do.merge.files - Function to merge a list of data.tables (one data.table per 'sample') into a single large data.table.

Usage

do.merge.files(dat, remove.duplicates)

do.merge.files(dat, remove.duplicates = TRUE)

Arguments

dat: NO DEFAULT. List of data.tables (or data.frames)
remove.duplicates: DEFAULT = TRUE. Do you want to remove duplicates?

Value

Returns a combined data.table.

References

https://sydneycytometry.org.au/spectre.

Author

Thomas M Ashhurst, thomas.ashhurst@sydney.edu.au

Examples

data.list <- list(
"01_Mock_01" = Spectre::demo.clustered[Sample == '01_Mock_01'],
"02_Mock_02" = Spectre::demo.clustered[Sample == '02_Mock_02']
)
cell.dat <- do.merge.files(dat = data.list, remove.duplicates = TRUE)
head(cell.dat)
#>           FileName     NK11        CD3     CD45       Ly6G    CD11b      B220
#>             <char>    <num>      <num>    <num>      <num>    <num>     <num>
#> 1: CNS_Mock_01.csv  42.3719  40.098700  6885.08  -344.7830 14787.30  -40.2399
#> 2: CNS_Mock_01.csv  42.9586 119.014000  1780.29  -429.6650  5665.73   86.6673
#> 3: CNS_Mock_01.csv  59.2366 206.238000 10248.30 -1603.8400 19894.30  427.8310
#> 4: CNS_Mock_01.csv 364.9480  -0.233878  3740.04  -815.9800  9509.43  182.4200
#> 5: CNS_Mock_01.csv 440.2470  40.035200  9191.38    40.5055  5745.82 -211.6940
#> 6: CNS_Mock_01.csv 151.5890 124.525000  4256.17  -596.1300 12200.80   94.0770
#>        CD8a      Ly6C     CD4 NK11_asinh    CD3_asinh CD45_asinh  Ly6G_asinh
#>       <num>     <num>   <num>      <num>        <num>      <num>       <num>
#> 1:  83.7175  958.7000 711.072 0.04235923  0.040087962   2.627736 -0.33829345
#> 2:  34.7219  448.2590 307.272 0.04294540  0.118734817   1.340828 -0.41743573
#> 3: 285.8800 1008.8300 707.094 0.05920201  0.204803270   3.022631 -1.25101677
#> 4: 333.6050  440.0710 249.784 0.35729716 -0.000233878   2.029655 -0.74509796
#> 5: 149.2200   87.4815 867.570 0.42713953  0.040024513   2.914359  0.04049443
#> 6: 109.3110  417.4010 352.982 0.15101436  0.124205401   2.155040 -0.56550357
#>    CD11b_asinh  B220_asinh CD8a_asinh Ly6C_asinh CD4_asinh     Sample  Group
#>          <num>       <num>      <num>      <num>     <num>     <char> <char>
#> 1:    3.388057 -0.04022905 0.08362002  0.8518665 0.6617135 01_Mock_01   Mock
#> 2:    2.435282  0.08655917 0.03471493  0.4344615 0.3026313 01_Mock_01   Mock
#> 3:    3.684212  0.41575012 0.28212257  0.8876036 0.6584685 01_Mock_01   Mock
#> 4:    2.948184  0.18142312 0.32770787  0.4269784 0.2472569 01_Mock_01   Mock
#> 5:    2.449108 -0.21014391 0.14867171  0.0873703 0.7845668 01_Mock_01   Mock
#> 6:    3.196324  0.09393878 0.10909447  0.4061429 0.3460348 01_Mock_01   Mock
#>     Batch FlowSOM_cluster FlowSOM_metacluster Population     UMAP_X   UMAP_Y
#>    <char>           <num>              <fctr>     <char>      <num>    <num>
#> 1:      A              23                   2  Microglia -2.3603757 6.201213
#> 2:      A              55                   2  Microglia  2.7505242 7.119595
#> 3:      A              64                   2  Microglia -2.9486033 4.012670
#> 4:      A              53                   2  Microglia  0.6482904 6.481466
#> 5:      A             110                   4   NK cells -2.3941295 6.975885
#> 6:      A              24                   2  Microglia -0.4012698 6.679605