2     MS2   M        30 MS     C    
## 5 MS3     MS3   M         8 MS     C    
## 6 MS4     MS4   F        14 MS     C    
## 7 MS5     MS5   F        15 MS     C    
## 8 MS6     MS6   F        15 MS     C
names(repFilter(immdata, .method = "by.meta", .query = list(Lane = include("B", "C")))$data)
## [1] "A4-i191" "A4-i192" "MS1"     "MS2"     "MS3"     "MS4"     "MS5"    
## [8] "MS6"

Filter out samples where Lane is not A and not C:

repFilter(immdata, .method = "by.meta", .query = list(Lane = exclude("A", "C")))$meta
## # A tibble: 2 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A4-i191 C8    F        22 C      B    
## 2 A4-i192 C9    F        24 C      B
names(repFilter(immdata, .method = "by.meta", .query = list(Lane = exclude("A", "C")))$data)
## [1] "A4-i191" "A4-i192"

Example 2

Use interval, lessthan(from minus infinity to your value) or morethan(from your value to plus infinity) to define interval values in the filter statement. These options accept float. lessthan and morethan do not include borders and interval includes the left border and excludes the right border.

Filter for samples where Age is lower than 23:

repFilter(immdata, .method = "by.meta", .query = list(Age = lessthan(23)))$meta
## # A tibble: 10 × 6
##    Sample  ID    Sex     Age Status Lane 
##    <chr>   <chr> <chr> <dbl> <chr>  <chr>
##  1 A2-i129 C1    M        11 C      A    
##  2 A2-i131 C2    M         9 C      A    
##  3 A2-i133 C4    M        16 C      A    
##  4 A2-i132 C3    F         6 C      A    
##  5 A4-i191 C8    F        22 C      B    
##  6 MS1     MS1   M        12 MS     C    
##  7 MS3     MS3   M         8 MS     C    
##  8 MS4     MS4   F        14 MS     C    
##  9 MS5     MS5   F        15 MS     C    
## 10 MS6     MS6   F        15 MS     C
names(repFilter(immdata, .method = "by.meta", .query = list(Age = lessthan(23)))$data)
##  [1] "A2-i129" "A2-i131" "A2-i133" "A2-i132" "A4-i191" "MS1"     "MS3"    
##  [8] "MS4"     "MS5"     "MS6"

Filter for samples where Age is upper than 15:

repFilter(immdata, .method = "by.meta", .query = list(Age = morethan(15)))$meta
## # A tibble: 4 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A2-i133 C4    M        16 C      A    
## 2 A4-i191 C8    F        22 C      B    
## 3 A4-i192 C9    F        24 C      B    
## 4 MS2     MS2   M        30 MS     C
names(repFilter(immdata, .method = "by.meta", .query = list(Age = morethan(15)))$data)
## [1] "A2-i133" "A4-i191" "A4-i192" "MS2"

Filter for samples where Age is between 15 and 23:

repFilter(immdata, .method = "by.meta", .query = list(Age = interval(15, 23)))$meta
## # A tibble: 4 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A2-i133 C4    M        16 C      A    
## 2 A4-i191 C8    F        22 C      B    
## 3 MS5     MS5   F        15 MS     C    
## 4 MS6     MS6   F        15 MS     C
names(repFilter(immdata, .method = "by.meta", .query = list(Age = interval(15, 23)))$data)
## [1] "A2-i133" "A4-i191" "MS5"     "MS6"

You can also use multiple conditions. In this case, the function returns values that matches both of this conditions (logical AND operator)

Filter for samples where Age is between 15 and 23 and Lane is B :

repFilter(immdata, .method = "by.meta", .query = list(Age = interval(15, 23), Lane = include('B')))$meta
## # A tibble: 1 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A4-i191 C8    F        22 C      B
names(repFilter(immdata, .method = "by.meta", .query = list(Age = interval(15, 23), Lane = include('B')))$data)
## [1] "A4-i191"

Filter for samples where Age is between 15 and 23 and Lane is A or B:

repFilter(immdata, .method = "by.meta", .query = list(Age = interval(15, 23), Lane = include('A', 'B')))$meta
## # A tibble: 2 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A2-i133 C4    M        16 C      A    
## 2 A4-i191 C8    F        22 C      B
names(repFilter(immdata, .method = "by.meta", .query = list(Age = interval(15, 23), Lane = include('A', 'B')))$data)
## [1] "A2-i133" "A4-i191"

Method by.repertoire (short alias is by.rep)

Example 3

Filter for repertoires containing more than 60000 clonotypes:

repFilter(immdata, .method = "by.repertoire", .query = list(n_clonotypes = morethan(6000)))$meta
## # A tibble: 8 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A2-i129 C1    M        11 C      A    
## 2 A2-i131 C2    M         9 C      A    
## 3 A2-i133 C4    M        16 C      A    
## 4 A2-i132 C3    F         6 C      A    
## 5 MS2     MS2   M        30 MS     C    
## 6 MS3     MS3   M         8 MS     C    
## 7 MS4     MS4   F        14 MS     C    
## 8 MS6     MS6   F        15 MS     C
repFilter(immdata, .method = "by.rep", .query = list(n_clonotypes = morethan(6000)))$meta # Works both with by.rep and by.repertoire
## # A tibble: 8 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A2-i129 C1    M        11 C      A    
## 2 A2-i131 C2    M         9 C      A    
## 3 A2-i133 C4    M        16 C      A    
## 4 A2-i132 C3    F         6 C      A    
## 5 MS2     MS2   M        30 MS     C    
## 6 MS3     MS3   M         8 MS     C    
## 7 MS4     MS4   F        14 MS     C    
## 8 MS6     MS6   F        15 MS     C

Be careful, filtering by.repertoire or by.rep could also change a number of repertoires(samples) in your data:

names(repFilter(immdata, .method = "by.repertoire", .query = list(n_clonotypes = morethan(6000)))$data)
## [1] "A2-i129" "A2-i131" "A2-i133" "A2-i132" "MS2"     "MS3"     "MS4"    
## [8] "MS6"

Filter for repertoires containing less than 6000 clonotypes:

repFilter(immdata, .method = "by.repertoire", .query = list(n_clonotypes = lessthan(6000)))$meta
## # A tibble: 4 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A4-i191 C8    F        22 C      B    
## 2 A4-i192 C9    F        24 C      B    
## 3 MS1     MS1   M        12 MS     C    
## 4 MS5     MS5   F        15 MS     C
repFilter(immdata, .method = "by.rep", .query = list(n_clonotypes = lessthan(6000)))$meta # Works both with by.rep and by.repertoire
## # A tibble: 4 × 6
##   Sample  ID    Sex     Age Status Lane 
##   <chr>   <chr> <chr> <dbl> <chr>  <chr>
## 1 A4-i191 C8    F        22 C      B    
## 2 A4-i192 C9    F        24 C      B    
## 3 MS1     MS1   M        12 MS     C    
## 4 MS5     MS5   F        15 MS     C
names(repFilter(immdata, .method = "by.repertoire", .query = list(n_clonotypes = lessthan(6000)))$data)
## [1] "A4-i191" "A4-i192" "MS1"     "MS5"

Method by.clonotype (short alias isby.cl)

Example 4

Filter out all noncoding clonotypes from immdata. As you see, immdata dataset doesn’t contain any noncoding clonotypes:

repFilter(immdata, .method = "by.clonotype", .query = list(CDR3.aa = exclude("partial", "out_of_frame")))$meta
## # A tibble: 12 × 6
##    Sample  ID    Sex     Age Status Lane 
##    <chr>   <chr> <chr> <dbl> <chr>  <chr>
##  1 A2-i129 C1    M        11 C      A    
##  2 A2-i131 C2    M         9 C      A    
##  3 A2-i133 C4    M        16 C      A    
##  4 A2-i132 C3    F         6 C      A    
##  5 A4-i191 C8    F        22 C      B    
##  6 A4-i192 C9    F        24 C      B    
##  7 MS1     MS1   M        12 MS     C    
##  8 MS2     MS2   M        30 MS     C    
##  9 MS3     MS3   M         8 MS     C    
## 10 MS4     MS4   F        14 MS     C    
## 11 MS5     MS5   F        15 MS     C    
## 12 MS6     MS6   F        15 MS     C

Note that filtering by.clonotype or by.cl works within repertoire(sample). Repertoire(sample) could be removed from your data only in case if all clonotypes in sample do not satisfy the condition:

names(repFilter(immdata, .method = "by.clonotype", .query = list(CDR3.aa = exclude("partial", "out_of_frame")))$data)
##  [1] "A2-i129" "A2-i131" "A2-i133" "A2-i132" "A4-i191" "A4-i192" "MS1"    
##  [8] "MS2"     "MS3"     "MS4"     "MS5"     "MS6"

Filter out clonotypes that have only one clone:

repFilter(immdata, .method = "by.clonotype", .query = list(Clones = morethan(1)))$meta
## # A tibble: 12 × 6
##    Sample  ID    Sex     Age Status Lane 
##    <chr>   <chr> <chr> <dbl> <chr>  <chr>
##  1 A2-i129 C1    M        11 C      A    
##  2 A2-i131 C2    M         9 C      A    
##  3 A2-i133 C4    M        16 C      A    
##  4 A2-i132 C3    F         6 C      A    
##  5 A4-i191 C8    F        22 C      B    
##  6 A4-i192 C9    F        24 C      B    
##  7 MS1     MS1   M        12 MS     C    
##  8 MS2     MS2   M        30 MS     C    
##  9 MS3     MS3   M         8 MS     C    
## 10 MS4     MS4   F        14 MS     C    
## 11 MS5     MS5   F        15 MS     C    
## 12 MS6     MS6   F        15 MS     C
names(repFilter(immdata, .method = "by.clonotype", .query = list(Clones = morethan(1)))$data)
##  [1] "A2-i129" "A2-i131" "A2-i133" "A2-i132" "A4-i191" "A4-i192" "MS1"    
##  [8] "MS2"     "MS3"     "MS4"     "MS5"     "MS6"

Example 5

In method by.clonotype or by.cl, there is an extra argument .match. The .match argument can has the following values: - exact - looks for exact match in gene names - substring- looks for substring in gene names - startswith - looks for gene names starting with the same pattern

Filter out all clonotypes within samples with V gene ‘TRBV1’ or ‘TRGV11’

repFilter(immdata, .method = "by.clonotype", .query = list(V.name = exclude("TRBV1", "TRGV11")), .match="exact")

Filter out all clonotypes within samples where V gene name contains substrings ‘TRBV1’ or ‘TRGV11’

repFilter(immdata, .method = "by.clonotype", .query = list(V.name = exclude("TRBV1", "TRGV11")), .match="substring")

Filter out all clonotypes within samples where V gene name starts with ‘TRBV1’ or ‘TRGV11’

repFilter(immdata, .method = "by.clonotype", .query = list(V.name = exclude("TRBV1", "TRGV11")), .match="startswith")

Using repFilter function for repertoire data analysis

We will use scdata datasets from Immunarch library. Load them by executing the following command:

data(scdata)

Look at meta data from scdata datasets:

#look at the metadata
scdata$meta
## # A tibble: 1 × 1
##   Sample
##   <chr> 
## 1 flu
#look at samples name in data
names(scdata$data)
## [1] "flu"

repFiter can also work with single-cell data containing not only meta and ’data`, but also extra information, for example, about clusters:

repFilter(scdata, .method = "by.clonotype", .query = list(CDR3.aa = exclude("partial", "out_of_frame")))

Create a new dataset with cluster-specific immune repertoires (for more information see Single-cell tutorials):

scdata_cl <- select_clusters(scdata, scdata$bc_cluster, "Cluster")
scdata_cl$meta
## # A tibble: 3 × 3
##   Sample     Cluster.source Cluster
##   <chr>      <chr>          <chr>  
## 1 flu_Activ  flu            Activ  
## 2 flu_Memory flu            Memory 
## 3 flu_Naive  flu            Naive

And compare the number of clonotypes between clusters:

vis(repExplore(scdata_cl$data, .method = "volume"))

Compare J gene usage between three clusters:

sc_active <- geneUsage(repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Activ')))$data, "hs.trbj", .norm = T)
p1 <- vis(sc_active)
## Using Names as id variables
p1

sc_memory <- geneUsage(repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Memory')))$data, "hs.trbj", .norm = T)
p2 <- vis(sc_memory)
## Using Names as id variables
p2

sc_naive <- geneUsage(repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Naive')))$data, "hs.trbj", .norm = T)
p3 <- vis(sc_naive)
## Using Names as id variables
p3

Compare gene usage of IGHJ4 between three clusters:

scdata_active <- repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Activ')))
scdata_active <- repFilter(scdata_active, .method = "by.cl", .query = list(J.name = include('IGHJ4')), .match = 'substring')
sc_active <- geneUsage(scdata_active$data, "hs.trbj", .norm = T)
p1 <- vis(sc_active)
## Using Names as id variables
scdata_memory <- repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Memory')))
scdata_memory <- repFilter(scdata_memory, .method = "by.cl", .query = list(J.name = include('IGHJ4')), .match = 'substring')
sc_memory <- geneUsage(scdata_memory$data, "hs.trbj", .norm = T)
p2 <- vis(sc_memory)
## Using Names as id variables
scdata_naive <- repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Naive')))
scdata_naive <- repFilter(scdata_naive, .method = "by.cl", .query = list(J.name = include('IGHJ4')), .match = 'substring')
sc_naive <- geneUsage(scdata_naive$data, "hs.trbj", .norm = T)
p3 <- vis(sc_naive)
## Using Names as id variables
p1 + p2 + p3

Look at the coding clonotypes in each cluster. Note that there aren’t any noncoding clonotypes in the datasets:

scdata_active <- repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Activ')))
scdata_active <- repFilter(scdata_active, .method = "by.clonotype", .query = list(CDR3.aa = exclude("partial", "out_of_frame")))
exp_vol <- repExplore(scdata_active$data, .method = "volume")
p1 <- vis(exp_vol)

exp_vol <- repExplore(repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Activ')))$data, .method = "volume")
p2 <- vis(exp_vol)
p1+p2

scdata_memory <- repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Memory')))
scdata_memory <- repFilter(scdata_memory, .method = "by.clonotype", .query = list(CDR3.aa = exclude("partial", "out_of_frame")))
exp_vol <- repExplore(scdata_memory$data, .method = "volume")
p1 <- vis(exp_vol)

exp_vol <- repExplore(repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Memory')))$data, .method = "volume")
p2 <- vis(exp_vol)
p1+p2

scdata_naive <- repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Naive')))
scdata_naive <- repFilter(scdata_naive, .method = "by.clonotype", .query = list(CDR3.aa = exclude("partial", "out_of_frame")))
exp_vol <- repExplore(scdata_naive$data, .method = "volume")
p1 <- vis(exp_vol)

exp_vol <- repExplore(repFilter(scdata_cl, .method = "by.meta", .query = list(Cluster = include('Naive')))$data, .method = "volume")
p2 <- vis(exp_vol)
p1+p2