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LPRI

“LuProgRegu Index” (LPRI) is an algorithm to calculate an index to help lung cancer patients’ prognostic stratification from regulons

workflow.png

Installation

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

Example

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

# 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

Run LPRI with one line

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

Citation

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

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