Data Loading and Transformation
Source:vignettes/Data_Loading_and_Transformation.Rmd
Data_Loading_and_Transformation.RmdHow to load data
cyCONDOR provides an integrated function to prepare a
condor object (flow cytometry dataset) starting from the
input files in either .fcs or .csv format. All
files should be saved in a single directory which path should be stated
in data_path. The user can define the number of (cells) to
process from each file within ‘max_cell’. If the input type is
.csv the useCSV setting should be set to
TRUE. Is important to keep in mind that currently all files
in the data_path folder are loaded independently from which
are also included in the annotation table, this can induce slight
differences in auto-logicle transformation, to avoid this only include
in the data_path the files you plan to analyse.
For data transformation cyCONDOR provides different
options:
-
auto_logi: For HDFC and Spectral Flow data (recommended, auto-logicle transformation). This transformation gives good results also with cyTOF data, especially if you are experiencing a lot of noise witharcsinhdue to negative values. auto-logicle transformation is inherited from theCytofkitpackage Chen er al. 2016. -
clr: Recommended for CITE-seq data (centered log ratio transformation) -
arcsinh: arcsinh transformation with co-factor 5, common transformation for cyTOF data.
The last important piece to build a condor object is the
annotation table. The annotation table should contain all necessary
metadata used for analysis as well a column containing the names of the
input files and should be supplied as .csv file. The column
containing the file names should be stated in ‘filename_col’. Below an
exemplary metadata table is shown.
read.csv("../.test_files/metadata.csv")## filename sample_ID group batch
## 1 ID1.fcs ID1 ctrl Day1
## 2 ID2.fcs ID2 pat Day1
## 3 ID3.fcs ID3 ctrl Day2
## 4 ID4.fcs ID4 pat Day2
## 5 ID5.fcs ID5 ctrl Day2
## 6 ID6.fcs ID6 pat Day2
## 7 ID7.fcs ID7 ctrl Day3
## 8 ID8.fcs ID8 pat Day3
## 9 ID9.fcs ID9 ctrl Day3
## 10 ID10.fcs ID10 pat Day3Unwanted parameters that are not important for the downstream
analysis (e.g. Time) and should be removed can be listed in
remove_param. In the prep_fcd function we also
set a seed for reproducibility since the subsetting to
max_cell is otherwise randomized.
Session Info
info <- sessionInfo()
info## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] cyCONDOR_0.2.0
##
## loaded via a namespace (and not attached):
## [1] IRanges_2.34.1 Rmisc_1.5.1
## [3] urlchecker_1.0.1 nnet_7.3-19
## [5] CytoNorm_2.0.1 TH.data_1.1-2
## [7] vctrs_0.6.4 digest_0.6.33
## [9] png_0.1-8 shape_1.4.6
## [11] proxy_0.4-27 slingshot_2.8.0
## [13] ggrepel_0.9.4 parallelly_1.36.0
## [15] MASS_7.3-60 pkgdown_2.0.7
## [17] reshape2_1.4.4 httpuv_1.6.12
## [19] foreach_1.5.2 BiocGenerics_0.46.0
## [21] withr_2.5.1 ggrastr_1.0.2
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## [41] promises_1.2.1 scatterplot3d_0.3-44
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## [45] ps_1.7.5 rstudioapi_0.15.0
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## [67] S4Arrays_1.0.6 hms_1.1.3
## [69] glmnet_4.1-8 GenomicRanges_1.52.1
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## [77] lmtest_0.9-40 readr_2.1.4
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## [99] lubridate_1.9.3 devtools_2.4.5
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## [173] mvtnorm_1.2-3 lava_1.7.3
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## [177] cytolib_2.12.1 timechange_0.2.0
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## [189] bitops_1.0-7 ggplot2_3.4.4
## [191] Rtsne_0.16 FlowSOM_2.8.0
## [193] ranger_0.16.0 flowCore_2.12.2
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