chenli 2025/09/20
FAScore is a machine learning framework designed to predict functionally significant alternative splicing (AS) events during biological processes such as hematopoietic differentiation. By integrating dynamic expression patterns of AS events and their harboring genes with isoform-specific sequence conservation and structural features, it computes functional scores through Random Forest (RF) modeling. The framework further classifies AS events into functional categories using a Gaussian Mixture Model (GMM).
The analysis incorporates two feature classes:
Dynamic features: Expression breadth (R), tissue specificity (τ), correlation (Spearman’s ρ with significance Pρ), and expression trends (regression slope β with Pβ). These are scaled (0 to 1) and combined into a DyScore (-1 to 1), where positive/negative values indicate up-/down-regulation respectively. Structural features: Derived from APPRIS, including evolutionary conservation, functional domains, transmembrane helices, and subcellular localization signatures.
FAScore outputs both DyScore (dynamic score) and FAScore (functional score) for all annotated AS events. The package supports full analysis in 11 species (human, macaque, chimpanzee, mouse, rat, pig, cow, chicken, zebrafish, fruitfly and C. elegans) and DyScore calculation in any species.
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 (>= 1.1),
GenomicRanges (>= 1.43),
GenomicFeatures (>= 1.43),
ggplot2 (>= 3.5),
ggrepel (>= 0.9),
methods (>= 4.0),
rtracklayer (>= 1.51),
stringr (>= 1.5),
tidyr (>= 1.3),
IRanges (>= 2.25),
stats (>= 4.0),
reshape2 (>= 1.0),
parallel (>= 4.0),
S4Vectors (>= 0.26),
cowplot (>= 1.1),
mclust (>= 5.4)
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/FAScore/package")Alternatively, you can install FAScore using the source file downloaded from the repository :
# Install FAScore from a downloaded source file
R CMD INSTALL FAScore_0.3.0.tar.gz
A data frame where rows represent alternative splicing (AS) events or splice junctions (SJs), columns correspond to sample identifiers, and values are pre-computed percent spliced in (PSI) scores generated by tools such as rMATS or ICAS.
A data frame containing normalized gene expression counts (e.g., TPM, FPKM, or DESeq2-normalized counts) with gene identifiers as rows and matching sample identifiers (consistent with the PSI data frame) as columns.
A data frame containing normalized transcript-level expression counts (e.g., RSEM-derived TPM/FPKM values), where rows represent transcript identifiers, columns correspond to sample identifiers (must match those in the PSI data frame), and values contain the normalized isoform expression measurements.
A file path pointing to a Gene Transfer Format (GTF) file containing genome annotations for alternative splicing (AS) events, splice junctions (SJs), isoforms, and gene loci. Species-specific GTF files are available from the Ensembl database
A data frame where rows represent sample identifiers (matching column names in PSI, gene expression, and isoform expression matrices), containing a required grouping variable with at least three distinct biological classes, along with any additional sample metadata.
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 dataset (You can download from our figshare database: https://doi.org/10.6084/m9.figshare.29069336.v4).
library(FAScore)
library(dplyr)
library(ggplot2)df_AS <- read.table("MusMus_Erythroid_AS.txt", header = T, check.names = FALSE)
df_AS <- df_AS[apply(df_AS, 1, function(x) {sum(x >= 0.05 & x <= 0.95, na.rm = T) >= 2}), ]
df_Gene <- read.table("MusMus_Erythroid_Gene.txt", header = T, check.names = FALSE)
df_Iso <- read.table("MusMus_Erythroid_Iso.txt", header = T, check.names = FALSE)head(df_AS)## LT-HSC_1 LT-HSC_2 ST-HSC_1
## X:161255030-161255659:+ 0.396732 0.365764 0.2242840
## 6:17282083-17282684:+ 0.135535 0.180492 0.0843074
## 4:120456285-120456509:+ 0.113175 0.375139 0.2064740
## 4:120462013-120462079:+ 0.602268 0.734503 0.5848140
## 4:120462080-120462155:+ 0.616394 0.761759 0.6365940
## 4:120463075-120463084:+ 0.922318 0.961764 0.9647620
## ST-HSC_2 MPP_1 MPP_2
## X:161255030-161255659:+ 0.4305620 0.4158790 0.477915
## 6:17282083-17282684:+ 0.0553682 0.0630537 0.128031
## 4:120456285-120456509:+ 0.3071080 0.1488300 0.386593
## 4:120462013-120462079:+ 0.6001620 0.5240050 0.643911
## 4:120462080-120462155:+ 0.6578870 0.5595110 0.704905
## 4:120463075-120463084:+ 0.9496600 0.9711360 0.962019
## CMP_1 CMP_2 MEP_1
## X:161255030-161255659:+ 0.409742 0.439201 0.397258
## 6:17282083-17282684:+ 0.168954 0.440274 0.000000
## 4:120456285-120456509:+ 0.143237 0.373457 0.205100
## 4:120462013-120462079:+ 0.554912 0.652065 0.607083
## 4:120462080-120462155:+ 0.650354 0.662815 0.621688
## 4:120463075-120463084:+ 0.946724 0.944725 0.947275
## MEP_2 MK_1 MK_2
## X:161255030-161255659:+ 0.378395 0.215838 0.479469
## 6:17282083-17282684:+ 0.000000 0.103275 0.101225
## 4:120456285-120456509:+ 0.254499 0.218328 0.482626
## 4:120462013-120462079:+ 0.636477 0.687440 0.800307
## 4:120462080-120462155:+ 0.635275 0.663026 0.814104
## 4:120463075-120463084:+ 0.923054 0.954565 0.974340
## EryA_1 EryA_2 EryB_1
## X:161255030-161255659:+ 0.424259 0.5186920 0.390533
## 6:17282083-17282684:+ 0.124026 0.0374126 0.000000
## 4:120456285-120456509:+ 0.348533 0.2273770 0.278966
## 4:120462013-120462079:+ 0.665422 0.6070830 0.661961
## 4:120462080-120462155:+ 0.721278 0.6774960 0.741367
## 4:120463075-120463084:+ 0.970910 0.9372700 1.000000
## EryB_2
## X:161255030-161255659:+ 0.657807
## 6:17282083-17282684:+ 0.000000
## 4:120456285-120456509:+ 0.137418
## 4:120462013-120462079:+ 0.785945
## 4:120462080-120462155:+ 0.820738
## 4:120463075-120463084:+ 1.000000
head(df_Gene)## LT-HSC_1 LT-HSC_2 ST-HSC_1 ST-HSC_2
## ENSMUSG00000000001 42.81 37.39 42.40 30.81
## ENSMUSG00000000003 0.00 0.00 0.00 0.00
## ENSMUSG00000000028 14.89 6.48 15.21 9.79
## ENSMUSG00000000031 0.88 0.45 0.09 0.40
## ENSMUSG00000000037 1.06 1.87 1.33 2.79
## ENSMUSG00000000049 0.00 0.00 0.00 0.00
## MPP_1 MPP_2 CMP_1 CMP_2 MEP_1 MEP_2
## ENSMUSG00000000001 28.19 26.35 31.50 18.98 23.66 14.39
## ENSMUSG00000000003 0.00 0.00 0.00 0.00 0.00 0.00
## ENSMUSG00000000028 9.74 9.74 10.19 8.01 13.28 9.99
## ENSMUSG00000000031 0.02 0.99 0.11 0.10 0.03 0.00
## ENSMUSG00000000037 1.16 1.81 0.93 0.93 2.03 2.00
## ENSMUSG00000000049 0.00 0.00 0.00 0.00 0.00 0.00
## MK_1 MK_2 EryA_1 EryA_2 EryB_1
## ENSMUSG00000000001 28.02 31.39 15.55 19.40 3.62
## ENSMUSG00000000003 0.00 0.00 0.00 0.00 0.00
## ENSMUSG00000000028 6.65 6.33 14.69 20.53 6.00
## ENSMUSG00000000031 0.01 0.02 0.01 0.01 0.00
## ENSMUSG00000000037 0.21 0.48 0.77 0.91 0.14
## ENSMUSG00000000049 0.00 0.02 0.00 0.00 0.00
## EryB_2
## ENSMUSG00000000001 3.29
## ENSMUSG00000000003 0.00
## ENSMUSG00000000028 4.83
## ENSMUSG00000000031 0.00
## ENSMUSG00000000037 0.10
## ENSMUSG00000000049 0.00
head(df_Iso)## gene_id LT-HSC_1 LT-HSC_2
## ENSMUST00000000001 ENSMUSG00000000001 42.81 37.39
## ENSMUST00000000003 ENSMUSG00000000003 0.00 0.00
## ENSMUST00000000010 ENSMUSG00000020875 0.07 0.06
## ENSMUST00000000028 ENSMUSG00000000028 13.02 5.13
## ENSMUST00000000033 ENSMUSG00000048583 0.30 0.12
## ENSMUST00000000049 ENSMUSG00000000049 0.00 0.00
## ST-HSC_1 ST-HSC_2 MPP_1 MPP_2 CMP_1
## ENSMUST00000000001 42.40 30.81 28.19 26.35 31.50
## ENSMUST00000000003 0.00 0.00 0.00 0.00 0.00
## ENSMUST00000000010 0.00 0.00 0.00 0.00 0.00
## ENSMUST00000000028 13.30 8.03 8.63 8.49 9.04
## ENSMUST00000000033 0.01 0.05 0.00 0.00 0.03
## ENSMUST00000000049 0.00 0.00 0.00 0.00 0.00
## CMP_2 MEP_1 MEP_2 MK_1 MK_2 EryA_1
## ENSMUST00000000001 18.98 23.66 14.39 28.02 31.39 15.55
## ENSMUST00000000003 0.00 0.00 0.00 0.00 0.00 0.00
## ENSMUST00000000010 0.00 0.00 0.00 0.00 0.00 0.00
## ENSMUST00000000028 6.51 11.44 7.85 4.78 4.20 11.57
## ENSMUST00000000033 0.01 0.00 0.00 0.00 0.02 0.00
## ENSMUST00000000049 0.00 0.00 0.00 0.00 0.02 0.00
## EryA_2 EryB_1 EryB_2
## ENSMUST00000000001 19.40 3.62 3.29
## ENSMUST00000000003 0.00 0.00 0.00
## ENSMUST00000000010 0.00 0.00 0.00
## ENSMUST00000000028 16.06 4.26 3.44
## ENSMUST00000000033 0.00 0.00 0.00
## ENSMUST00000000049 0.00 0.00 0.00
data(ExDataSet)
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 = df_AS,
GENE = df_Gene, ISOFORM = df_Iso)MyObj## class: FAScore
## dim: 22885 16
## metadata(1): version
## assays(1): AS
## rownames(22885): X:161255030-161255659:+
## 6:17282083-17282684:+ ...
## 16:92076720-92087473:+ 16:91676808-91676923:-
## 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 computed as the mean of
scaled (0-1) dynamic features from CalcuFeature, including:
Rscaled (scaled TPM/PSI range), τ (specificity index,
0-1), ρ (Spearman’s correlation) with binarized Pρ, and
β (linear slope, -1 to 1) with binarized Pβ. The final
score ranges from -1 to 1, where absolute values represent effect size
(0-1) and signs indicate directionality (β). This calculation is
performed by 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
## ENSMUSG00000000001 -0.7929133 0.0002483022
## ENSMUSG00000000003 NA NA
## ENSMUSG00000000028 -0.2842377 0.2860039065
## ENSMUSG00000000031 -0.8488461 0.0000321010
## ENSMUSG00000000037 -0.7224375 0.0015723392
## ENSMUSG00000000049 0.1690309 0.5314416160
head(MyObj@Tau$Gene) # head(MyObj@Tau$AS)## ENSMUSG00000000001 ENSMUSG00000000003
## 0.1925529 0.0000000
## ENSMUSG00000000028 ENSMUSG00000000031
## 0.2125353 0.7992165
## ENSMUSG00000000037 ENSMUSG00000000049
## 0.4109685 1.0000000
head(MyObj@Range$Gene) # head(MyObj@Range$AS)## ENSMUSG00000000001 ENSMUSG00000000003
## 3.20348823 0.00000000
## ENSMUSG00000000028 ENSMUSG00000000031
## 1.52459953 0.72339278
## ENSMUSG00000000037 ENSMUSG00000000049
## 1.42887147 0.01428458
head(MyObj@Linear$Gene) # head(MyObj@Linear$AS)## intercept slope
## ENSMUSG00000000001 5.3337205283 -0.0976133660
## ENSMUSG00000000003 0.0000000000 0.0000000000
## ENSMUSG00000000028 3.5319974802 -0.0125605032
## ENSMUSG00000000031 0.5115547613 -0.0326712188
## ENSMUSG00000000037 1.4298385411 -0.0455808804
## ENSMUSG00000000049 -0.0004310001 0.0002462858
## r2 pvalue
## ENSMUSG00000000001 0.44054084 0.005054309
## ENSMUSG00000000003 NaN NaN
## ENSMUSG00000000028 0.02621270 0.549136371
## ENSMUSG00000000031 0.45241263 0.004304452
## ENSMUSG00000000037 0.33808048 0.018155151
## ENSMUSG00000000049 0.05517241 0.381202376
Calculating the dynamic scores
MyObj <- CalcuDyScore(MyObj, maxSlope = 1, type = "Gene")
MyObj <- CalcuDyScore(MyObj, maxSlope = 1, type = "AS")head(MyObj@DyScore$Gene)## ENSMUSG00000000001 ENSMUSG00000000003
## -0.6805133 NA
## ENSMUSG00000000028 ENSMUSG00000000031
## -0.2515556 -0.7340211
## ENSMUSG00000000037 ENSMUSG00000000049
## -0.6964978 0.1972603
head(MyObj@DyScore$AS)## X:161255030-161255659:+ 6:17282083-17282684:+
## 0.12973496 -0.43323441
## 4:120456285-120456509:+ 4:120462013-120462079:+
## 0.07340320 0.13429497
## 4:120462080-120462155:+ 4:120463075-120463084:+
## 0.30033876 0.08212004
The framework integrates dynamic, evolutionary conserved, and structural
features to train a predictive model using randomForest (provided as a
pre-trained model). Functional scores and classifications are generated
via the CalcuFAScore function, which supports both the pre-trained
model and user-customized models.
Adding gene and transcript information of AS events. Note: Processing time may increase significantly with larger FAScore object dimensions.
my_gtf <- "Mus_musculus.GRCm38.101.gtf"
# or using other gtf files
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")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
premodel = readRDS("finalModel.Rds")
MyObj <- CalcuFAScore(MyObj, model = premodel)
# Sorted by FAScore
MyObj@RFpredict <- MyObj@RFpredict[order(MyObj@RFpredict$FAScore,decreasing = T),]Classify the AS by GMM
MyObj <- calcuGMM(MyObj)## fitting ...
## | | | 0% | |======================= | 50% | |==============================================| 100%
head(MyObj@RFpredict)## ASID
## 18 X:7762695-7763060:+
## 27 X:7767893-7767997:+
## 93 18:24205959-24206080:+
## 102 6:115675689-115675964:-
## 117 11:60240570-60240936:-
## 118 11:60240937-60241345:-
## TranscriptID
## 18 ENSMUST00000077680,ENSMUST00000079542,ENSMUST00000115679,ENSMUST00000137467,ENSMUST00000143680,ENSMUST00000115680
## 27 ENSMUST00000077680,ENSMUST00000115679,ENSMUST00000137467,ENSMUST00000143680,ENSMUST00000115680,ENSMUST00000115677,ENSMUST00000101695,ENSMUST00000126694
## 93 ENSMUST00000170243,ENSMUST00000164998
## 102 ENSMUST00000112949
## 117 ENSMUST00000064019
## 118 ENSMUST00000064019,ENSMUST00000102682
## TranscriptName
## 18 Tfe3-201,Tfe3-202,Tfe3-206,Tfe3-211,Tfe3-212,Tfe3-207
## 27 Tfe3-201,Tfe3-206,Tfe3-211,Tfe3-212,Tfe3-207,Tfe3-204,Tfe3-203,Tfe3-208
## 93 Galnt1-206,Galnt1-203
## 102 Raf1-202
## 117 Tom1l2-201
## 118 Tom1l2-201,Tom1l2-205
## GeneID GeneName matchTransID
## 18 ENSMUSG00000000134 Tfe3 ENSMUST00000077680
## 27 ENSMUSG00000000134 Tfe3 ENSMUST00000077680
## 93 ENSMUSG00000000420 Galnt1 ENSMUST00000170243
## 102 ENSMUSG00000000441 Raf1 ENSMUST00000112949
## 117 ENSMUSG00000000538 Tom1l2 ENSMUST00000064019
## 118 ENSMUSG00000000538 Tom1l2 ENSMUST00000102682
## matchGeneID Firestar Matador3D CORSAIR SPADE
## 18 ENSMUSG00000000134 0 2.250 7.2 311.6
## 27 ENSMUSG00000000134 0 2.250 7.2 311.6
## 93 ENSMUSG00000000420 23 12.250 18.1 234.0
## 102 ENSMUSG00000000441 18 12.008 13.4 356.4
## 117 ENSMUSG00000000538 0 6.200 0.0 251.1
## 118 ENSMUSG00000000538 0 6.200 0.0 251.1
## THUMP SignalP TargetP AS.Spearman.cor AS.Spearman.p
## 18 0 -4 -5 -0.6390644 1
## 27 0 -4 -5 -0.8047478 1
## 93 3 -1 -5 0.7633270 1
## 102 0 -4 -5 0.6035609 1
## 117 0 -4 -5 0.6331472 1
## 118 0 -4 -5 0.6745680 1
## AS.Tau AS.Range AS.Slope AS.Fit.p
## 18 0.03831619 0.1037665 -0.004392621 1
## 27 0.05476454 0.0861585 -0.003291382 1
## 93 0.06479391 0.1014530 0.004204700 1
## 102 0.57567747 0.3539160 0.012118099 1
## 117 0.50129677 0.3996310 0.015102335 1
## 118 0.44797153 0.3885665 0.015223646 1
## ASDyScore Gene.Spearman.cor Gene.Spearman.p
## 18 -0.4642566 -0.1361972 0
## 27 -0.4914937 -0.1361972 0
## 93 0.4889631 -0.1479316 0
## 102 0.5908787 -0.5029674 1
## 117 0.5915295 -0.8698377 1
## 118 0.5877216 -0.8698377 1
## Gene.Tau Gene.Range Gene.Slope Gene.Fit.p
## 18 0.21975519 1.200320 0.01108142 0
## 27 0.21975519 1.200320 0.01108142 0
## 93 0.05473359 0.947713 -0.01502795 0
## 102 0.17934681 2.401096 -0.04339802 0
## 117 0.38613949 2.214737 -0.09362355 1
## 118 0.38613949 2.214737 -0.09362355 1
## GeDyScore FAScore FASType
## 18 0.2278390 1 Func
## 27 0.2278390 1 Func
## 93 -0.1942344 1 Func
## 102 -0.4542854 1 Func
## 117 -0.7249335 1 Func
## 118 -0.7249335 1 Func
or visualization GMM
calcuGMM(MyObj, silent =T, main="Mouse")
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/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8
## [2] LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8
## [4] LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8
## [6] LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8
## [8] LC_NAME=C
## [9] LC_ADDRESS=C
## [10] LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8
## [12] LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets
## [6] methods base
##
## other attached packages:
## [1] rmarkdown_2.9 ggplot2_3.5.1 dplyr_1.1.4
## [4] FAScore_0.3.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2
## [2] tidyselect_1.2.0
## [3] RSQLite_2.2.7
## [4] AnnotationDbi_1.53.1
## [5] htmlwidgets_1.5.3
## [6] grid_4.1.2
## [7] BiocParallel_1.25.5
## [8] scatterpie_0.1.6
## [9] munsell_0.5.0
## [10] miniUI_0.1.1.1
## [11] withr_2.5.0
## [12] colorspace_2.0-2
## [13] GOSemSim_2.17.1
## [14] energy_1.7-8
## [15] filelock_1.0.2
## [16] Biobase_2.51.0
## [17] knitr_1.33
## [18] rstudioapi_0.13
## [19] stats4_4.1.2
## [20] ggsignif_0.6.4
## [21] DOSE_3.17.0
## [22] labeling_0.4.2
## [23] MatrixGenerics_1.3.1
## [24] GenomeInfoDbData_1.2.4
## [25] polyclip_1.10-0
## [26] bit64_4.0.5
## [27] farver_2.1.0
## [28] downloader_0.4
## [29] vctrs_0.6.4
## [30] treeio_1.15.6
## [31] generics_0.1.0
## [32] xfun_0.24
## [33] ggthemes_4.2.4
## [34] BiocFileCache_1.99.3
## [35] randomForest_4.6-14
## [36] R6_2.5.0
## [37] GenomeInfoDb_1.27.11
## [38] graphlayouts_0.7.1
## [39] bitops_1.0-7
## [40] cachem_1.0.5
## [41] fgsea_1.17.0
## [42] DelayedArray_0.17.10
## [43] assertthat_0.2.1
## [44] promises_1.2.0.1
## [45] BiocIO_1.1.2
## [46] scales_1.3.0
## [47] ggraph_2.0.5
## [48] nnet_7.3-16
## [49] enrichplot_1.11.2
## [50] ggExtra_0.10.1
## [51] gtable_0.3.0
## [52] tidygraph_1.2.0
## [53] rlang_1.1.2
## [54] splines_4.1.2
## [55] rtracklayer_1.51.5
## [56] rstatix_0.7.0
## [57] lazyeval_0.2.2
## [58] broom_0.7.9
## [59] checkmate_2.0.0
## [60] BiocManager_1.30.22
## [61] yaml_2.2.1
## [62] reshape2_1.4.4
## [63] abind_1.4-5
## [64] GenomicFeatures_1.43.8
## [65] backports_1.2.1
## [66] httpuv_1.6.1
## [67] qvalue_2.23.0
## [68] Hmisc_4.5-0
## [69] clusterProfiler_3.99.1
## [70] tools_4.1.2
## [71] ellipsis_0.3.2
## [72] RColorBrewer_1.1-2
## [73] BiocGenerics_0.37.1
## [74] Rcpp_1.0.12
## [75] plyr_1.8.6
## [76] progress_1.2.2
## [77] base64enc_0.1-3
## [78] zlibbioc_1.37.0
## [79] purrr_1.0.2
## [80] RCurl_1.98-1.3
## [81] prettyunits_1.1.1
## [82] ggpubr_0.4.0
## [83] rpart_4.1-15
## [84] viridis_0.6.1
## [85] cowplot_1.1.1
## [86] S4Vectors_0.29.15
## [87] SummarizedExperiment_1.21.3
## [88] haven_2.4.1
## [89] ggrepel_0.9.4
## [90] cluster_2.1.2
## [91] fs_1.5.0
## [92] magrittr_2.0.1
## [93] data.table_1.14.0
## [94] DO.db_2.9
## [95] openxlsx_4.2.4
## [96] matrixStats_0.62.0
## [97] gsl_2.1-6
## [98] hms_1.1.0
## [99] patchwork_1.1.1
## [100] mime_0.11
## [101] evaluate_0.14
## [102] xtable_1.8-4
## [103] XML_3.99-0.6
## [104] rio_0.5.27
## [105] jpeg_0.1-9
## [106] mclust_5.4.9
## [107] readxl_1.3.1
## [108] IRanges_2.25.7
## [109] gridExtra_2.3
## [110] biomaRt_2.47.7
## [111] compiler_4.1.2
## [112] tibble_3.2.1
## [113] crayon_1.4.1
## [114] shadowtext_0.0.8
## [115] htmltools_0.5.1.1
## [116] mgcv_1.8-38
## [117] segmented_1.3-4
## [118] ggfun_0.1.3
## [119] later_1.2.0
## [120] Formula_1.2-4
## [121] tidyr_1.3.0
## [122] aplot_0.0.6
## [123] DBI_1.1.1
## [124] tweenr_1.0.2
## [125] dbplyr_2.1.1
## [126] rappdirs_0.3.3
## [127] MASS_7.3-54
## [128] boot_1.3-28
## [129] Matrix_1.6-0
## [130] car_3.0-11
## [131] cli_3.6.1
## [132] parallel_4.1.2
## [133] igraph_1.2.6
## [134] GenomicRanges_1.43.4
## [135] forcats_0.5.1
## [136] pkgconfig_2.0.3
## [137] rvcheck_0.1.8
## [138] GenomicAlignments_1.27.2
## [139] foreign_0.8-81
## [140] ggtree_3.10.0
## [141] XVector_0.31.1
## [142] yulab.utils_0.1.1
## [143] stringr_1.5.1
## [144] digest_0.6.27
## [145] Biostrings_2.59.2
## [146] cellranger_1.1.0
## [147] fastmatch_1.1-3
## [148] tidytree_0.4.6
## [149] htmlTable_2.2.1
## [150] restfulr_0.0.13
## [151] curl_4.3.2
## [152] kernlab_0.9-29
## [153] shiny_1.6.0
## [154] Rsamtools_2.7.2
## [155] rjson_0.2.20
## [156] lifecycle_1.0.3
## [157] nlme_3.1-152
## [158] jsonlite_1.7.2
## [159] carData_3.0-4
## [160] viridisLite_0.4.0
## [161] fansi_0.5.0
## [162] pillar_1.9.0
## [163] lattice_0.20-45
## [164] KEGGREST_1.31.1
## [165] fastmap_1.1.0
## [166] httr_1.4.2
## [167] survival_3.2-13
## [168] GO.db_3.12.1
## [169] glue_1.6.2
## [170] zip_2.2.0
## [171] png_0.1-7
## [172] bit_4.0.4
## [173] ggforce_0.3.3
## [174] stringi_1.7.3
## [175] mixtools_1.2.0
## [176] blob_1.2.2
## [177] latticeExtra_0.6-29
## [178] memoise_2.0.0
## [179] ggunchained_0.0.1
## [180] ape_5.5
Please contact Lu Chen ([email protected]) or Li Chen ([email protected]).
If you use FAScore in your publication, please cite FAScore by