我正在嘗試使用所有可能的組合對具有不同分類和連續變數的多個主成分運行方差分析。
我的資料框的尺寸是
dim(tcga_mrna.pcs55)
[1] 147 67
我必須測驗的模型組合數是這個112585
這是由此產生的
frms <- with(expand.grid(dv, rhs), paste(Var1, Var2, sep = ' ~ '))
現在我嘗試運行它一次它卡住了很長一段時間，所以我不得不放棄它給我的計算資源。
因此，我認為如果我將我的資料框拆分為較小的資料框，我想在其中保持所有預測變數不變，但我想將其他列分成小子集。
我的資料小子集
dput(head(tcga_mrna_pcs55))
structure(list(Sample_ID = c("TCGA-AB-2856", "TCGA-AB-2849",
"TCGA-AB-2971", "TCGA-AB-2930", "TCGA-AB-2891", "TCGA-AB-2872"
), FAB = c("M4", "M0", "M4", "M2", "M1", "M3"), prior_malignancy = c("no",
"no", "no", "no", "no", "no"), Age = c(63, 39, 76, 62, 42, 42
), BM_percentage = c(82, 83, 91, 72, 68, 88), Cytogenetic_Code = c("Normal Karyotype",
"Complex Cytogenetics", "Normal Karyotype", "Normal Karyotype",
"Complex Cytogenetics", "PML-RARA"), Histologic_Subtype = c("NUP98 Translocation",
"Complex Cytogenetics", "Normal Karyotype", "NUP98 Translocation",
"Complex Cytogenetics", "PML-RARA"), Risk_Cyto = c("Intermediate",
"Poor", "Intermediate", "Intermediate", "Poor", "Good"), Risk_Molecular = c("Poor",
"Poor", "Intermediate", "Poor", "Poor", "Good"), Sex = c("Male",
"Male", "Female", "Female", "Male", "Male"), TMB = c(0, 0.733333333333,
0.3, 0.266666666667, 0.466666666667, 0.333333333333), WBC = c(76.7,
5, 5, 27.7, 10.7, 2.1), PC1 = c(-25.4243169876343, 38.5584419151387,
-18.8838255683554, 3.773812175371, -5.02868029999407, 21.4658284982092
), PC2 = c(14.4895578447888, -27.8233346053999, -0.318074813205288,
6.17043126174388, -9.29150756229324, 35.1156168048889), PC3 = c(-10.6509445605983,
28.0996432599761, 5.88270605324811, -26.4971717145656, -0.896362785151599,
23.2794429531062), PC4 = c(1.18248804745738, -21.0145760152975,
-13.6652202316835, 4.64544888299446, 6.10552116611012, 1.085498115633
), PC5 = c(-14.8325881422899, 17.8653710387376, 8.90002489087104,
-0.550793434039587, 5.90790796345414, 13.7446793572887), PC6 = c(0.695367268633542,
-7.46255391237719, -9.48973541984696, 5.27626778248046, 2.85645531301921,
-2.5417697261715), PC7 = c(-16.7000152968204, 14.3887321471474,
16.0657716315069, -9.86610587188809, -8.27832660111485, -3.14876491002283
), PC8 = c(2.79822148585397, -6.63528657940777, -12.8725509038156,
-2.17579923819722, -12.5781664467208, -2.90943809569856), PC9 = c(-7.05331558116121,
-12.1985749853038, 4.10613337565274, -20.0374908146072, -13.4276520442583,
-2.77032899744962), PC10 = c(13.2132444645362, -2.82152344784948,
-8.00771994862333, 5.3333694628255, -6.78114804624295, -5.63354620465723
), PC11 = c(-1.79050241538047, -6.57822316228283, -4.20132241912175,
4.51589800987586, -1.67953673784626, 3.75349242056027), PC12 = c(7.83152902157972,
-19.5950183628134, -9.38164109885085, 16.3690122002304, 0.0735031667926224,
2.32446981112219), PC13 = c(-5.25219547328429, -7.13380025578665,
6.09600053996671, -7.11925980557811, -5.61967462665635, -9.80647746645279
), PC14 = c(1.45188764160216, -25.5978607332207, 18.3643001800981,
4.7265900178811, -15.071134439125, 11.3956478391763), PC15 = c(-7.3393199774991,
-33.112294903764, -4.10920083616075, -11.3366588668303, 2.5968258382962,
14.4766162599917), PC16 = c(0.529278749351839, -20.0921377085554,
9.88228975185339, -0.264632117869371, 4.39109257712349, 17.8403742741107
), PC17 = c(-5.79919206631477, -34.4597935232432, -0.284077310829092,
-1.45723530362592, 8.066297152665, -4.36479763922708), PC18 = c(6.16739223066386,
-0.668191107754327, 7.17864592583405, 1.10258322969635, -2.88635363509576,
-3.55077626222531), PC19 = c(-2.46075725680638, 11.2317147986833,
10.7210109810505, -1.86175537360617, 9.00649577117842, -5.20964171868026
), PC20 = c(0.447290924483848, 0.882697730068387, -1.64992531160428,
3.69926682756107, -8.45636279736397, 12.0178514144455), PC21 = c(7.77512402052619,
-13.723689855566, 0.929876575603838, 7.20400850159562, -0.614055839592973,
-6.15633968149479), PC22 = c(-1.56535673338356, -13.2971868706006,
1.87562172644287, -3.28771663165701, -5.64722916304599, 0.636358407474463
), PC23 = c(0.164107670637167, -15.2249958235848, 8.00555210033773,
2.0662276295149, 7.73028430813706, -2.32179860594496), PC24 = c(-1.8934805361982,
8.21971891071679, 3.08512611513449, -0.628702548440314, -0.233105377199397,
2.87674317483379), PC25 = c(0.893451809081066, 6.60513492724147,
8.88171627539804, 2.97249584622476, -17.4778489423161, -4.58539478100194
), PC26 = c(-1.32955071985976, 11.9145713692928, -3.79820868194203,
4.91276198192432, 1.14456788292366, 9.69280466752626), PC27 = c(5.80488907470531,
-9.84420624259338, 2.14543167774679, -3.04254310413812, 5.7902970935943,
-3.75331337674036), PC28 = c(-8.18472344420157, 1.65255506997329,
7.07760527456274, -6.32026527255729, -4.33442214041778, -6.65351307662841
), PC29 = c(1.75032780020844, 15.5611773097845, -2.52903882532741,
2.53566972972068, 6.44542594461733, -2.73677227120317), PC30 = c(-0.862387620806526,
-14.0405815436268, -7.08059737134561, -0.429947697667332, -4.93506927070922,
-7.24877851150857), PC31 = c(5.04914290995488, 1.94876316261089,
-1.44943546186944, 0.589695885543367, 7.55928674782029, -2.70932468259665
), PC32 = c(-0.331134735300882, 6.19579420256524, -1.11785338261286,
-1.29691032897408, 20.2001081109543, 7.8570225951223), PC33 = c(4.89375087245026,
6.48463626836495, 6.73612277868434, 4.24109357290756, 1.02817278604743,
0.680027817141749), PC34 = c(-0.800041139194579, -1.88905732488826,
1.7772915935601, -0.499932283505083, 10.7430548643924, -6.53775164240871
), PC35 = c(5.12118821250308, -3.98313005901599, -4.52005990894197,
-3.07369863487262, 3.92078873433114, -2.18933519508166), PC36 = c(-2.54985917927219,
-1.70921978278497, -2.44961274490961, 1.56802927495698, 7.08687990990386,
-0.604700521943517), PC37 = c(5.1747232970747, -5.34247962945995,
-1.83839184464979, 6.70262336281884, -1.10932786180704, -3.25652639774021
), PC38 = c(-4.18410989825183, -6.98950710609193, 0.866526234992652,
-0.0950366191443256, 3.35399502292955, 2.90766983495248), PC39 = c(2.46730811173428,
-0.455543469604487, -4.63050936679246, -1.34675190382428, -6.1200022250839,
-3.40619104956874), PC40 = c(-0.731471474196848, -4.24515300461387,
-3.43245666463953, 3.70020703587818, -8.76472221293956, -1.1281798870577
), PC41 = c(-3.79301551015471, -5.25686203441764, 6.76297802293118,
-3.68970972173239, 4.35055761452324, -18.4180107861132), PC42 = c(4.83388024710314,
-0.25083519933247, -3.21152818097955, 5.96597185780427, 4.19254774340514,
-8.18426155110418), PC43 = c(-0.217047959384719, -1.13621909801165,
-4.4592933756817, -6.96360564960356, 2.27400449542372, -2.86813634075033
), PC44 = c(-3.33545179774935, 6.11834882717519, -0.264585462886141,
-7.6792938724774, -3.99915221656525, -2.5294702493956), PC45 = c(2.77954857939566,
7.82470034842594, -3.52534065178766, -2.56221337540028, 7.09562358045148,
-1.49373245991455), PC46 = c(-1.60423065922446, -0.428508391589366,
4.03490498808649, 2.12844259167901, -1.3678347436909, -6.13180626071563
), PC47 = c(-3.20068124812043, 5.06644140525654, 7.37963017443048,
-4.84325578581087, -17.680506272578, 0.560814898057312), PC48 = c(2.91858197345977,
-1.11915083153502, 3.47278363466071, 1.21240736359339, -5.58511090848592,
5.52652026954627), PC49 = c(3.84744380211926, 0.861663719832773,
-1.40060221851844, 1.62791310594578, -2.52243080963911, 0.361029214307694
), PC50 = c(5.15785104158866, -0.319668135009027, 4.80115302565519,
4.45746767521537, 2.76979916871901, -10.7678984312634), PC51 = c(-6.22760710964996,
-3.55897006680048, -1.68421228474145, -1.51499187118043, 4.69802013777757,
-7.25050359857057), PC52 = c(-2.26345921059907, 3.60461592062774,
-1.37792205061882, 8.69053064558714, -10.7983766769631, -2.63687558522692
), PC53 = c(-1.65172511606967, 0.118920655863908, 6.29953754003559,
-3.16092526827426, -3.64199764016276, -6.98013560579073), PC54 = c(6.17213064069784,
3.78913668381605, 5.94121227070784, 1.6838389802013, 2.47727981128471,
1.71804579216696), PC55 = c(-3.7893860872842, -0.325634230487849,
-5.98312342448493, -5.37971579967361, -6.71876005026094, -4.19058766854014
)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))
因此，在我的第一個子集中將 PC1 添加到 PC10 時，我希望保持前 12 列不變。同樣，我將再次保持前 12 個不變，然后將 PC11 添加到 PC20 ，這樣資料幀的小子集直到我的最后一列，第一個11這樣的資料幀的每個子集都是常數。
[1] "FAB" "prior_malignancy" "Age" "BM_percentage" "Cytogenetic_Code" "Histologic_Subtype"
[7] "Risk_Cyto" "Risk_Molecular" "Sex" "TMB" "WBC"
Sample_ID FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subt… Risk_Cyto Risk_Molecular Sex TMB WBC PC1 PC2
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 TCGA-AB-… M4 no 63 82 Normal Karyotype NUP98 Transloca… Intermed… Poor Male 0 76.7 -25.4 14.5
2 TCGA-AB-… M0 no 39 83 Complex Cytogen… Complex Cytogen… Poor Poor Male 0.733 5 38.6 -27.8
3 TCGA-AB-… M4 no 76 91 Normal Karyotype Normal Karyotype Intermed… Intermediate Fema… 0.3 5 -18.9 -0.318
4 TCGA-AB-… M2 no 62 72 Normal Karyotype NUP98 Transloca… Intermed… Poor Fema… 0.267 27.7 3.77 6.17
5 TCGA-AB-… M1 no 42 68 Complex Cytogen… Complex Cytogen… Poor Poor Male 0.467 10.7 -5.03 -9.29
6 TCGA-AB-… M3 no 42 88 PML-RARA PML-RARA Good Good Male 0.333 2.1 21.5 35.1
我的 目標是運行它，因為沒有這么大的組合需要很多時間，所以我粗略地認為如果資料框可以拆分，它會更容易運行。如果有更快的方法來執行以下代碼，我會很高興知道。
任何幫助或建議都非常感謝。
models <- lapply(frms, function(x) anova(lm(x, data = tcga_mrna.pcs55)))
uj5u.com熱心網友回復：
這是一個嘗試！我進行了很多搜索，但找不到簡單的解決方案所以這是一個建議，您可以如何將較短的資料幀放入串列中。這很乏味，但是一旦你得到一個串列，你就可以將你的操作應用于串列的每個元素：
我找到的最接近的解決方案是：R: Splitting dataframe columnwise。但是這里只有一列被添加到常量列中！
library(dplyr)
col1_12 <- df %>%
select(1:12)
PC1_PC10 <- df %>%
select(1, 13:22) %>%
right_join(col1_12, by = "Sample_ID")
PC11_PC20 <- df %>%
select(1, 23:32) %>%
right_join(col1_12, by = "Sample_ID")
PC21_PC30 <- df %>%
select(1, 33:42) %>%
right_join(col1_12, by = "Sample_ID")
PC31_PC40 <- df %>%
select(1, 43:52) %>%
right_join(col1_12, by = "Sample_ID")
PC41_PC50 <- df %>%
select(1, 53:62) %>%
right_join(col1_12, by = "Sample_ID")
PC51_PC55 <- df %>%
select(1, 63:67) %>%
right_join(col1_12, by = "Sample_ID")
list_of_dfs <- list(PC1_PC10, PC11_PC20, PC21_PC30,
PC31_PC41, PC41_PC50, PC51_PC55)
list_of_dfs
輸出：
> list_of_dfs
[[1]]
# A tibble: 6 x 22
Sample_ID PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subtype Risk_Cyto Risk_Molecular Sex TMB WBC
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 TCGA-AB-2856 -25.4 14.5 -10.7 1.18 -14.8 0.695 -16.7 2.80 -7.05 13.2 M4 no 63 82 Normal Karyotype NUP98 Translocation Intermed~ Poor Male 0 76.7
2 TCGA-AB-2849 38.6 -27.8 28.1 -21.0 17.9 -7.46 14.4 -6.64 -12.2 -2.82 M0 no 39 83 Complex Cytogenetics Complex Cytogeneti~ Poor Poor Male 0.733 5
3 TCGA-AB-2971 -18.9 -0.318 5.88 -13.7 8.90 -9.49 16.1 -12.9 4.11 -8.01 M4 no 76 91 Normal Karyotype Normal Karyotype Intermed~ Intermediate Fema~ 0.3 5
4 TCGA-AB-2930 3.77 6.17 -26.5 4.65 -0.551 5.28 -9.87 -2.18 -20.0 5.33 M2 no 62 72 Normal Karyotype NUP98 Translocation Intermed~ Poor Fema~ 0.267 27.7
5 TCGA-AB-2891 -5.03 -9.29 -0.896 6.11 5.91 2.86 -8.28 -12.6 -13.4 -6.78 M1 no 42 68 Complex Cytogenetics Complex Cytogeneti~ Poor Poor Male 0.467 10.7
6 TCGA-AB-2872 21.5 35.1 23.3 1.09 13.7 -2.54 -3.15 -2.91 -2.77 -5.63 M3 no 42 88 PML-RARA PML-RARA Good Good Male 0.333 2.1
[[2]]
# A tibble: 6 x 22
Sample_ID PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subtype Risk_Cyto Risk_Molecular Sex TMB WBC
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 TCGA-AB-2856 -1.79 7.83 -5.25 1.45 -7.34 0.529 -5.80 6.17 -2.46 0.447 M4 no 63 82 Normal Karyotype NUP98 Translocation Intermed~ Poor Male 0 76.7
2 TCGA-AB-2849 -6.58 -19.6 -7.13 -25.6 -33.1 -20.1 -34.5 -0.668 11.2 0.883 M0 no 39 83 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.733 5
3 TCGA-AB-2971 -4.20 -9.38 6.10 18.4 -4.11 9.88 -0.284 7.18 10.7 -1.65 M4 no 76 91 Normal Karyotype Normal Karyotype Intermed~ Intermediate Fema~ 0.3 5
4 TCGA-AB-2930 4.52 16.4 -7.12 4.73 -11.3 -0.265 -1.46 1.10 -1.86 3.70 M2 no 62 72 Normal Karyotype NUP98 Translocation Intermed~ Poor Fema~ 0.267 27.7
5 TCGA-AB-2891 -1.68 0.0735 -5.62 -15.1 2.60 4.39 8.07 -2.89 9.01 -8.46 M1 no 42 68 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.467 10.7
6 TCGA-AB-2872 3.75 2.32 -9.81 11.4 14.5 17.8 -4.36 -3.55 -5.21 12.0 M3 no 42 88 PML-RARA PML-RARA Good Good Male 0.333 2.1
[[3]]
# A tibble: 6 x 22
Sample_ID PC21 PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subtype Risk_Cyto Risk_Molecular Sex TMB WBC
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 TCGA-AB-2856 7.78 -1.57 0.164 -1.89 0.893 -1.33 5.80 -8.18 1.75 -0.862 M4 no 63 82 Normal Karyotype NUP98 Translocation Intermed~ Poor Male 0 76.7
2 TCGA-AB-2849 -13.7 -13.3 -15.2 8.22 6.61 11.9 -9.84 1.65 15.6 -14.0 M0 no 39 83 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.733 5
3 TCGA-AB-2971 0.930 1.88 8.01 3.09 8.88 -3.80 2.15 7.08 -2.53 -7.08 M4 no 76 91 Normal Karyotype Normal Karyotype Intermed~ Intermediate Fema~ 0.3 5
4 TCGA-AB-2930 7.20 -3.29 2.07 -0.629 2.97 4.91 -3.04 -6.32 2.54 -0.430 M2 no 62 72 Normal Karyotype NUP98 Translocation Intermed~ Poor Fema~ 0.267 27.7
5 TCGA-AB-2891 -0.614 -5.65 7.73 -0.233 -17.5 1.14 5.79 -4.33 6.45 -4.94 M1 no 42 68 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.467 10.7
6 TCGA-AB-2872 -6.16 0.636 -2.32 2.88 -4.59 9.69 -3.75 -6.65 -2.74 -7.25 M3 no 42 88 PML-RARA PML-RARA Good Good Male 0.333 2.1
[[4]]
# A tibble: 6 x 25
Sample_ID PC31 PC32 PC33 PC34 PC35 PC36 PC37 PC38 PC39 PC40 PC41 PC42 PC43 FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subt~ Risk_Cyto Risk_Molecular Sex
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 TCGA-AB-2856 5.05 -0.331 4.89 -0.800 5.12 -2.55 5.17 -4.18 2.47 -0.731 -3.79 4.83 -0.217 M4 no 63 82 Normal Karyotype NUP98 Transloca~ Intermed~ Poor Male
2 TCGA-AB-2849 1.95 6.20 6.48 -1.89 -3.98 -1.71 -5.34 -6.99 -0.456 -4.25 -5.26 -0.251 -1.14 M0 no 39 83 Complex Cytogenet~ Complex Cytogen~ Poor Poor Male
3 TCGA-AB-2971 -1.45 -1.12 6.74 1.78 -4.52 -2.45 -1.84 0.867 -4.63 -3.43 6.76 -3.21 -4.46 M4 no 76 91 Normal Karyotype Normal Karyotype Intermed~ Intermediate Fema~
4 TCGA-AB-2930 0.590 -1.30 4.24 -0.500 -3.07 1.57 6.70 -0.0950 -1.35 3.70 -3.69 5.97 -6.96 M2 no 62 72 Normal Karyotype NUP98 Transloca~ Intermed~ Poor Fema~
5 TCGA-AB-2891 7.56 20.2 1.03 10.7 3.92 7.09 -1.11 3.35 -6.12 -8.76 4.35 4.19 2.27 M1 no 42 68 Complex Cytogenet~ Complex Cytogen~ Poor Poor Male
6 TCGA-AB-2872 -2.71 7.86 0.680 -6.54 -2.19 -0.605 -3.26 2.91 -3.41 -1.13 -18.4 -8.18 -2.87 M3 no 42 88 PML-RARA PML-RARA Good Good Male
# ... with 2 more variables: TMB <dbl>, WBC <dbl>
[[5]]
# A tibble: 6 x 22
Sample_ID PC41 PC42 PC43 PC44 PC45 PC46 PC47 PC48 PC49 PC50 FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subtype Risk_Cyto Risk_Molecular Sex TMB WBC
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 TCGA-AB-2856 -3.79 4.83 -0.217 -3.34 2.78 -1.60 -3.20 2.92 3.85 5.16 M4 no 63 82 Normal Karyotype NUP98 Translocation Intermedi~ Poor Male 0 76.7
2 TCGA-AB-2849 -5.26 -0.251 -1.14 6.12 7.82 -0.429 5.07 -1.12 0.862 -0.320 M0 no 39 83 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.733 5
3 TCGA-AB-2971 6.76 -3.21 -4.46 -0.265 -3.53 4.03 7.38 3.47 -1.40 4.80 M4 no 76 91 Normal Karyotype Normal Karyotype Intermedi~ Intermediate Fema~ 0.3 5
4 TCGA-AB-2930 -3.69 5.97 -6.96 -7.68 -2.56 2.13 -4.84 1.21 1.63 4.46 M2 no 62 72 Normal Karyotype NUP98 Translocation Intermedi~ Poor Fema~ 0.267 27.7
5 TCGA-AB-2891 4.35 4.19 2.27 -4.00 7.10 -1.37 -17.7 -5.59 -2.52 2.77 M1 no 42 68 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.467 10.7
6 TCGA-AB-2872 -18.4 -8.18 -2.87 -2.53 -1.49 -6.13 0.561 5.53 0.361 -10.8 M3 no 42 88 PML-RARA PML-RARA Good Good Male 0.333 2.1
[[6]]
# A tibble: 6 x 17
Sample_ID PC51 PC52 PC53 PC54 PC55 FAB prior_malignancy Age BM_percentage Cytogenetic_Code Histologic_Subtype Risk_Cyto Risk_Molecular Sex TMB WBC
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 TCGA-AB-2856 -6.23 -2.26 -1.65 6.17 -3.79 M4 no 63 82 Normal Karyotype NUP98 Translocation Intermediate Poor Male 0 76.7
2 TCGA-AB-2849 -3.56 3.60 0.119 3.79 -0.326 M0 no 39 83 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.733 5
3 TCGA-AB-2971 -1.68 -1.38 6.30 5.94 -5.98 M4 no 76 91 Normal Karyotype Normal Karyotype Intermediate Intermediate Female 0.3 5
4 TCGA-AB-2930 -1.51 8.69 -3.16 1.68 -5.38 M2 no 62 72 Normal Karyotype NUP98 Translocation Intermediate Poor Female 0.267 27.7
5 TCGA-AB-2891 4.70 -10.8 -3.64 2.48 -6.72 M1 no 42 68 Complex Cytogenetics Complex Cytogenetics Poor Poor Male 0.467 10.7
6 TCGA-AB-2872 -7.25 -2.64 -6.98 1.72 -4.19 M3 no 42 88 PML-RARA PML-RARA Good Good Male 0.333 2.1
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