I'm trying to run anova on multiple Principal component with different categorical as well continuous variables using all possible combination.
The dimensions of my data frame is
dim(tcga_mrna.pcs55)
[1] 147 67
The no of combination of models i have to test is this 112585
which was generated from this
frms <- with(expand.grid(dv, rhs), paste(Var1, Var2, sep = ' ~ '))
Now I tried to run it once It was stuck for quite a while so i had to abort it give my computational resources.
Therefore I think if I split my data frame into smaller data frame where I want to keep all the predictors constant but I would like to break the other columns into small subset.
My data small subset
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"
))
So here the first 12 columns I want to keep constant while adding PC1 to PC10 in my first subset. Similarly I would keep the first 12 again constant then add PC11 to PC20 , this way small subset of data-frame till my last column with first 11 such as this as constant for each subset of dataframe.
[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
My objective is to run to run this since with such huge no of combination its taking a lot of time, so in a crude way i thought if the data frame can be split it would be easier to run. If there is faster way to execute the below code i would be glad to know.
Any help or suggestion are really appreciated.
models <- lapply(frms, function(x) anova(lm(x, data = tcga_mrna.pcs55)))
CodePudding user response：
Here is a try! I searched a lot but was not able to find a simple solution So this is a sugesstion how you could bring your shorter dataframes in a list. It is tedious but once you got a list, you could apply your operations to each element of the list:
The nearest solution I found was here: R: Splitting dataframe columnwise. But here only one column is added to the constant columns!
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
output:
> 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