R version 4.0.5 (2021-03-31) -- "Shake and Throw"
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> #中文教程链接：https://www.jianshu.com/p/36a4780953f5
> library(dplyr)
载入程辑包：‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
> library(Seurat)
Attaching SeuratObject
> library(patchwork)
> # Load the PBMC dataset
> pbmc.data <- Read10X(data.dir = "./pbmc3k_filtered_gene_bc_matrices/filtered_gene_bc_matrices/hg19/")
> # Initialize the Seurat object with the raw (non-normalized data).
> pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
> pbmc
An object of class Seurat
13714 features across 2700 samples within 1 assay
Active assay: RNA (13714 features, 0 variable features)
> # The [[ operator can add columns to object metadata. This is a great place to stash QC stats
> pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
> # Show QC metrics for the first 5 cells
> head(pbmc@meta.data, 5)
orig.ident nCount_RNA nFeature_RNA percent.mt
AAACATACAACCAC-1 pbmc3k 2419 779 3.0177759
AAACATTGAGCTAC-1 pbmc3k 4903 1352 3.7935958
AAACATTGATCAGC-1 pbmc3k 3147 1129 0.8897363
AAACCGTGCTTCCG-1 pbmc3k 2639 960 1.7430845
AAACCGTGTATGCG-1 pbmc3k 980 521 1.2244898
> #Visualize QC metrics as a violin plot
> VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
> # FeatureScatter is typically used to visualize feature-feature relationships, but can be used for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
> plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
> plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
> plot1 + plot2
> pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
> pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 1e4)
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
> pbmc <- FindVariableFeatures(pbmc, selection.method = 'vst', nfeatures = 2000)
Calculating gene variances
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**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
> # Identify the 10 most highly variable genes
> top10 <- head(VariableFeatures(pbmc), 10)
> # plot variable features with and without labels
> plot1 <- VariableFeaturePlot(pbmc)
> plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
When using repel, set xnudge and ynudge to 0 for optimal results
> plot1 + plot2#调整窗口大小
Warning messages:
1: Transformation introduced infinite values in continuous x-axis
2: Transformation introduced infinite values in continuous x-axis
> all.genes <- rownames(pbmc)
> pbmc <- ScaleData(pbmc, features = all.genes)
Centering and scaling data matrix
|=======================================================================================| 100%
> set.seed(20210818)
> pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
PC_ 1
Positive: CST3, TYROBP, LST1, AIF1, FTL, FTH1, LYZ, FCN1, S100A9, TYMP
FCER1G, CFD, LGALS1, S100A8, CTSS, LGALS2, SERPINA1, IFITM3, SPI1, CFP
PSAP, IFI30, SAT1, COTL1, S100A11, NPC2, GRN, LGALS3, GSTP1, PYCARD
Negative: MALAT1, LTB, IL32, IL7R, CD2, B2M, ACAP1, CD27, STK17A, CTSW
CD247, GIMAP5, AQP3, CCL5, SELL, TRAF3IP3, GZMA, MAL, CST7, ITM2A
MYC, GIMAP7, HOPX, BEX2, LDLRAP1, GZMK, ETS1, ZAP70, TNFAIP8, RIC3
PC_ 2
Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DQB1, HLA-DRA, LINC00926, CD79B, HLA-DRB1, CD74
HLA-DMA, HLA-DPB1, HLA-DQA2, CD37, HLA-DRB5, HLA-DMB, HLA-DPA1, FCRLA, HVCN1, LTB
BLNK, P2RX5, IGLL5, IRF8, SWAP70, ARHGAP24, FCGR2B, SMIM14, PPP1R14A, C16orf74
Negative: NKG7, PRF1, CST7, GZMB, GZMA, FGFBP2, CTSW, GNLY, B2M, SPON2
CCL4, GZMH, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX
TTC38, APMAP, CTSC, S100A4, IGFBP7, ANXA1, ID2, IL32, XCL1, RHOC
PC_ 3
Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, HLA-DPA1, CD74, MS4A1, HLA-DRB1, HLA-DRA
HLA-DRB5, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, CD37, HVCN1, FCRLA, IRF8
PLAC8, BLNK, MALAT1, SMIM14, PLD4, LAT2, IGLL5, P2RX5, SWAP70, FCGR2B
Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU
HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, PTCRA, CA2, ACRBP, MMD, TREML1
NGFRAP1, F13A1, SEPT5, RUFY1, TSC22D1, MPP1, CMTM5, RP11-367G6.3, MYL9, GP1BA
PC_ 4
Positive: HLA-DQA1, CD79B, CD79A, MS4A1, HLA-DQB1, CD74, HLA-DPB1, HIST1H2AC, PF4, TCL1A
SDPR, HLA-DPA1, HLA-DRB1, HLA-DQA2, HLA-DRA, PPBP, LINC00926, GNG11, HLA-DRB5, SPARC
GP9, AP001189.4, CA2, PTCRA, CD9, NRGN, RGS18, GZMB, CLU, TUBB1
Negative: VIM, IL7R, S100A6, IL32, S100A8, S100A4, GIMAP7, S100A10, S100A9, MAL
AQP3, CD2, CD14, FYB, LGALS2, GIMAP4, ANXA1, CD27, FCN1, RBP7
LYZ, S100A11, GIMAP5, MS4A6A, S100A12, FOLR3, TRABD2A, AIF1, IL8, IFI6
PC_ 5
Positive: GZMB, NKG7, S100A8, FGFBP2, GNLY, CCL4, CST7, PRF1, GZMA, SPON2
GZMH, S100A9, LGALS2, CCL3, CTSW, XCL2, CD14, CLIC3, S100A12, CCL5
RBP7, MS4A6A, GSTP1, FOLR3, IGFBP7, TYROBP, TTC38, AKR1C3, XCL1, HOPX
Negative: LTB, IL7R, CKB, VIM, MS4A7, AQP3, CYTIP, RP11-290F20.3, SIGLEC10, HMOX1
PTGES3, LILRB2, MAL, CD27, HN1, CD2, GDI2, ANXA5, CORO1B, TUBA1B
FAM110A, ATP1A1, TRADD, PPA1, CCDC109B, ABRACL, CTD-2006K23.1, WARS, VMO1, FYB
> # Examine and visualize PCA results a few different ways
> print(pbmc[['pca']], dims = 1:5, nfeatures = 5)
PC_ 1
Positive: CST3, TYROBP, LST1, AIF1, FTL
Negative: MALAT1, LTB, IL32, IL7R, CD2
PC_ 2
Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DQB1
Negative: NKG7, PRF1, CST7, GZMB, GZMA
PC_ 3
Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1
Negative: PPBP, PF4, SDPR, SPARC, GNG11
PC_ 4
Positive: HLA-DQA1, CD79B, CD79A, MS4A1, HLA-DQB1
Negative: VIM, IL7R, S100A6, IL32, S100A8
PC_ 5
Positive: GZMB, NKG7, S100A8, FGFBP2, GNLY
Negative: LTB, IL7R, CKB, VIM, MS4A7
> VizDimLoadings(pbmc, dims = 1:2, reduction = 'pca')
> DimPlot(pbmc, reduction = 'pca')
> DimHeatmap(pbmc, dims = 1, cells = 500, balanced = TRUE)
> DimHeatmap(pbmc, dims = 1:15, cells = 500, balanced = TRUE)
> #pbmc <- JackStraw(pbmc, num.replicate = 100)
> #pbmc <- ScoreJackStraw(pbmc, dims = 1:20)
> ElbowPlot(pbmc)
> pbmc <- FindNeighbors(pbmc, dims = 1:10)
Computing nearest neighbor graph
Computing SNN
> pbmc <- FindClusters(pbmc, resolution = 0.5)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 2638
Number of edges: 95965
Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8723
Number of communities: 9
Elapsed time: 0 seconds
> # Look at cluster IDs of the first 5 cells
> head(Idents(pbmc), 5)
AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 AAACCGTGTATGCG-1
2 3 2 1 6
Levels: 0 1 2 3 4 5 6 7 8
> pbmc <- RunUMAP(pbmc, dims = 1:10)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
08:48:04 UMAP embedding parameters a = 0.9922 b = 1.112
08:48:04 Read 2638 rows and found 10 numeric columns
08:48:04 Using Annoy for neighbor search, n_neighbors = 30
08:48:04 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:48:05 Writing NN index file to temp file C:\Users\Nano\AppData\Local\Temp\RtmpeIvHJD\file259c7ddba5e
08:48:05 Searching Annoy index using 1 thread, search_k = 3000
08:48:06 Annoy recall = 100%
08:48:06 Commencing smooth kNN distance calibration using 1 thread
08:48:07 Initializing from normalized Laplacian + noise
08:48:07 Commencing optimization for 500 epochs, with 105124 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:48:19 Optimization finished
> DimPlot(pbmc, reduction = 'umap',label = TRUE)
> saveRDS(pbmc, file = "./pbmc_tutorial.rds",compress = F)
> save(phe,file = 'pbmc_tutorial.Rdata') # 是Rdata
Error in save(phe, file = "pbmc_tutorial.Rdata") : 目标对象‘phe’不存在
> save(pbmc,file = 'pbmc_tutorial.Rdata') # 是Rdata
> # find markers for every cluster compared to all remaining cells, report only the positive ones
> pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 1
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
Calculating cluster 2
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 3
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
Calculating cluster 4
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 5
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 6
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Calculating cluster 7
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 8
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
> FoldChange(pbmc)
Error in IdentsToCells(object = object, ident.1 = ident.1, ident.2 = ident.2, :
Please provide ident.1
> FoldChange(pbmc,ident.1 = 1)
avg_log2FC pct.1 pct.2
AL627309.1 5.991942e-02 0.013 0.001
AP006222.2 1.081806e-02 0.002 0.001
RP11-206L10.2 -9.997706e-03 0.000 0.002
RP11-206L10.9 1.377193e-02 0.004 0.000
LINC00115 1.661765e-02 0.010 0.006
NOC2L -2.347717e-01 0.065 0.103
KLHL17 8.565006e-03 0.004 0.003
PLEKHN1 3.039706e-02 0.004 0.002
RP11-54O7.17 2.229769e-02 0.004 0.000
HES4 8.618699e-02 0.073 0.051
RP11-54O7.11 -9.668898e-03 0.000 0.002
ISG15 1.169418e+00 0.694 0.393
AGRN 8.992492e-02 0.013 0.001
C1orf159 1.732418e-02 0.010 0.009
TNFRSF18 -2.569412e-01 0.004 0.042
TNFRSF4 -4.459027e-01 0.004 0.070
SDF4 -1.886270e-01 0.100 0.132
B3GALT6 1.353525e-03 0.027 0.021
FAM132A 8.753051e-03 0.002 0.000
UBE2J2 -1.759542e-01 0.083 0.117
ACAP3 -7.589399e-02 0.015 0.025
PUSL1 -3.293018e-02 0.019 0.023
CPSF3L -3.660217e-01 0.050 0.081
GLTPD1 -1.083306e-01 0.013 0.032
DVL1 1.609506e-02 0.004 0.001
MXRA8 -1.404046e-02 0.000 0.002
AURKAIP1 3.132754e-01 0.340 0.277
CCNL2 1.685216e-02 0.058 0.052
RP4-758J18.2 -1.449301e-01 0.017 0.041
MRPL20 2.886429e-01 0.329 0.238
ATAD3C -6.717900e-02 0.000 0.004
ATAD3B -3.877044e-02 0.006 0.012
ATAD3A -2.103357e-02 0.017 0.018
SSU72 1.977560e-01 0.390 0.323
AL645728.1 -3.772974e-03 0.010 0.010
C1orf233 3.329228e-02 0.006 0.003
RP11-345P4.9 6.299331e-02 0.021 0.013
MIB2 -3.708821e-01 0.017 0.076
MMP23B -6.350711e-02 0.000 0.010
CDK11B -1.864986e-02 0.021 0.023
SLC35E2B -5.619317e-02 0.010 0.018
CDK11A -2.663936e-03 0.067 0.059
SLC35E2 -3.089777e-02 0.002 0.007
NADK 3.121756e-01 0.088 0.038
GNB1 3.479070e-01 0.183 0.104
RP1-140A9.1 3.832413e-03 0.002 0.001
TMEM52 -6.961956e-03 0.000 0.001
PRKCZ -5.944040e-02 0.006 0.013
RP5-892K4.1 2.714462e-02 0.008 0.003
C1orf86 4.425400e-01 0.235 0.176
AL590822.2 -8.080619e-03 0.000 0.001
SKI 4.804997e-02 0.025 0.021
RER1 1.340477e-01 0.288 0.213
PEX10 -2.321032e-02 0.013 0.013
PLCH2 -1.203256e-02 0.000 0.001
PANK4 -7.142484e-02 0.010 0.023
RP3-395M20.12 2.415163e-03 0.067 0.062
TNFRSF14 -2.180262e-01 0.183 0.230
RP3-395M20.9 -3.839743e-02 0.000 0.006
FAM213B 4.848099e-03 0.017 0.014
MMEL1 3.761794e-02 0.010 0.004
TTC34 -9.721445e-03 0.000 0.002
MEGF6 -3.191884e-05 0.002 0.004
TPRG1L 4.391686e-02 0.017 0.019
WRAP73 -8.098833e-02 0.031 0.037
TP73-AS1 -4.447081e-02 0.008 0.013
SMIM1 3.680400e-02 0.010 0.004
LRRC47 -6.618629e-02 0.042 0.044
CEP104 4.098695e-02 0.025 0.023
DFFB -9.937146e-03 0.010 0.008
C1orf174 -4.609406e-02 0.048 0.054
NPHP4 5.580424e-02 0.010 0.003
KCNAB2 1.312300e-01 0.123 0.091
RPL22 -5.190237e-01 0.725 0.831
RNF207 -2.373453e-02 0.002 0.005
ICMT -4.027702e-02 0.021 0.026
GPR153 -1.256047e-02 0.000 0.002
ACOT7 -3.774562e-02 0.002 0.010
RP1-202O8.3 -1.587012e-02 0.000 0.002
ESPN -1.456495e-02 0.000 0.002
TNFRSF25 -4.565410e-01 0.008 0.076
PLEKHG5 -1.861976e-03 0.004 0.004
NOL9 -5.003837e-02 0.013 0.015
ZBTB48 3.789975e-02 0.027 0.024
KLHL21 4.493523e-03 0.019 0.013
PHF13 -1.309725e-02 0.006 0.007
THAP3 -8.388027e-02 0.021 0.032
DNAJC11 -3.029132e-02 0.010 0.017
RP11-312B8.1 1.881996e-02 0.004 0.000
CAMTA1 -4.207547e-02 0.102 0.086
VAMP3 3.297590e-01 0.123 0.062
PER3 -6.999526e-02 0.002 0.011
UTS2 -3.458646e-02 0.000 0.006
TNFRSF9 -3.185832e-03 0.002 0.005
PARK7 3.205536e-01 0.560 0.474
SLC45A1 -6.602073e-03 0.000 0.001
RERE 9.478820e-02 0.054 0.046
RP5-1115A15.1 -1.300274e-03 0.002 0.001
ENO1 2.203026e-01 0.617 0.554
ENO1-AS1 -3.463614e-02 0.002 0.006
CA6 -1.720481e-02 0.000 0.002
SLC2A5 8.127241e-03 0.002 0.001
GPR157 2.541433e-03 0.002 0.001
H6PD 8.400673e-02 0.031 0.016
SPSB1 -2.928534e-02 0.000 0.005
SLC25A33 1.354193e-01 0.050 0.034
TMEM201 -2.085647e-02 0.000 0.003
PIK3CD 1.700481e-01 0.108 0.079
C1orf200 3.385222e-04 0.002 0.002
RP11-558F24.4 -1.482720e-03 0.002 0.002
CLSTN1 -8.705609e-02 0.044 0.057
CTNNBIP1 3.065072e-01 0.129 0.048
LZIC -4.641315e-02 0.056 0.054
NMNAT1 2.640163e-02 0.013 0.009
RBP7 1.651701e+00 0.312 0.013
UBE4B 5.054159e-02 0.038 0.025
KIF1B 5.734362e-02 0.021 0.007
PGD 1.140026e+00 0.331 0.070
APITD1 -7.468917e-02 0.004 0.014
DFFA 4.658086e-02 0.054 0.040
PEX14 -5.536691e-02 0.013 0.020
CASZ1 -1.839335e-02 0.000 0.003
TARDBP -1.876865e-01 0.042 0.071
RP4-635E18.8 1.717453e-03 0.002 0.002
SRM -7.958546e-02 0.098 0.138
EXOSC10 -4.422812e-02 0.027 0.038
MTOR 2.394534e-02 0.017 0.012
UBIAD1 -2.296973e-01 0.015 0.048
FBXO2 -4.535472e-02 0.000 0.006
FBXO44 -2.074842e-01 0.004 0.037
FBXO6 5.551531e-02 0.069 0.052
MAD2L2 7.544205e-03 0.077 0.083
DRAXIN -2.346166e-03 0.004 0.002
AGTRAP 1.258683e+00 0.415 0.095
MTHFR 1.556204e-01 0.044 0.018
CLCN6 -5.555576e-02 0.006 0.012
NPPA-AS1 -1.381248e-02 0.000 0.001
KIAA2013 2.549525e-01 0.052 0.016
PLOD1 1.085345e-01 0.031 0.006
MFN2 1.709259e-01 0.038 0.021
MIIP 2.122353e-01 0.092 0.054
TNFRSF8 7.903345e-02 0.023 0.010
TNFRSF1B 8.983417e-01 0.262 0.116
VPS13D 3.124422e-02 0.023 0.021
DHRS3 -1.361857e-01 0.004 0.025
PRDM2 -9.055290e-02 0.058 0.068
TMEM51 5.313614e-02 0.013 0.001
RP3-467K16.4 -1.531229e-02 0.000 0.002
EFHD2 8.546119e-01 0.383 0.152
CASP9 -2.934787e-02 0.013 0.014
DNAJC16 -4.550817e-02 0.006 0.011
AGMAT -1.008681e-01 0.002 0.019
DDI2 -1.181420e-01 0.008 0.013
PLEKHM2 1.996065e-01 0.050 0.008
FBLIM1 -3.097529e-02 0.006 0.011
RP11-169K16.9 2.997338e-02 0.054 0.054
SPEN 6.743284e-02 0.023 0.029
ZBTB17 4.101209e-02 0.019 0.017
ARHGEF19 -4.261470e-02 0.000 0.006
RSG1 -1.856744e-02 0.000 0.003
FBXO42 4.847491e-02 0.035 0.020
SZRD1 4.681319e-02 0.096 0.086
SPATA21 2.303181e-03 0.002 0.001
NECAP2 -2.220942e-01 0.150 0.191
RP4-798A10.2 4.500992e-02 0.008 0.002
RP4-798A10.7 -6.621512e-03 0.000 0.001
RP4-798A10.4 -1.259137e-02 0.000 0.002
NBPF1 -1.843437e-03 0.013 0.015
CROCCP2 -6.018580e-02 0.031 0.051
CROCC 1.585994e-02 0.013 0.008
RP11-108M9.4 1.737063e-01 0.058 0.042
RP11-108M9.6 -6.965478e-02 0.002 0.011
ATP13A2 -1.731202e-02 0.025 0.023
SDHB 8.715809e-01 0.312 0.195
PADI2 3.927035e-02 0.006 0.000
PADI4 1.613264e-01 0.046 0.021
RCC2 2.638353e-01 0.065 0.025
ARHGEF10L 2.274314e-01 0.044 0.004
ALDH4A1 6.237586e-03 0.002 0.001
IFFO2 -1.569966e-02 0.004 0.007
UBR4 4.983854e-01 0.121 0.038
RP1-43E13.2 6.563346e-03 0.006 0.004
EMC1 -3.052703e-02 0.010 0.012
MRTO4 -1.478492e-01 0.031 0.053
AKR7A2 1.495705e-01 0.171 0.125
PQLC2 1.154350e-02 0.013 0.007
CAPZB -3.942409e-02 0.548 0.563
MINOS1-NBL1 -7.915231e-03 0.000 0.002
MINOS1 -5.579122e-02 0.262 0.264
NBL1 -2.416721e-02 0.000 0.005
TMCO4 -5.559194e-02 0.006 0.013
OTUD3 5.409893e-02 0.010 0.003
UBXN10-AS1 -1.664253e-02 0.000 0.002
UBXN10 -5.432753e-03 0.000 0.001
CAMK2N1 -5.826301e-02 0.000 0.003
MUL1 -7.772366e-03 0.021 0.026
CDA 2.060354e+00 0.544 0.058
PINK1 1.509215e-01 0.046 0.018
PINK1-AS 6.928788e-03 0.010 0.008
DDOST -4.433892e-02 0.192 0.189
HP1BP3 -3.107734e-01 0.079 0.126
RP5-930J4.4 -9.031179e-03 0.000 0.001
EIF4G3 -2.394044e-02 0.006 0.010
ECE1 6.911880e-02 0.050 0.033
RP3-329E20.2 -1.043293e-02 0.000 0.001
NBPF3 -1.469782e-02 0.002 0.004
ALPL -8.662576e-03 0.000 0.001
USP48 -2.340587e-02 0.056 0.054
HSPG2 -1.502434e-02 0.000 0.002
RP1-224A6.3 5.655863e-03 0.002 0.001
LINC00339 -8.487187e-02 0.006 0.018
CDC42 -1.262787e-01 0.348 0.380
ZBTB40 -6.017991e-02 0.008 0.018
C1QA -9.316259e-02 0.017 0.013
C1QC -2.776394e-02 0.000 0.002
C1QB -4.048819e-02 0.004 0.007
EPHB2 1.041767e-02 0.004 0.002
KDM1A -2.375324e-02 0.021 0.022
LUZP1 -7.162162e-02 0.015 0.028
RP5-1057J7.6 -5.205044e-03 0.002 0.001
HNRNPR -4.392090e-01 0.123 0.225
ZNF436 -4.415926e-02 0.004 0.002
C1orf213 -6.562440e-03 0.000 0.001
TCEA3 -2.154492e-01 0.015 0.045
E2F2 5.249200e-02 0.015 0.007
ID3 -3.890228e-01 0.021 0.076
MDS2 -1.949829e-01 0.000 0.025
RPL11 -3.086715e-01 1.000 0.996
TCEB3 -9.892833e-02 0.019 0.031
RP5-886K2.3 -2.496814e-02 0.008 0.013
PITHD1 -3.952648e-01 0.069 0.151
LYPLA2 -1.281389e-01 0.081 0.086
GALE -4.432143e-02 0.008 0.014
HMGCL -1.977504e-01 0.040 0.053
FUCA1 2.498017e-01 0.073 0.029
CNR2 -7.878203e-02 0.000 0.009
PNRC2 -1.236901e-01 0.033 0.057
SRSF10 -1.955860e-01 0.025 0.054
IFNLR1 -2.162172e-02 0.000 0.003
STPG1 -5.691299e-02 0.002 0.012
NIPAL3 -2.999888e-01 0.019 0.069
RCAN3 -4.653407e-01 0.040 0.125
RP4-594I10.3 -5.838712e-02 0.000 0.009
SRRM1 -1.355575e-01 0.171 0.175
CLIC4 3.302778e-02 0.017 0.014
RUNX3 -1.806617e-01 0.067 0.090
SYF2 -2.832320e-02 0.398 0.376
C1orf63 -4.097265e-01 0.127 0.192
RP3-465N24.6 1.645835e-02 0.004 0.002
TMEM50A -2.494101e-01 0.246 0.271
TMEM57 -1.173546e-01 0.013 0.029
LDLRAP1 -7.981361e-01 0.027 0.149
RP11-70P17.1 -6.897598e-02 0.004 0.011
MAN1C1 -3.951201e-02 0.002 0.011
RP1-187B23.1 -1.891914e-02 0.008 0.009
SEPN1 4.786262e-02 0.023 0.013
RP1-317E23.3 -1.038133e-02 0.000 0.001
MTFR1L 2.371556e-01 0.073 0.046
AL020996.1 1.880819e-03 0.002 0.001
PAQR7 2.437612e-02 0.004 0.000
STMN1 -5.483368e-01 0.017 0.084
PAFAH2 -2.713999e-02 0.010 0.012
PDIK1L -1.713773e-01 0.002 0.013
ZNF593 1.597591e-01 0.096 0.054
CNKSR1 -9.947208e-03 0.000 0.002
CEP85 3.775482e-03 0.006 0.007
SH3BGRL3 8.641798e-01 0.973 0.833
UBXN11 5.780082e-01 0.181 0.057
CD52 -2.552909e-01 0.833 0.909
AIM1L -3.596177e-02 0.000 0.005
ZNF683 -1.093657e-01 0.002 0.016
DHDDS 1.345060e-02 0.017 0.018
HMGN2 1.580849e-01 0.102 0.093
RPS6KA1 -8.601323e-02 0.085 0.088
ARID1A 1.526485e-01 0.085 0.061
PIGV 2.109163e-03 0.021 0.017
ZDHHC18 2.254451e-03 0.019 0.015
GPN2 -1.996983e-02 0.027 0.031
GPATCH3 -8.922064e-03 0.017 0.018
NUDC -2.620021e-01 0.181 0.225
C1orf172 -2.590364e-02 0.000 0.004
TRNP1 2.412450e-03 0.002 0.001
SLC9A1 4.232937e-02 0.021 0.011
WDTC1 3.773947e-02 0.038 0.025
TMEM222 -2.122516e-01 0.029 0.070
SYTL1 -5.817626e-01 0.075 0.190
MAP3K6 -3.771882e-02 0.010 0.017
WASF2 3.188862e-01 0.277 0.178
RP1-159A19.4 -1.776124e-02 0.000 0.004
AHDC1 -1.767365e-02 0.008 0.009
FGR 1.096524e+00 0.502 0.152
IFI6 1.565802e+00 0.773 0.321
FAM76A -8.631183e-02 0.004 0.014
STX12 1.430702e-01 0.100 0.059
PPP1R8 -2.227218e-01 0.042 0.063
THEMIS2 7.836859e-01 0.256 0.086
RPA2 -7.236961e-01 0.062 0.209
XKR8 -9.824569e-04 0.023 0.029
EYA3 -9.279806e-03 0.010 0.010
PTAFR 6.383363e-01 0.106 0.012
DNAJC8 -3.062794e-01 0.175 0.229
AL353354.2 -2.396709e-03 0.002 0.003
ATPIF1 -3.895030e-01 0.240 0.329
RP5-1092A3.4 -5.030800e-02 0.000 0.007
SESN2 2.035592e-02 0.013 0.009
MED18 -1.332760e-02 0.004 0.007
PHACTR4 -1.388753e-01 0.035 0.038
RCC1 -2.926636e-02 0.017 0.021
TRNAU1AP -1.297726e-01 0.040 0.056
SNHG12 -2.283764e-01 0.025 0.059
TAF12 4.373150e-02 0.125 0.145
RP11-442N24--B.1 7.580135e-03 0.004 0.001
RNU11 -4.490895e-04 0.002 0.001
GMEB1 -7.863571e-02 0.019 0.034
YTHDF2 -4.035629e-01 0.077 0.137
EPB41 -1.789451e-01 0.042 0.068
TMEM200B -7.133444e-03 0.000 0.001
SRSF4 2.205702e-02 0.138 0.135
MECR -2.407613e-02 0.019 0.019
MATN1 6.125155e-03 0.002 0.001
MATN1-AS1 -8.641880e-02 0.000 0.013
LAPTM5 2.888276e-02 0.827 0.785
PUM1 2.588529e-01 0.052 0.029
SNRNP40 -3.139502e-01 0.062 0.133
ZCCHC17 -6.007964e-02 0.075 0.075
SERINC2 1.145346e-01 0.023 0.003
PEF1 -1.221568e-01 0.079 0.108
SPOCD1 -3.372977e-02 0.000 0.001
PTP4A2 2.431195e-02 0.267 0.230
KHDRBS1 -7.032664e-02 0.240 0.246
TMEM39B 5.960061e-02 0.038 0.026
KPNA6 1.517865e-02 0.044 0.040
TXLNA 7.034703e-02 0.021 0.012
[ reached 'max' / getOption("max.print") -- omitted 13381 rows ]
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