“LuProgRegu Index” (LPRI) is an algorithm to calculate an index to help lung cancer patients’ prognostic stratification from regulons
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("Richard-Li-lab-team/LPRI")
if (!require("GSVA")) BiocManager::install("GSVA")
#> Loading required package: GSVA
library(LPRI)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# load data required for calculate the LPRI score
load(paste0(system.file("extdata", package = "LPRI"),"/sysdata.rda"))
# load gene expression profile and you can prepare you own
# here is the example data
load(paste0(system.file("extdata", package = "LPRI"),"/genes_expr.rda"))
genes_expr[1:5,1:5]
#> GSM773540 GSM773541 GSM773542 GSM773543 GSM773544
#> HES4 6.653416 3.903760 7.282034 6.859125 6.872071
#> AGRN 9.589176 9.128973 9.196498 9.129542 8.162682
#> TNFRSF18 7.050105 6.572753 6.458297 6.721626 6.705704
#> TNFRSF4 3.989532 6.951755 3.014087 2.670102 5.517663
#> B3GALT6 11.005699 10.292961 10.702499 11.034827 10.480836
Sample_risk <- runLPRI(genes_expr)
#> Estimating GSVA scores for 17 gene sets.
#> Estimating ECDFs with Gaussian kernels
#> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100%
head(Sample_risk)
#> Risk_subgroup LPRI CREB3 HOXC9 HDAC2 XBP1
#> GSM773540 High 0.842585 -0.03356979 -0.10717254 0.06598811 0.08407326
#> GSM773541 High 2.580867 0.19669976 0.06013602 0.42772266 0.34000403
#> GSM773542 High 2.728034 0.30574107 -0.32086922 0.34136444 0.34454039
#> GSM773543 High 2.826515 0.28676843 -0.06045523 0.31302579 0.30071139
#> GSM773544 High 2.045391 0.28791697 -0.13929613 0.22728829 0.29663632
#> GSM773545 High 1.229202 0.10160781 0.17080506 0.16388966 0.13756607
#> PRDM16 HLF PPARG MAFF SP6
#> GSM773540 0.57171297 0.276527524 -0.06492422 -0.05062570 0.3470341
#> GSM773541 -0.14624173 -0.234207315 0.67303760 -0.02290026 -0.3616213
#> GSM773542 -0.13816097 0.033455706 0.50275924 -0.02359293 0.2068804
#> GSM773543 0.22267371 -0.006837051 -0.22858743 -0.21025947 0.1623654
#> GSM773544 0.42919545 0.283416948 0.54718064 0.04403181 0.1752060
#> GSM773545 -0.05400268 -0.175578939 0.57609028 0.15665927 -0.1634231
#> MAFK TEAD4 FOSL1 KLF16 DLX2 NPAS2
#> GSM773540 0.18275834 0.26300801 0.2297406 0.17387206 -0.5288176 0.52114949
#> GSM773541 0.16603099 -0.45088472 0.3702004 0.33142587 0.3931181 0.02501231
#> GSM773542 0.16978301 0.09088876 0.4287179 0.26240536 -0.5611053 0.34594259
#> GSM773543 0.18912735 0.46882738 0.2747302 0.10538252 0.1779930 0.83411467
#> GSM773544 0.26789747 0.36253634 0.3230945 0.11306148 0.5930327 0.02346127
#> GSM773545 0.07047889 0.12370004 0.1644147 -0.08482584 -0.3206728 0.23042737
#> E2F7 WT1
#> GSM773540 -0.0005412282 -0.2429986
#> GSM773541 0.4890692755 -0.3750415
#> GSM773542 0.1416913390 -0.2521748
#> GSM773543 -0.1227983358 -0.4518235
#> GSM773544 -0.4820723840 -0.1682996
#> GSM773545 0.1210554985 -0.5395216
Please cite: Xiong, Y., Zhang, Y., Liu, N. et al. Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma. J Transl Med 21, 499 (2023). https://doi.org/10.1186/s12967-023-04331-z