chenli 2024/11/8
FAScore is a machine learning framework for predicting functional alternative splicing (AS) events within biological processes, such as hematopoietic differentiation. It integrates dynamic expression patterns of AS events and their host genes, along with sequence conservation and structural details of the isoforms, to compute a functional score via Random Forest (RF) modeling. Additionally, FAScore uses a Gaussian Mixture Model (GMM) to classify AS events into functional categories.
The dynamic features used in FAScore include expression breadth (R), expression specificity (τ), the expression relationship (Spearman’s rank coefficient of correlation ρ) and p value (Pρ), expression gradient (the linear fitting slope β) and p value (Pβ). The dynamic score (DyScore) for genes or AS events is defined as the mean of these scaled dynamic features, normalized to a range from 0 to 1. Consequently, DyScore values range from -1 to 1, where positive values indicate up-regulation, and negative values indicate down-regulation. The structural features, sourced from the APPRIS database, include cross-species sequence conservation score, the functionally important residues score, domain integrity score, trans-membrane helices score, signal peptide score, subcellular location score, and the structural homologs and integrity score. The final outputs of FAScore are the dynamic score (DyScore) and functional score (FAScore) for all annotated AS events.
The package support the multiple species including human, mouse, zebrafish, rat, pig, chimpanzee, chicken, cow, macaque, fruitfly, and elegans. But if you only calculate the DyScore, you can use this package in any species. The following figure shows the workflow of FAScore.
Installation
Input
Output
Tutorial
SessionInfo
Contact
Citation
FAScore has been developed with R 4.0.0 and the following packages are
needed to be installed in R (R scripts in FAScore automatically check
to see if an R library is already installed and then install those that
are needed. So no need for manual preinstallation!):
randomForest (>= 4.0),
SummarizedExperiment (>= 1.18),
dplyr (>= 1.0),
GenomicRanges (>= 1.0),
GenomicFeatures (>= 1.0),
ggplot2 (>= 3.0),
ggrepel (>= 0.9),
methods (>= 4.0),
rtracklayer (>= 1.0),
stringr (>= 1.0),
tidyr (>= 1.0),
IRanges (>= 2.0),
stats (>= 4.0),
reshape2 (>= 1.0),
parallel (>= 4.0),
S4Vectors (>= 0.26),
cowplot (>= 1.0)
mclust (>= 5.0)
To install FAScore, you have two options: either install directly from GitHub or use the compressed source file:
# Install from GitHub if remotes package is not installed
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("LuChenLab/DEMINERS/DecodeR")Alternatively, you can install FAScore using the source file downloaded from the repository :
# Install DecodeR from a downloaded source file
R CMD INSTALL FAScore_0.2.0.tar.gz
A data frame with alternative splicing (AS) events or splice junctions (SJs) as rows and sample IDs as columns. The percent spliced in (PSI) values are calculated using tools like rMAST or ICAS, among others.
A data frame with gene IDs as rows and sample IDs as columns, containing normalized gene expression counts. The columns should match those in the PSI data frame.
A data frame with transcript IDs as rows and sample IDs as columns, containing normalized isoform expression counts (e.g., from RSEM). The columns should be consistent with those in the PSI data frame.
The file path to a gene transfer format (GTF) file, which contains genome annotation details for AS (SJs), isoforms, and gene loci. GTF files specific to various species can be obtained from the Ensembl database.
A data frame with sample IDs and group variables in the colData, where rows correspond to columns in the PSI, gene expression, and isoform expression data frames. The group variable must contain at least three distinct classes.
A data frame with structural-related scores such as cross-species
sequence conservation score (Conservation), the functionally important
residues score (AA.residues), domain integrity score (Domain.integrity),
the structural homologs and integrity score (Protein3D), trans-membrane
helices score (Transmembrane), signal peptide score (SignalP), and
subcellular location score (TargetP). Files for species beyond
human(GRCh38), rhesus macaque(Mmul10), rat(Rnor6.0),
mouse(GRCm38), and zebrafish(GRCz11) can be downloaded from the
APPRIS database.
The output will be an S4 object containing the following slots:
assays: A SimpleList of matrix-like objects, including the PSI matrix of AS events derived from the input.colData: An optional DataFrame providing information on the samples, as specified in the input.rowRanges: A GRanges object describing the ranges of AS events and corresponding gene and isoform IDs. It includes columns for ASID, TranscriptID, GeneID, GeneName and matchTransID. The matchTransID column stores the best-matching transcript for each gene based on maximum expression values (fromISOFORMdata), aiding in mapping isoform structural information.GENE: A data frame of gene expression values provided in the input.ISOFORM: A data frame of isoform expression values provided in the input.Correlation: A list containing the Spearman’s rank correlation coefficient (ρ) and corresponding p-value (Pρ) for genes or AS events.Tau: A list containing the specificity index (τ) for genes and AS events, which quantifies the specificity of expression profiles. Higher values indicate greater specificity.Range: A list containing range value (R) for genes and AS events, representing the breadth of expression.Linear: A list containing linear fit parameters (intercept, slope (β), r2), and the p-value (Pβ) for the linear fit of genes and AS events, representing the expression gradient.DyScore: A list capturing dynamic scores for genes and AS events.Structure: A data frame of isoform structural scores obtained from the APPRIS database.RFpredict: The predicted scores and classification results from the pre-trained model, stored in the FAScore and FAStype columns.GMM: The parameter result of gaussian mixture model (GMM).
For detailed usage of parameters of functions, please help() or ?.
Loading example data set.
library(FAScore)
library(dplyr)
data(ExDataSet)head(ExDataSet$AS)## LT-HSC_1 LT-HSC_2 ST-HSC_1 ST-HSC_2
## 1:4774517-4776409:- 1.485149 1.298701 7.1428571 5.9523810
## 1:4774517-4777524:- 98.514851 98.701299 92.8571429 94.0476190
## 1:4782734-4783950:- 98.007590 97.067449 97.0588235 99.3775934
## 1:4782734-4785572:- 1.992410 2.932551 2.9411765 0.6224066
## 1:4828650-4830267:+ 99.189463 100.000000 99.6891192 99.2125984
## 1:4828650-4832310:+ 0.810537 0.000000 0.3108808 0.7874016
## MPP_1 MPP_2 CMP_1 CMP_2
## 1:4774517-4776409:- 3.1446541 5.1282051 3.7037037 5.8394161
## 1:4774517-4777524:- 96.8553459 94.8717949 96.2962963 94.1605839
## 1:4782734-4783950:- 98.2180294 97.3895582 98.0341880 96.0591133
## 1:4782734-4785572:- 1.7819706 2.6104418 1.9658120 3.9408867
## 1:4828650-4830267:+ 99.7979798 99.4094488 99.4541485 99.0825688
## 1:4828650-4832310:+ 0.2020202 0.5905512 0.5458515 0.9174312
## MEP_1 MEP_2 MK_1 MK_2
## 1:4774517-4776409:- 3.3519553 4.901961 2.0408163 10.2564103
## 1:4774517-4777524:- 96.6480447 95.098039 97.9591837 89.7435897
## 1:4782734-4783950:- 98.4264786 97.285714 98.1351981 98.7500000
## 1:4782734-4785572:- 1.5735214 2.714286 1.8648019 1.2500000
## 1:4828650-4830267:+ 99.1423671 98.679245 99.3036212 99.7732426
## 1:4828650-4832310:+ 0.8576329 1.320755 0.6963788 0.2267574
## EryA_1 EryA_2 EryB_1 EryB_2
## 1:4774517-4776409:- 3.4090909 1.6393443 13.333333 16.666667
## 1:4774517-4777524:- 96.5909091 98.3606557 86.666667 83.333333
## 1:4782734-4783950:- 97.8494624 99.6721311 98.648649 98.461538
## 1:4782734-4785572:- 2.1505376 0.3278689 1.351351 1.538462
## 1:4828650-4830267:+ 99.0291262 98.9510490 100.000000 100.000000
## 1:4828650-4832310:+ 0.9708738 1.0489510 0.000000 0.000000
head(ExDataSet$GENE)## LT-HSC_1 LT-HSC_2 ST-HSC_1 ST-HSC_2 MPP_1 MPP_2
## ENSMUSG00000033845 23.05 13.37 24.63 16.83 18.55 17.75
## ENSMUSG00000025903 25.01 16.63 27.51 20.29 23.53 20.05
## ENSMUSG00000104217 0.18 0.04 0.29 0.04 0.43 0.15
## ENSMUSG00000025903.1 25.01 16.63 27.51 20.29 23.53 20.05
## ENSMUSG00000104217.1 0.18 0.04 0.29 0.04 0.43 0.15
## ENSMUSG00000033813 40.78 30.87 43.12 33.82 34.04 32.50
## CMP_1 CMP_2 MEP_1 MEP_2 MK_1 MK_2 EryA_1
## ENSMUSG00000033845 17.00 14.88 31.98 24.67 7.66 12.33 9.95
## ENSMUSG00000025903 21.56 14.42 13.68 8.56 14.67 13.55 7.89
## ENSMUSG00000104217 0.15 0.04 0.07 0.14 0.05 0.15 0.06
## ENSMUSG00000025903.1 21.56 14.42 13.68 8.56 14.67 13.55 7.89
## ENSMUSG00000104217.1 0.15 0.04 0.07 0.14 0.05 0.15 0.06
## ENSMUSG00000033813 37.95 20.42 45.18 27.30 12.08 17.55 32.63
## EryA_2 EryB_1 EryB_2
## ENSMUSG00000033845 10.21 2.18 1.65
## ENSMUSG00000025903 9.41 1.58 1.52
## ENSMUSG00000104217 0.22 0.03 0.00
## ENSMUSG00000025903.1 9.41 1.58 1.52
## ENSMUSG00000104217.1 0.22 0.03 0.00
## ENSMUSG00000033813 45.86 24.17 21.21
head(ExDataSet$ISOFORM)## gene_id LT-HSC_1 LT-HSC_2 ST-HSC_1
## ENSMUST00000001166 ENSMUSG00000001138 5.13 4.83 5.08
## ENSMUST00000001171 ENSMUSG00000001143 0.68 2.06 1.78
## ENSMUST00000001172 ENSMUSG00000079610 1.07 0.43 1.00
## ENSMUST00000003219 ENSMUSG00000003135 8.00 6.05 8.05
## ENSMUST00000006037 ENSMUSG00000005886 6.71 5.55 6.82
## ENSMUST00000006462 ENSMUSG00000006299 32.24 22.48 36.18
## ST-HSC_2 MPP_1 MPP_2 CMP_1 CMP_2 MEP_1 MEP_2
## ENSMUST00000001166 5.49 4.10 4.73 3.71 2.80 2.63 2.19
## ENSMUST00000001171 1.52 0.97 1.46 0.56 0.78 0.21 0.26
## ENSMUST00000001172 0.69 1.27 0.62 0.76 0.73 0.69 0.44
## ENSMUST00000003219 6.85 6.38 6.81 5.82 3.67 5.99 3.72
## ENSMUST00000006037 5.47 5.55 4.64 5.13 3.25 3.59 2.65
## ENSMUST00000006462 28.38 29.68 26.82 24.80 18.09 25.18 19.33
## MK_1 MK_2 EryA_1 EryA_2 EryB_1 EryB_2
## ENSMUST00000001166 2.47 4.38 1.56 2.27 1.03 0.76
## ENSMUST00000001171 1.45 1.47 0.68 1.07 0.23 0.11
## ENSMUST00000001172 0.37 0.77 0.34 0.27 0.04 0.05
## ENSMUST00000003219 2.56 4.52 2.58 3.71 0.89 0.79
## ENSMUST00000006037 3.17 2.23 1.48 2.23 0.90 0.98
## ENSMUST00000006462 13.12 22.40 8.30 10.13 2.67 2.33
head(ExDataSet$meta)## Run CellType Lineage
## LT-HSC_1 LT-HSC_1 LT-HSC Stem
## LT-HSC_2 LT-HSC_2 LT-HSC Stem
## ST-HSC_1 ST-HSC_1 ST-HSC Stem
## ST-HSC_2 ST-HSC_2 ST-HSC Stem
## MPP_1 MPP_1 MPP Progenitor
## MPP_2 MPP_2 MPP Progenitor
The alternative splicing PSI matrix, gene expression matrix, transcripts expression matrix, and sample information table need to be prepared to create a FAScore object.
MyObj <- FAScoreDataSet(colData = ExDataSet$meta, AS = ExDataSet$AS,
GENE = ExDataSet$GENE, ISOFORM = ExDataSet$ISOFORM)MyObj## class: FAScore
## dim: 1000 16
## metadata(1): version
## assays(1): AS
## rownames(1000): 1:4774517-4776409:- 1:4774517-4777524:- ...
## 1:86583097-86587015:+ 1:86583097-86589163:+
## rowData names(0):
## colnames(16): LT-HSC_1 LT-HSC_2 ... EryB_1 EryB_2
## colData names(3): Run CellType Lineage
The dynamic score of genes or AS events is defined as the mean of the
scaled values (ranging from 0 to 1) of dynamic features calculated by
the CalcuFeature function. These features include Rscaled,
τ, ρ, Pρ, β and Pβ.
Rscaled represents the range values of TPM and PSI across all
stages or cell types per gene or AS event, scaled to a range between 0
and 1 by defining the maximum range (max range). The specificity index
τ quantifies the specificity of an expression profile for a particular
stage or cell type, ranging from 0 (housekeeping) to 1 (stage or cell
type-specific). ρ is the Spearman’s rank correlation coefficient, and
Pρ is the corresponding P-value (two. side t-test). If
the Pρ is significant, it is set to 1, otherwise to 0.
β is the slope of the linear fit based on TPM and PSI across all
stages or cell types per gene or AS event, scaled between -1 and 1.
Values greater than or equal to 1 are set to 1, and values less than or
equal to -1 are set to -1. Pβ is the p-value from the
F-statistic test during the linear fitting; if the p-value is
significant, it is set to 1, otherwise to 0. The absolute DyScore ranges
between 0 and 1, with the sign indicating the direction of the slope
(β). The dynamic score is calculated using the CalcuDyScore
function.
Preprocessing the related dynamic features
MyObj <- CalcuFeature(MyObj, group.by = "CellType", cores = 10)head(MyObj@Correlation$Gene) # head(MyObj@Correlation$AS)## Spearman.cor Spearman.p
## ENSMUSG00000033845 -0.6449817 0.0069804230
## ENSMUSG00000025903 -0.8698377 0.0000119491
## ENSMUSG00000104217 -0.3452381 0.1903171729
## ENSMUSG00000025903.1 -0.8698377 0.0000119491
## ENSMUSG00000104217.1 -0.3452381 0.1903171729
## ENSMUSG00000033813 -0.3372840 0.2014142966
head(MyObj@Tau$Gene) # head(MyObj@Tau$AS)## ENSMUSG00000033845 ENSMUSG00000025903 ENSMUSG00000104217
## 0.5207666 0.4260012 0.6403941
## ENSMUSG00000025903.1 ENSMUSG00000104217.1 ENSMUSG00000033813
## 0.4260012 0.6403941 0.2337695
head(MyObj@Range$Gene) # head(MyObj@Range$AS)## ENSMUSG00000033845 ENSMUSG00000025903 ENSMUSG00000104217
## 26.410 22.350 0.275
## ENSMUSG00000025903.1 ENSMUSG00000104217.1 ENSMUSG00000033813
## 22.350 0.275 24.430
head(MyObj@Linear$Gene) # head(MyObj@Linear$AS)## intercept slope r2 pvalue
## ENSMUSG00000033845 42.13620 -0.77794989 0.3170984 0.0231327141
## ENSMUSG00000025903 44.48707 -1.09057858 0.5728384 0.0006882753
## ENSMUSG00000104217 2.13947 -0.06570322 0.1216056 0.1855878360
## ENSMUSG00000025903.1 44.48707 -1.09057858 0.5728384 0.0006882753
## ENSMUSG00000104217.1 2.13947 -0.06570322 0.1216056 0.1855878360
## ENSMUSG00000033813 48.20751 -0.26908852 0.1446797 0.1461264931
Calculating the dynamic scores
MyObj <- CalcuDyScore(MyObj, maxRange = 10, maxSlope = 1, type = "Gene")MyObj <- CalcuDyScore(MyObj, maxRange = 30, maxSlope = 1, type = "AS")head(MyObj@DyScore$Gene)## ENSMUSG00000033845 ENSMUSG00000025903 ENSMUSG00000104217
## -0.8239497 -0.8826398 -0.1752226
## ENSMUSG00000025903.1 ENSMUSG00000104217.1 ENSMUSG00000033813
## -0.8826398 -0.1752226 -0.3066903
head(MyObj@DyScore$AS)## 1:4774517-4776409:- 1:4774517-4777524:- 1:4782734-4783950:-
## 0.29129371 -0.17977795 0.08980269
## 1:4782734-4785572:- 1:4828650-4830267:+ 1:4828650-4832310:+
## -0.15165456 -0.01005425 0.09792982
We use dynamic, conserved and structural features to train a predictive
model using the randomForest function (pre-trained model). The
functional scores and classes can be calculated using the CalcuFAScore
function, which applies either the pre-trained model or a custom model
of your choice.
Adding gene and transcript information for AS events. Note that
processing the HostGene may take a considerable amount of time if the
GTF file is large.
my_gtf <- system.file("extdata", "GRCm38.93_sub.gtf.gz", package = "FAScore", mustWork = TRUE)
MyObj <- ASmapIso(MyObj, gtf = my_gtf, AStype = "exonic", cores = 10)
MyObj <- ChooseIso(MyObj)Adding the structural scores of transcripts
MyObj <- matchAppris(MyObj, gtf = my_gtf, species = "MusMus")
head(MyObj@Structure)or other species and annotation versions downloaded from APPRIS database
MyObj <- matchAppris(MyObj, gtf = my_gtf, appris = OtherFile)
head(MyObj@Structure)Calculating the functional scores and classes
MyObj <- CalcuFAScore(MyObj)Sorted by FAScore
MyObj@RFpredict <- MyObj@RFpredict[order(MyObj@RFpredict$FAScore,decreasing = T),]
MyObj@RFpredict[,c(1,3,4,26,27)] %>% headThe plotDyScore function can rank the dynamic scores of genes and AS
events. You can label different classes of AS events, as defined by you,
along with their corresponding genes in the figure.
ASID = list(Func = (MyObj@RFpredict$AS %>% head), nonFunc = (MyObj@RFpredict$AS %>% tail))
label = paste0(c(head(MyObj@RFpredict$GeneName),tail(MyObj@RFpredict$GeneName)), "|", stringr::str_split_fixed(unlist(ASID), "[|]", 2)[,2] )plotDyScore(MyObj, ASID = ASID, label = label, color = c( "#FF4500", "#2E9FDF"), size = 18)The plotFAScore function can be used to rank the functional scores of
AS events, with the option to label specific events of interest.
plotFAScore(MyObj, ASID = ASID, label = label, color = c( "#FF4500", "#2E9FDF"), size = 18) sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
##
## 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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods
## [7] base
##
## other attached packages:
## [1] dplyr_1.1.4 FAScore_0.2.0 rmarkdown_2.9
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 shadowtext_0.0.8
## [3] backports_1.2.1 Hmisc_4.5-0
## [5] fastmatch_1.1-3 BiocFileCache_1.99.3
## [7] plyr_1.8.6 igraph_1.2.6
## [9] lazyeval_0.2.2 splines_4.1.2
## [11] BiocParallel_1.25.5 GenomeInfoDb_1.27.11
## [13] ggplot2_3.5.1 digest_0.6.27
## [15] yulab.utils_0.1.1 htmltools_0.5.1.1
## [17] GOSemSim_2.17.1 viridis_0.6.1
## [19] GO.db_3.12.1 fansi_0.5.0
## [21] magrittr_2.0.1 checkmate_2.0.0
## [23] memoise_2.0.0 cluster_2.1.2
## [25] openxlsx_4.2.4 Biostrings_2.59.2
## [27] graphlayouts_0.7.1 matrixStats_0.62.0
## [29] prettyunits_1.1.1 enrichplot_1.11.2
## [31] jpeg_0.1-9 colorspace_2.0-2
## [33] rappdirs_0.3.3 blob_1.2.2
## [35] ggrepel_0.9.4 haven_2.4.1
## [37] xfun_0.24 RCurl_1.98-1.3
## [39] crayon_1.4.1 jsonlite_1.7.2
## [41] scatterpie_0.1.6 survival_3.2-13
## [43] ape_5.5 glue_1.6.2
## [45] polyclip_1.10-0 gtable_0.3.0
## [47] zlibbioc_1.37.0 XVector_0.31.1
## [49] DelayedArray_0.17.10 car_3.0-11
## [51] BiocGenerics_0.37.1 abind_1.4-5
## [53] scales_1.3.0 DOSE_3.17.0
## [55] DBI_1.1.1 rstatix_0.7.0
## [57] Rcpp_1.0.12 progress_1.2.2
## [59] viridisLite_0.4.0 htmlTable_2.2.1
## [61] tidytree_0.4.6 mclust_5.4.9
## [63] foreign_0.8-81 bit_4.0.4
## [65] Formula_1.2-4 stats4_4.1.2
## [67] htmlwidgets_1.5.3 httr_1.4.2
## [69] fgsea_1.17.0 RColorBrewer_1.1-2
## [71] ellipsis_0.3.2 XML_3.99-0.6
## [73] pkgconfig_2.0.3 farver_2.1.0
## [75] dbplyr_2.1.1 nnet_7.3-16
## [77] utf8_1.2.2 tidyselect_1.2.0
## [79] rlang_1.1.2 reshape2_1.4.4
## [81] AnnotationDbi_1.53.1 munsell_0.5.0
## [83] cellranger_1.1.0 tools_4.1.2
## [85] cachem_1.0.5 downloader_0.4
## [87] cli_3.6.1 generics_0.1.0
## [89] RSQLite_2.2.7 broom_0.7.9
## [91] evaluate_0.14 stringr_1.5.1
## [93] fastmap_1.1.0 yaml_2.2.1
## [95] ggtree_3.10.0 knitr_1.33
## [97] bit64_4.0.5 fs_1.5.0
## [99] tidygraph_1.2.0 zip_2.2.0
## [101] randomForest_4.6-14 purrr_1.0.2
## [103] KEGGREST_1.31.1 ggraph_2.0.5
## [105] nlme_3.1-152 aplot_0.0.6
## [107] DO.db_2.9 biomaRt_2.47.7
## [109] compiler_4.1.2 rstudioapi_0.13
## [111] filelock_1.0.2 curl_4.3.2
## [113] png_0.1-7 ggsignif_0.6.4
## [115] treeio_1.15.6 tibble_3.2.1
## [117] tweenr_1.0.2 stringi_1.7.3
## [119] GenomicFeatures_1.43.8 forcats_0.5.1
## [121] lattice_0.20-45 Matrix_1.6-0
## [123] vctrs_0.6.4 pillar_1.9.0
## [125] lifecycle_1.0.3 BiocManager_1.30.22
## [127] bitops_1.0-7 data.table_1.14.0
## [129] cowplot_1.1.1 GenomicRanges_1.43.4
## [131] rtracklayer_1.51.5 patchwork_1.1.1
## [133] qvalue_2.23.0 BiocIO_1.1.2
## [135] R6_2.5.0 latticeExtra_0.6-29
## [137] gridExtra_2.3 rio_0.5.27
## [139] IRanges_2.25.7 assertthat_0.2.1
## [141] MASS_7.3-54 SummarizedExperiment_1.21.3
## [143] rjson_0.2.20 GenomicAlignments_1.27.2
## [145] Rsamtools_2.7.2 GenomeInfoDbData_1.2.4
## [147] S4Vectors_0.29.15 parallel_4.1.2
## [149] hms_1.1.0 clusterProfiler_3.99.1
## [151] grid_4.1.2 rpart_4.1-15
## [153] ggfun_0.1.3 tidyr_1.3.0
## [155] rvcheck_0.1.8 MatrixGenerics_1.3.1
## [157] carData_3.0-4 ggpubr_0.4.0
## [159] ggforce_0.3.3 Biobase_2.51.0
## [161] base64enc_0.1-3 restfulr_0.0.13
Please contact Lu Chen ([email protected]) or Li Chen ([email protected]).
If you use FAScore in your publication, please cite FAScore by