library(DTSEA)
#> This package SHOULD NOT BE USED UNDER INTEL MATH KERNEL LIBRARY ON ANY OCCASION.
#> There is an avoidable but critical bug with Intel Math Kernel Library (MKL) on various operating systems.
#>
#> ======================================
#> For better performance, we recommend not using RStudio on Windows because RStudio cannot take advantage of the multi-core capabilities available on modern computers.The Drug Target Set Enrichment Analysis (DTSEA) is a novel tool used to identify the most effective drug set against a particular disease based on the Gene Set Enrichment Analysis (GSEA).
The central hypothesis of DTSEA is that the targets of potential candidates for a specific disease (e.g., COVID-19) ought to be close to each other, or at least not so far away from the disease. The DTSEA algorithm determines whether a drug is potent for the chosen disease by the proximity between drug targets and the disease-related genes. Under the central hypothesis of DTSEA, the DTSEA consists of two main parts:
To evaluate the influence, we compute the disease-node distance by using the random walk with restart (RwR) algorithm, then rank the nodes reversely.
The GSEA approach is adopted in this part to identify whether candidate drug targets are disease-related (top) or disease-unrelated (bottom) on the human PPI list. The specific disease gene list is normalized by the median and is set zero as the arbitrary cutoff point to classify the relations manually.
This vignette illustrates how to use the DTSEA easily. Here, using functions in this package, users could identify potential drugs for disease by the DTSEA algorithm.
The function DTSEA is used to calculate the enrichment
scores of drugs. The parameters are as follows:
network: The human protein-protein interactome network.
It should be or be preconverted before being inputted in DTSEA.disease: The disease-related nodes.drugs: The drug-target long format dataframe. It
includes at least columns with the drug_id and drug_target.rwr.pt: The random walk p0 vector. Set it to zero if
you wish DTSEA automatically compute it, or you can provide your
predetermined p0 vector.sampleSize: The size of a randomly selected gene
collection, where size = pathwaySizeminSize: Minimal set of a drug set to be tested.maxSize: Maximal set of a drug set to be tested.nproc: The CPU workers that fgsea would utilize.eps: The boundary of calculating the p value.nPermSimple: Number of permutations in the simple fgsea
implementation for preliminary estimation of P-values.gseaParam: GSEA parameter value, all gene-level
statistics are raised to the power of ‘gseaParam’ before calculating
GSEA enrichment scores.verbose: Show the messages.#> Random walking...
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#> Doing GSEA enrichment...
#> Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (11.74% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.You can arrange the positive results by NES
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
select(result, -leadingEdge) %>%
arrange(desc(NES)) %>%
filter(NES > 0 & pval < .05)
#> # A tibble: 0 × 7
#> # … with 7 variables: drug_id <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
#> # ES <dbl>, NES <dbl>, size <int>The function random.walk calculates the pt
vector. The parameters are as follows:
network The input graph object. It should be either
igraph object or edge list matrix / data frame.p0 The starting vector on time 0.edge_weight Logical to indicate whether the input graph
contains weight information.gamma The restart probability used for RwR. The
gamma takes the value from 0 to 1, controlling the
probability that a node would go back to its starting node.threshold The threshold used for RwR. The
threshold indicates the stabilization status, which is a
stopping criterion of RwR.pt.post.processing The way to scale the pt
vector. It can be ‘none’, ‘zscore’, and ‘log’.pt.align The way to normalize the output
pt vector. It can be ‘mean’ to manually cut the up- and
down-regulated genes, ‘median’ to avoid the influence of the
distribution shape, or ‘none’ for no normalization.# Calculate p0
p0 <- calculate_p0(nodes = example_ppi, disease = example_disease_list)
# Then perform random walk
random.walk(network = example_ppi, p0 = p0)
#> STAT1 IL1RAP MYD88 MAPK14 JAK2 IRAK1
#> 4.38701169 4.30737409 4.25188979 4.16908854 4.16503271 4.15789286
#> TRAF1 TLR4 IL1B IL2RA AGT IRAK2
#> 4.15178891 4.11965253 4.11770168 4.06488697 4.03783534 4.03336011
#> IL6 JAK1 PML C1QA ETS1 C2
#> 4.01463662 4.00768863 4.00596675 4.00213414 3.99983386 3.99847197
#> NT5E SERPING1 IRF7 TLR2 C1QB MBL2
#> 3.98335137 3.97970848 3.97851989 3.97616137 3.96910325 3.96673811
#> IFI16 CCND3 C1QBP IL2RG PRKCD TOLLIP
#> 3.95323306 3.94642847 3.94432859 3.94280132 3.94220196 3.94070406
#> NFATC3 CDKN1A IL23A NFKBIA CD247 CASP1
#> 3.93970313 3.93796030 3.93647457 3.93523591 3.93140067 3.92811064
#> F2 STAT4 CCR5 STING1 IRF5 GAPDH
#> 3.92750167 3.92561996 3.91887581 3.91576054 3.90930125 3.90469456
#> MBP SOCS1 PTGS2 MALT1 NLRP3 IL7R
#> 3.90376816 3.90285509 3.90193402 3.90057877 3.90023935 3.89351485
#> CCR2 CCR6 ITGAM IRAK3 CD274 CSF3R
#> 3.89296010 3.89296010 3.89290581 3.89268002 3.89093980 3.88928847
#> PTAFR LTB4R LTF IFIH1 IL18R1 CD46
#> 3.88895931 3.88680801 3.88524107 3.88505320 3.87774449 3.87771671
#> SKI TLR5 IL1RN TCF7 IRF4 CD28
#> 3.87521895 3.87462430 3.87433760 3.87234853 3.87102120 3.87044348
#> MAF TNFRSF9 BAX TGFB1 PECAM1 CD163
#> 3.86986001 3.86919740 3.86861504 3.86689863 3.86026731 3.85883368
#> ACE ADA EEF1G FCER1G C8A IL16
#> 3.85833543 3.85800541 3.85780874 3.85565345 3.85549815 3.85319778
#> TAP1 FKBP5 PSMC2 XBP1 S100A9 MX1
#> 3.85214404 3.85077421 3.85028943 3.84954512 3.84867092 3.84838877
#> MS4A1 BST2 B3GAT1 LTBR CX3CR1 TNFRSF10C
#> 3.84763941 3.84644867 3.84618465 3.84589444 3.84537511 3.84496091
#> ICAM3 LAIR1 LILRA6 LILRB4 ATG7 IFITM1
#> 3.84315301 3.84302474 3.84262790 3.84262790 3.84239112 3.84168233
#> CD40LG ADGRE5 NFKB1 SYK SRC TP53
#> 3.84105370 3.83965498 3.67472363 3.57733466 3.26402290 3.22414317
#> C3 ESR1 CSK PIK3R1 TBK1 TNF
#> 3.04802687 2.93527627 2.90788374 2.88969069 2.87025710 2.86434047
#> PRKCQ JAK3 IKBKE SDC2 APOA1 EGFR
#> 2.84667962 2.81566863 2.80198798 2.79253555 2.77076326 2.75539424
#> MAPK1 MAPK3 MTOR MAPK13 CALM1 LCK
#> 2.74091869 2.71134443 2.67836336 2.66053057 2.62877491 2.60600596
#> HIF1A IKBKB KNG1 FYN CHUK PIK3CA
#> 2.58173530 2.56795038 2.56051772 2.55854267 2.54630160 2.53579939
#> LYN CSNK2A1 MAP3K1 IRAK4 APP AKT1
#> 2.49121008 2.42586569 2.40630756 2.40290900 2.37670015 2.35348677
#> TUBB PIK3CB MAPK9 MAPK10 STAT5B PIK3R2
#> 2.33273555 2.26342349 2.26331332 2.25217495 2.24130437 2.24050626
#> PIK3CD C4B C4A SNCA PIK3R3 PTK2
#> 2.23863782 2.20060511 2.19504960 2.19358930 2.15812888 2.15582429
#> RIPK2 C5 ERN1 PRKCG HBA1 HBB
#> 2.15533667 2.14044941 2.13130165 2.11331085 2.09246440 2.09246440
#> C1S HIPK2 C1QC TYK2 SOD1 RIPK1
#> 2.06078674 2.03519288 2.02961417 2.01331274 2.00808897 2.00089712
#> CP PDIA3 GSK3B MYC CCND1 NR3C1
#> 1.98812109 1.98058938 1.97141850 1.97090086 1.95009823 1.90365863
#> AR PIK3CG CASP3 NOS2 PRKACA FCN3
#> 1.89367014 1.89144514 1.88787914 1.86914056 1.82219486 1.82037801
#> BTK VTN MAP2K6 ANXA1 C1R PIK3R5
#> 1.81567558 1.81508004 1.80113028 1.78611129 1.78218134 1.76966374
#> CSNK2A2 AKT3 PAK1 AKT2 S100A8 JUP
#> 1.76887994 1.76423639 1.74844321 1.74578839 1.71003926 1.70419801
#> MAP2K3 ACE2 BDKRB1 PDPK1 CREB1 IL2RB
#> 1.64606502 1.63924393 1.63177562 1.62644211 1.59974425 1.59136733
#> DRD1 PGR PDGFRB IL3 MTNR1A YES1
#> 1.58812108 1.58255185 1.58146911 1.57837052 1.56997662 1.55950959
#> MAP3K3 ERBB2 CAMK2B CDK4 CSF2RB PTK2B
#> 1.53804063 1.53736870 1.53256669 1.51455270 1.51073272 1.49719504
#> TLR9 PDGFRA MMP2 HSPA8 TLR7 GRIN2B
#> 1.45916893 1.45078223 1.43340822 1.42441869 1.42071789 1.41094675
#> C8G GRIN2A C8B CAMK2A FN1 CSF2RA
#> 1.39983503 1.38000317 1.37970886 1.37817395 1.36791859 1.35880873
#> GRIN2D TGFBR1 PRKACB FCGR3A IL3RA CFI
#> 1.34084145 1.33426021 1.32955896 1.32821020 1.31434794 1.31293402
#> KIT TUBA3C CFB FCGR3B ADRA1B EEF1A1
#> 1.30936131 1.29528036 1.28476211 1.21594994 1.20513355 1.19768769
#> CDK1 IFNAR1 HTR2A VEGFA PRKD1 HCK
#> 1.18981104 1.18198983 1.18113584 1.18111479 1.16063919 1.15702306
#> PARP1 PCNA OPRD1 TUBB4A PRKCI EDNRA
#> 1.15532616 1.15055644 1.14426586 1.14119971 1.12621311 1.12451937
#> TGFBR2 ITK BRAF ADRA1A IFNAR2 BRCC3
#> 1.11903965 1.10879796 1.10625356 1.10294493 1.10076800 1.09813916
#> CHRM3 FGFR3 HTR2B ADRA1D ERBB4 CHRM1
#> 1.09470994 1.09156667 1.08666793 1.08227696 1.08210302 1.07918857
#> CHRM5 HRH1 TACR1 RAF1 VDR TEK
#> 1.07918857 1.07918857 1.07918857 1.07798536 1.06083117 1.05784340
#> CAMK2D F12 HDAC1 MDM2 A2M OPRM1
#> 1.05376231 1.04844209 1.04829885 1.03880814 1.03272295 1.03238071
#> EIF2AK2 MTNR1B ZAP70 TUBB3 DRD2 ABL1
#> 1.03182198 1.02837550 1.02773836 1.02131330 1.02078732 1.01673894
#> ADORA3 ADRA2C CHRM2 CHRM4 HRH4 HTR1A
#> 1.01396245 1.01396245 1.01396245 1.01396245 1.01396245 1.01396245
#> HTR1B HTR1D HTR1E HTR1F OPRK1 FCGR2A
#> 1.01396245 1.01396245 1.01396245 1.01396245 1.01396245 1.00367013
#> MAPK7 GRIN1 FCGR1A FGFR2 FGFR1 PRKCE
#> 0.99871456 0.97520735 0.97234658 0.96742643 0.94948161 0.91910577
#> ITGB3 PAK2 INSR RBX1 FGA HSPA5
#> 0.91013154 0.90127560 0.80782914 0.80307139 0.75847664 0.74135852
#> KCNJ4 TP73 DRD3 LIMK1 MAPK6 SELE
#> 0.73332628 0.71746237 0.70792579 0.70078230 0.67008037 0.63783393
#> PIM1 ADRB2 TEC KRT1 KCNJ2 MMP9
#> 0.63775183 0.63752110 0.63328527 0.63178284 0.59869683 0.56833028
#> MAPKAPK5 C4BPA SLC2A1 AXL RPS6KA1 AURKA
#> 0.51979048 0.49689235 0.49358427 0.49305428 0.48477289 0.47453927
#> SIRT1 PZP APCS FCGR2C DSP TUBA1A
#> 0.45907203 0.45528753 0.45262224 0.44506796 0.44235591 0.44141729
#> KDR DYRK1B C4BPB MAPK4 HPR IGFALS
#> 0.43724188 0.42850759 0.42809668 0.36924205 0.34146894 0.33495379
#> MAP2K2 HIPK3 PAK3 APOL1 CSNK1A1 CFH
#> 0.33215315 0.32646779 0.32033314 0.31948655 0.29744619 0.29679488
#> ITGA5 STK39 PRDX1 MAP3K2 GABRB1 BCR
#> 0.28914839 0.24069779 0.18841078 0.17766633 0.17129435 0.15630617
#> TUBB2B FGR MKNK1 INS PDE1A MAPK15
#> 0.15332004 0.14905980 0.14551103 0.13369752 0.12949984 0.12680317
#> RCAN1 TUBA1B CLU TUBA4A ALOX5 MET
#> 0.11492500 0.09588107 0.09318039 0.08977558 0.08117469 0.06575737
#> PPARA APOA2 ALOX15 ALDOA EPHB2 PTGDS
#> 0.05543149 0.05010703 0.03703279 0.01617541 0.00000000 -0.02179376
#> NPPB GJA1 FES BMX FLT1 PDE6G
#> -0.03685839 -0.06480503 -0.06719487 -0.07384513 -0.07746998 -0.08182198
#> CAMK2G P4HB PDE3B APOE CSF1R ALK
#> -0.08449631 -0.10273148 -0.12187141 -0.12234875 -0.13426309 -0.13922150
#> PI4K2B TLK1 ACVR1 NTRK3 TPI1 PDE1B
#> -0.15177764 -0.16966605 -0.16982699 -0.17670834 -0.17847530 -0.19207530
#> PDE1C EPHB1 EPHB4 PDE4B BMPR2 FLT3
#> -0.19207530 -0.20102624 -0.20649820 -0.20839147 -0.21015412 -0.21095020
#> PDE4A RPS6KA3 TLK2 CAMK1 PDE6A PDE6B
#> -0.21901408 -0.22263188 -0.23217379 -0.23715159 -0.25181963 -0.25181963
#> PDE10A GSK3A TXK BMPR1B PDE4C PDE4D
#> -0.25660468 -0.26216369 -0.27210694 -0.27819411 -0.28369770 -0.28369770
#> ROCK2 NEK9 RPS6KA6 PDE3A BMP2K CLK1
#> -0.29487952 -0.29548858 -0.31454434 -0.31657884 -0.34472708 -0.34472708
#> MELK STK16 CASK PDE5A GSS CHEK2
#> -0.34472708 -0.34472708 -0.36650929 -0.37193837 -0.39032070 -0.39379955
#> NOS1 PDE2A PDE6C PDE6D PDE6H PDE7A
#> -0.43408722 -0.43949726 -0.43949726 -0.43949726 -0.43949726 -0.43949726
#> PDE7B PDE8A PDE8B PDE9A TTR VCAM1
#> -0.43949726 -0.43949726 -0.43949726 -0.43949726 -0.46696471 -0.46812713
#> EIF2AK4 PLK1 A1BG DAPK3 MAP3K4 PAK6
#> -0.47399402 -0.49961054 -0.50525933 -0.51587155 -0.51927190 -0.52122347
#> RET EIF2AK1 GSN FER EPHA3 L1CAM
#> -0.52925055 -0.54417557 -0.54554914 -0.55188302 -0.55266663 -0.56888415
#> TAOK3 F13B SERPIND1 FCGR2B MKNK2 PRKG2
#> -0.57155298 -0.57230800 -0.57230800 -0.60664819 -0.61843221 -0.61900615
#> CTSL CHRNA9 MAP4K1 TUBB6 EPHA2 MST1R
#> -0.62793197 -0.64722832 -0.68367659 -0.68410326 -0.74689785 -0.76335088
#> SLC18A2 MAP3K11 CCS SDHA CHEK1 GABRA1
#> -0.80196763 -0.80493123 -0.80532175 -0.80532175 -0.81423402 -0.81480108
#> PRKAA1 PRKAB1 KHSRP CACNA1D PRKAA2 DYRK1A
#> -0.81532851 -0.81631825 -0.83188242 -0.84007760 -0.85396954 -0.86565247
#> ABL2 GRIA1 CTSS PRG2 ORM1 CHRNA7
#> -0.86861281 -0.87342834 -0.88042620 -0.89560990 -0.89601491 -0.89660410
#> PRKAB2 PRKAG1 PRKAG2 PRKAG3 GRIA2 MYLK
#> -0.89716708 -0.89716708 -0.89716708 -0.89716708 -0.92177302 -0.93400112
#> CACNA1C TUBB4B EPHA4 PON1 PIP4K2B PIP4K2C
#> -0.94009780 -0.94843248 -0.96809925 -0.98269935 -0.99556773 -0.99556773
#> GRIA3 ADRB3 LRAT DDB1 EPHA8 NTRK1
#> -0.99627714 -1.00499769 -1.00793230 -1.00951640 -1.02833056 -1.05615212
#> MARK4 HTR6 MYLK2 ADRA2A FLT4 PKN1
#> -1.05619832 -1.09768413 -1.09771133 -1.10673946 -1.10826808 -1.10978132
#> EPHA1 EPHA5 EPHA7 GRIA4 RIPK4 MATK
#> -1.11446172 -1.11446172 -1.11446172 -1.11878440 -1.13297185 -1.13599459
#> CPN1 TUBA1C TUBA3E CHRNA3 CHRNA4 CHRNA6
#> -1.15122072 -1.15262569 -1.15262569 -1.16606613 -1.16606613 -1.16606613
#> CHRNB2 CHRNB4 PLK4 DRD5 HTR7 SHBG
#> -1.16606613 -1.16606613 -1.19299386 -1.22031083 -1.22031083 -1.22385880
#> PTGS1 ENO1 PPARG NEK2 CLASP1 PKN2
#> -1.26245351 -1.27623940 -1.28990093 -1.29652839 -1.30158706 -1.31473569
#> CACNA1F CACNA1S LIMK2 S100A7 INSRR MARK3
#> -1.31507896 -1.31507896 -1.34811302 -1.35065515 -1.35230695 -1.35297620
#> TAOK2 WEE1 PLK3 KRT10 TUBB1 CUL4A
#> -1.40401379 -1.40669937 -1.44861483 -1.46375161 -1.46834592 -1.49293688
#> HDAC4 TUBB2A ULK1 TAOK1 MYLK3 MYLK4
#> -1.50957228 -1.51821274 -1.55665254 -1.56007205 -1.56966693 -1.56966693
#> NR0B1 AHR NR3C2 KLKB1 MAP2K5 TYRO3
#> -1.59782766 -1.62976772 -1.63707628 -1.63895437 -1.65678124 -1.65847824
#> TUBB8 PIK3C2B TSSK1B HDAC8 KRT5 TF
#> -1.67456622 -1.70688760 -1.72608882 -1.72983522 -1.76063896 -1.77067116
#> NDUFC2 NDUFV3 MGMT BLK ULK2 IGHM
#> -1.81004541 -1.81004541 -1.81313702 -1.83348856 -1.84786760 -1.85097942
#> DAPK2 TTK CACNA1G SIVA1 GAK CAMKK1
#> -1.85925108 -1.91217605 -1.91654251 -1.91892030 -1.91975739 -1.92046898
#> CAMKK2 MAP3K6 DAPK1 CACNA1H CACNA1I MT1A
#> -1.94206860 -1.94242064 -1.95120402 -1.97984473 -1.97984473 -1.98716282
#> S100A2 SLC7A11 KRT14 CACNG2 CACNG4 PI4KB
#> -1.98716282 -1.98716282 -2.03903908 -2.07327805 -2.07328544 -2.07349753
#> CSN3 CACNG3 MMP12 CACNB3 CACNB1 CACNB4
#> -2.07415019 -2.08324895 -2.10144086 -2.10739342 -2.12626863 -2.12626863
#> TNK2 GABRA4 SGK3 CACNA2D2 LATS1 CACNB2
#> -2.14096355 -2.14435373 -2.15673538 -2.15952686 -2.16659099 -2.17033518
#> CACNA2D1 CACNA2D3 CACNA2D4 KRT9 MPG KRT16
#> -2.17039267 -2.17039267 -2.17039267 -2.18071954 -2.18071954 -2.18071954
#> APLP2 CACNG1 CACNG5 CACNG6 CACNG7 KCNJ9
#> -2.20431078 -2.22707682 -2.22707682 -2.22707682 -2.22707682 -2.27189945
#> MARK2 GAPDHS STK3 CAMK1D PAK4 KCNJ6
#> -2.28406530 -2.29255526 -2.30217057 -2.30482336 -2.30807287 -2.31190075
#> AURKB OXSR1 NME1 NTRK2 MAP3K13 MAP3K10
#> -2.31347641 -2.32260467 -2.33887145 -2.37267325 -2.37369204 -2.39759778
#> NEU1 CAMK1G GABRA3 CACNA1A GRIN3A GABRG3
#> -2.40418038 -2.42152566 -2.43360277 -2.45686471 -2.45795840 -2.46465053
#> CACNG8 PAK5 MAP4K3 MAP4K4 MT2A LTA4H
#> -2.47364300 -2.48478397 -2.48654469 -2.48654469 -2.50217721 -2.50909248
#> SLK PHKG1 SERPINA3 PTK6 PKMYT1 DDR2
#> -2.51589358 -2.52839699 -2.57402238 -2.61799294 -2.62123119 -2.63129778
#> NR1I2 CYP17A1 CHRNA2 CHRNA5 CHRNB3 NUAK2
#> -2.63129778 -2.63901239 -2.64645207 -2.64645207 -2.64645207 -2.69554951
#> HTR2C TIE1 STK35 KRT6A DDR1 ADRB1
#> -2.70505357 -2.76285500 -2.76715561 -2.84105844 -2.84734424 -2.86346711
#> MAP3K12 LTK STK24 KCNJ3 PDCD6 LRRK2
#> -2.97091746 -3.01012060 -3.05423936 -3.06295739 -3.06624359 -3.06999373
#> GABRA2 GABRA5 GABRA6 STK26 GABRB2 GABRB3
#> -3.07455806 -3.07455806 -3.07455806 -3.10028330 -3.11231674 -3.11231674
#> CYP11B2 HTR3A CDK17 CDKL1 MYO3A BRD4
#> -3.14945625 -3.16047948 -3.16554946 -3.16554946 -3.16554946 -3.18181656
#> MAP4K5 APOD SIK1 SERPINA4 CIT KCNJ5
#> -3.22782874 -3.23330731 -3.26779020 -3.27396267 -3.31779958 -3.46003287
#> PIK3C2G SIK2 MERTK EPHB6 AHSG DBH
#> -3.47451430 -3.53731725 -3.53780442 -3.61459077 -3.61501949 -3.61537835
#> CPN2 MT3 DRD4 MAP4K2 CES1 UGT1A1
#> -3.74148789 -3.75037905 -3.75530590 -3.79667101 -3.86879464 -3.87594328
#> NR4A3 AKR1C1 EPHA6 ROS1 TNIK MINK1
#> -4.05140443 -4.08725877 -4.11905950 -4.11905950 -4.14741721 -4.15261417
#> CDK16 KCNH2 RETSAT MARK1 CACNA1B CACNA1E
#> -4.16090144 -4.16090144 -4.27064204 -4.36360482 -4.38375855 -4.38450346
#> JCHAIN DHRS3 DHRS4 RDH11 RDH12 RDH13
#> -4.44124658 -4.49506991 -4.49506991 -4.49506991 -4.49506991 -4.49506991
#> RDH8 RDH5 PLK2 STK38L NEK11 SRD5A1
#> -4.49506991 -4.55020243 -4.88163717 -4.95135934 -5.06545055 -5.08905020
#> SRD5A2 SRD5A3 NUAK1 SERPINA1 ALDH1A1 ALDH1A2
#> -5.08905020 -5.08905020 -5.16232326 -5.16844359 -5.21518519 -5.21518519
#> ALDH1A3 HTR3B HTR3C HTR3D HTR3E CBR1
#> -5.21518519 -5.97389012 -5.97389012 -5.97389012 -5.97389012 -7.38250118
#> MTHFR MTR MTRR
#> -Inf -Inf -InfThe function cronbach.alpha and kendall.w
are used to calculate the prediction consistency.
If you have several prediction results, you can use either function to describe the consistency.
# Just report the results
kendall.w(data)$report
#> [1] "Kendall's coefficient W = 0.472, p = 0.398"
# Or just report the alpha
cronbach.alpha(data)
#> [1] 0.1931346