Input / output

The package provides several IO functions:

repLoad detects the input file format automatically. immunarch currently support the following immune repertoire data formats:

These parsers will be available soon.

Please contact us if there are more file formats you want to be supported.

For parsing IgBLAST results process the data with MigMap first.

You can load the data either from a single file, a list of repertoire file paths or from a folder with repertoire files. A single file can be loaded as follows:

# To load the data from a single file (note that you don't need to specify the data format):
file_path = paste0(system.file(package="immunarch"), "/extdata/io/Sample1.tsv.gz")
immdata <- repLoad(file_path)

In other cases you may want to provide a metadata file and locate it in the folder. It is necessary to name it exactly “metadata.txt”.

# For instance you have a following structure in your folder:
# >_ ls
# immunoseq1.txt
# immunoseq2.txt
# immunoseq3.txt
# metadata.txt

With the metadata repLoad will create a list in the environment with 2 elements, namely data and meta. All the data will be accessible simply from immdata$data.

Otherwise repLoad will create a dummy metadata file with only sample names.

# To load the whole folder with every file in it type:
file_path = paste0(system.file(package="immunarch"), "/extdata/io/")
immdata <- repLoad(file_path)
print(names(immdata))

# In order to do that your folder must contain metadata file named
# exactly "metadata.txt".

# In R, when you load your data:
# > immdata <- repLoad("path/to/your/folder/")
# > names(immdata)
# [1] "data" "meta"

# Suppose you do not have "metadata.txt":
# > immdata <- repLoad("path/to/your/folder/")
# > names(immdata)
# [1] "data" "meta"

Dummy metadata data frame look like this:

as_tibble(data.frame(Sample = c("immunoseq1", "immunoseq2", "immunoseq3"), stringsAsFactors = F))
## # A tibble: 3 x 1
##   Sample    
##   <chr>     
## 1 immunoseq1
## 2 immunoseq2
## 3 immunoseq3

The metadata file “metadata.txt” has to be tab delimited file with first column named “Sample” and any number of additional columns with arbitrary names. The first column should contain base names of files without extensions in your folder.

Sample Sex Age Status
immunoseq_1 M 1 C
immunoseq_2 M 2 C
immunoseq_3 F 3 A

In order to import data from the external databases you have to create a connection to this database and then load the data. Make sure that the table format in your database matches the immunarch’s format.

To illustrate the use of external database, here is an example demonstrating data loading to the local MonetDB database:

# Your list of repertoires in immunarch's format
DATA
# Metadata data frame
META

# Create a temporary directory
dbdir = tempdir()

# Create a DBI connection to MonetDB in the temporary directory.
con = DBI::dbConnect(MonetDBLite::MonetDBLite(), embedded = dbdir)

# Write each repertoire to MonetDB. Each table has corresponding name from the DATA
for (i in 1:length(DATA)) {
  DBI::dbWriteTable(con, names(DATA)[i], DATA[[i]], overwrite=TRUE)
}

# Create a source in the temporary directory with MonetDB
ms = MonetDBLite::src_monetdblite(dbdir = dbdir)
res_db = list()

# Load the data from MonetDB to dplyr tables
for (i in 1:length(DATA)) {
  res_db[[names(DATA)[i]]] = dplyr::tbl(ms, names(DATA)[i])
}

# Your data is ready to use
list(data = res_db, meta = META)

immunarch is compatible with following sources:

Basic data manipulations with dplyr and immunarch

You can find the introduction to dplyr here: https://CRAN.R-project.org/package=dplyr/vignettes/dplyr.html

Get the most abundant clonotypes

The function returns the most abundant clonotypes for the given repertoire:

top(immdata$data[[1]])
## # A tibble: 10 x 15
##    Clones Proportion CDR3.nt    CDR3.aa V.name D.name J.name V.end D.start D.end
##     <dbl>      <dbl> <chr>      <chr>   <chr>  <chr>  <chr>  <int>   <int> <int>
##  1    173    0.0204  TGCGCCAGC… CASSQE… TRBV4… TRBD1  TRBJ2…    16      18    26
##  2    163    0.0192  TGCGCCAGC… CASSYR… TRBV4… TRBD1  TRBJ2…    11      13    18
##  3     66    0.00776 TGTGCCACC… CATSTN… TRBV15 TRBD1  TRBJ2…    11      16    22
##  4     54    0.00635 TGTGCCACC… CATSIG… TRBV15 TRBD2  TRBJ2…    11      19    25
##  5     48    0.00565 TGTGCCAGC… CASSPW… TRBV27 TRBD1  TRBJ1…    11      16    23
##  6     48    0.00565 TGCGCCAGC… CASQGD… TRBV4… TRBD1  TRBJ1…     8      13    19
##  7     40    0.00471 TGCGCCAGC… CASSQD… TRBV4… TRBD1  TRBJ2…    16      21    26
##  8     31    0.00365 TGTGCCAGC… CASSEE… TRBV2  TRBD1  TRBJ1…    15      17    20
##  9     30    0.00353 TGCGCCAGC… CASSQP… TRBV4… TRBD1  TRBJ2…    14      23    28
## 10     28    0.00329 TGTGCCAGC… CASSWV… TRBV6… TRBD1  TRBJ2…    12      20    25
## # … with 5 more variables: J.start <int>, VJ.ins <dbl>, VD.ins <dbl>,
## #   DJ.ins <dbl>, Sequence <lgl>

Filter functional / non-functional / in-frame / out-of-frame clonotypes

Conveniently, functions are vectorised over the list of data frames; and coding(immdata$data) in the example below returns a list of data frames with coding sequences:

coding(immdata$data[[1]])

The next one operates in a similar fashion:

noncoding(immdata$data[[1]])

Now, the computation of the number of filtered sequences is straightforward:

nrow(inframes(immdata$data[[1]]))

And for the out-of-frame clonotypes:

nrow(outofframes(immdata$data[[1]]))

Get subset of clonotypes with a specific V gene

It is simple to subset data frame according to labels in the specified index. In the example the resulting data frame contains only records with ‘TRBV10-1’ V gene:

filter(immdata$data[[1]], V.name == 'TRBV10-1')
## # A tibble: 24 x 15
##    Clones Proportion CDR3.nt    CDR3.aa V.name D.name J.name V.end D.start D.end
##     <dbl>      <dbl> <chr>      <chr>   <chr>  <chr>  <chr>  <int>   <int> <int>
##  1      2   0.000235 TGCGCCAGC… CASSES… TRBV1… TRBD2  TRBJ2…    16      20    25
##  2      2   0.000235 TGCGCCAGC… CASSDG… TRBV1… TRBD1  TRBJ2…    13      15    22
##  3      1   0.000118 TGCGCCAGC… CASSGD… TRBV1… TRBD2  TRBJ2…     8      10    15
##  4      1   0.000118 TGCGCCACC… CATLRS… TRBV1… TRBD1  TRBJ2…     6       7     9
##  5      1   0.000118 TGCGCCAGC… CASSES… TRBV1… TRBD2  TRBJ2…    16      20    22
##  6      1   0.000118 TGCGCCAGC… CASSES… TRBV1… TRBD2  TRBJ2…    16      17    21
##  7      1   0.000118 TGCGCCAGC… CASRAS… TRBV1… TRBD2  TRBJ2…    10      13    21
##  8      1   0.000118 TGCGCCAGC… CASRRD… TRBV1… TRBD1  TRBJ2…     8      13    19
##  9      1   0.000118 TGCGCCAGC… CASSEV… TRBV1… TRBD1  TRBJ2…    14      19    24
## 10      1   0.000118 TGCGCCAGC… CASSEG… TRBV1… TRBD2  TRBJ2…    13      19    27
## # … with 14 more rows, and 5 more variables: J.start <int>, VJ.ins <dbl>,
## #   VD.ins <dbl>, DJ.ins <dbl>, Sequence <lgl>

Downsampling

ds = repSample(immdata$data, "downsample", 100)
sapply(ds, nrow)
## A2-i129 A2-i131 A2-i133 A2-i132 A4-i191 A4-i192     MS1     MS2     MS3     MS4 
##      98      97      91      99      91      94      82      99      88     100 
##     MS5     MS6 
##      87      99
ds = repSample(immdata$data, "sample", .n = 10)
sapply(ds, nrow)
## A2-i129 A2-i131 A2-i133 A2-i132 A4-i191 A4-i192     MS1     MS2     MS3     MS4 
##      10      10      10      10      10      10      10      10      10      10 
##     MS5     MS6 
##      10      10

Immunarch data format

immunarch comes with its own data format, including tab-delimited columns that can be specified as follows: