mmi is an R package to conveniently fit and compare many models. It was developed in parallel to the work on the article "Natural variation in immune cell parameters is preferentially driven by genetic factors". The flow cytometry data, as well as the clinical/demographical data analyzed in that article are published within this package.
You can install mmi from github with:
install.packages("devtools")
devtools::install_github("JacobBergstedt/mmi")The flow cytometry and demographical data can be accessed after loading the library:
library(mmi)
facs
#> # A tibble: 816 x 167
#> SUBJID MFI_CD16_in_CD16hi_of… MFI_CD16_of_CD56hi_of… MFI_CD69_in_CD16h…
#> <int> <int> <dbl> <int>
#> 1 2 31186 1910 605
#> 2 3 32877 4283 794
#> 3 4 30851 4633 688
#> 4 5 27428 3261 620
#> 5 8 24202 3247 627
#> 6 9 35577 2037 594
#> 7 11 27207 2874 388
#> 8 13 12138 1348 419
#> 9 16 18179 2338 353
#> 10 19 15629 1639 381
#> # ... with 806 more rows, and 163 more variables:
#> # MFI_CD69_in_CD56hi.panel4 <int>, MFI_CD8a_in_CD16hi.panel4 <dbl>,
#> # MFI_CD8a_in_CD56hi.panel4 <int>, MFI_HLADR_in_CD16hi.panel4 <dbl>,
#> # MFI_HLADR_in_CD56hi.panel4 <dbl>, MFI_NKp46_of_NK_cells.panel4 <int>,
#> # MFI_of_CD69_in_CD69posCD16hi.panel4 <int>,
#> # MFI_of_CD69_in_CD69posCD56hi.panel4 <int>,
#> # MFI_of_CD69_in_CD8aposCD16hi.panel4 <dbl>,
#> # MFI_of_CD69_in_CD8aposCD56hi.panel4 <int>,
#> # MFI_of_CD69_in_HLADRposCD16hi.panel4 <dbl>,
#> # MFI_of_CD8a_in_CD69posCD16hi.panel4 <dbl>,
#> # MFI_of_CD8a_in_CD69posCD56hi.panel4 <dbl>,
#> # MFI_of_CD8a_in_CD8aposCD16hi.panel4 <int>,
#> # MFI_of_CD8a_in_CD8aposCD56hi.panel4 <int>,
#> # MFI_of_CD8a_in_HLADRposCD16hi.panel4 <dbl>,
#> # MFI_of_HLADR_in_CD69posCD16hi.panel4 <dbl>,
#> # MFI_of_HLADR_in_CD69posCD56hi.panel4 <dbl>,
#> # MFI_of_HLADR_in_CD8aposCD16hi.panel4 <dbl>,
#> # MFI_of_HLADR_in_CD8aposCD56hi.panel4 <dbl>,
#> # MFI_of_HLADR_in_HLADRposCD16hi.panel4 <int>,
#> # MFI_of_HLADR_in_HLADRposCD56hi.panel4 <int>, N_CD16hi.panel4 <int>,
#> # N_CD56hi.panel4 <int>, N_CD69posCD16hi.panel4 <int>,
#> # N_CD69posCD56hi.panel4 <int>, N_CD8aposCD16hi.panel4 <int>,
#> # N_CD8aposCD56hi.panel4 <int>, N_HLADRposCD56hi.panel4 <int>,
#> # N_NK_cells.panel4 <int>, ratio_CD16hi_CD56hi.panel4 <dbl>,
#> # ratio_MFI_CD16_in_CD16hi_and_MFI_CD16_inCD56hi.panel4 <dbl>,
#> # MFI_CD16_in_CD14hi_mono.panel5 <int>,
#> # MFI_CD16_in_CD16hi_mono.panel5 <int>, N_CD14hi_mono.panel5 <int>,
#> # N_CD16hi_mono.panel5 <int>, N_mono.panel5 <int>,
#> # N_CD45pos.panel5 <dbl>, N_total.panel5 <dbl>,
#> # MFI_BASOPHILS_CD16.panel7 <dbl>, MFI_BASOPHILS_CD203c.panel7 <dbl>,
#> # MFI_BASOPHILS_CD32.panel7 <int>, MFI_BASOPHILS_FceRI.panel7 <int>,
#> # MFI_EOSINOPHILS_CD16.panel7 <int>, MFI_EOSINOPHILS_CD62L.panel7 <int>,
#> # MFI_EOSINOPHILS_FceRI.panel7 <int>, MFI_NEUTROPHILS_CD16.panel7 <dbl>,
#> # MFI_NEUTROPHILS_CD62L.panel7 <int>,
#> # MFI_NEUTROPHILS_FceRI.panel7 <dbl>, N_VIABLE_BASOPHILS.panel7 <int>,
#> # N_VIABLE_EOSINOPHILS.panel7 <int>, N_VIABLE_NEUTROPHILS.panel7 <dbl>,
#> # CD86_MFI_in_CD14hi.panel8 <int>, CD86_MFI_in_cDC1.panel8 <dbl>,
#> # CD86_MFI_in_cDC3.panel8 <dbl>, CD86_MFI_in_pDC.panel8 <dbl>,
#> # HLADR_MFI_in_CD14hi.panel8 <int>, HLADR_MFI_in_cDC1.panel8 <int>,
#> # HLADR_MFI_in_cDC3.panel8 <int>, HLADR_MFI_in_pDC.panel8 <int>,
#> # N_cDC1.panel8 <int>, N_cDC3.panel8 <int>, N_pDC.panel8 <int>,
#> # N_ILC.panel10 <int>, N_ILC1.panel10 <int>, N_group2_ILC.panel10 <int>,
#> # N_CD56negCD117pos_LTiandILC3.panel10 <int>, N_CD56_ILC.panel10 <int>,
#> # MFI_of_CD127_of_ILC1.panel10 <int>,
#> # MFI_of_CD161_of_ILC1.panel10 <dbl>,
#> # MFI_of_CD161_of_CD56negCD117pos_LTiandILC3.panel10 <int>,
#> # N_CD161pos_CD56negCD117pos_LTiandILC3.panel10 <int>,
#> # N_CD56posNKp44neg_group1and3_ILC.panel10 <int>,
#> # N_NCRpos_ILC3.panel10 <int>, N_CD8bnegCD4neg.panel1 <int>,
#> # MFI_CCR7_in_CD4pos_CM.panel1 <dbl>,
#> # MFI_CCR7_in_CD4pos_EM.panel1 <dbl>,
#> # MFI_CCR7_oin_CD4pos_EMRA.panel1 <dbl>,
#> # MFI_CCR7_in_CD4_naive.panel1 <int>,
#> # MFI_CCR7_in_CD8bpos_CM.panel1 <dbl>,
#> # MFI_CCR7_in_CD8bpos_EM.panel1 <int>,
#> # MFI_CCR7_in_CD8bpos_EMRA.panel1 <int>,
#> # MFI_CCR7_in_CD8bpos_naive.panel1 <int>, N_CD3pos.panel1 <dbl>,
#> # N_CD4pos.panel1 <dbl>, N_CD4pos_CM.panel1 <int>,
#> # N_CD4pos_EM.panel1 <int>, N_CD4pos_naive.panel1 <int>,
#> # N_CD8bpos.panel1 <int>, N_CD8bpos_CM.panel1 <int>,
#> # N_CD8bpos_EM.panel1 <int>, N_CD8bpos_EMRA.panel1 <int>,
#> # N_CD8bpos_naive.panel1 <int>, N_HLADRpos_in_CD4pos_CM.panel1 <int>,
#> # N_HLADRpos_in_CD4pos_EM.panel1 <int>,
#> # N_HLADRpos_in_CD8bpos_CM.panel1 <int>,
#> # N_HLADRpos_in_CD8bpos_EM.panel1 <int>, ratio_CD4_CD8.panel1 <dbl>,
#> # N_CD4pos_EMRA.panel1 <int>, N_HLADRpos_in_CD4pos_EMRA.panel1 <int>, …
ecrf
#> # A tibble: 816 x 43
#> Age OwnsHouse PhysicalActivity Sex LivesWithPartner LivesWithKids
#> <dbl> <fct> <dbl> <fct> <fct> <fct>
#> 1 22.3 Yes 3 Female No No
#> 2 28.8 Yes 0 Female Yes No
#> 3 23.7 Yes 0 Female Yes No
#> 4 21.2 No 0.5 Female No No
#> 5 26.2 Yes 1.5 Female No No
#> 6 23.8 Yes 0 Female Yes No
#> 7 26.4 No 4 Female Yes No
#> 8 21.7 Yes 0 Female No No
#> 9 26.5 No 0 Female Yes No
#> 10 23.2 Yes 1.5 Female No No
#> # ... with 806 more rows, and 37 more variables: BornInCity <fct>,
#> # Inbreeding <dbl>, BMI <dbl>, CMVPositiveSerology <fct>, FluIgG <dbl>,
#> # MetabolicScore <dbl>, LowAppetite <dbl>, TroubleConcentrating <dbl>,
#> # TroubleSleeping <dbl>, HoursOfSleep <dbl>, Listless <dbl>,
#> # UsesCannabis <fct>, RecentPersonalCrisis <fct>, Smoking <fct>,
#> # Employed <fct>, Education <fct>, DustExposure <fct>, Income <fct>,
#> # HadMeasles <fct>, HadRubella <fct>, HadChickenPox <fct>,
#> # HadMumps <fct>, HadTonsillectomy <fct>, HadAppendicectomy <fct>,
#> # VaccineHepA <fct>, VaccineMMR <fct>, VaccineTyphoid <fct>,
#> # VaccineWhoopingCough <fct>, VaccineYellowFever <fct>,
#> # VaccineHepB <fct>, VaccineFlu <fct>, SUBJID <int>,
#> # DepressionScore <dbl>, HeartRate <dbl>, Temperature <dbl>,
#> # HourOfSampling <dbl>, DayOfSampling <fct>