将大数据框按列拆分为较小的子集
Splitting up a big data-frame into smaller subset column wise
我正在尝试 运行 使用所有可能的组合对具有不同分类变量和连续变量的多个主成分进行方差分析。
我的数据框的尺寸是
dim(tcga_mrna.pcs55)
[1] 147 67
我要测试的模型组合编号是这个112585
由此生成
frms <- with(expand.grid(dv, rhs), paste(Var1, Var2, sep = ' ~ '))
现在我尝试 运行 它曾经卡住了很长一段时间所以我不得不中止它给我的计算资源。
因此，我认为如果我将我的数据框拆分成更小的数据框，我想在其中保持所有预测变量不变，但我想将其他列分成小的子集。
我的数据小子集
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"
))
所以这里的前 12 列我想在我的第一个子集中将 PC1 添加到 PC10 时保持不变。类似地，我会再次保持前 12 个不变，然后将 PC11 添加到 PC20，这样数据帧的小子集直到我的最后一列第一个 11 例如作为每个数据帧子集的常量。
[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 objective 是 运行 运行 因为没有这么大的组合需要很多时间，所以在一种粗略的方法，我认为如果可以拆分数据框，运行 会更容易。如果有更快的方法来执行下面的代码，我会很高兴知道。
非常感谢任何帮助或建议。
models <- lapply(frms, function(x) anova(lm(x, data = tcga_mrna.pcs55)))
试试吧！我搜索了很多但无法找到一个简单的解决方案所以这是一个如何将较短的数据帧放入列表中的建议。这很乏味，但是一旦你得到一个列表，你就可以将你的操作应用于列表的每个元素：
我找到的最近的解决方案在这里：。但是这里只在常量列中增加了一列！
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
输出：
> 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
我正在尝试 运行 使用所有可能的组合对具有不同分类变量和连续变量的多个主成分进行方差分析。
我的数据框的尺寸是
dim(tcga_mrna.pcs55)
[1] 147 67
我要测试的模型组合编号是这个112585
由此生成
frms <- with(expand.grid(dv, rhs), paste(Var1, Var2, sep = ' ~ '))
现在我尝试 运行 它曾经卡住了很长一段时间所以我不得不中止它给我的计算资源。
因此，我认为如果我将我的数据框拆分成更小的数据框，我想在其中保持所有预测变量不变，但我想将其他列分成小的子集。
我的数据小子集
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"
))
所以这里的前 12 列我想在我的第一个子集中将 PC1 添加到 PC10 时保持不变。类似地，我会再次保持前 12 个不变，然后将 PC11 添加到 PC20，这样数据帧的小子集直到我的最后一列第一个 11 例如作为每个数据帧子集的常量。
[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 objective 是 运行 运行 因为没有这么大的组合需要很多时间，所以在一种粗略的方法，我认为如果可以拆分数据框，运行 会更容易。如果有更快的方法来执行下面的代码，我会很高兴知道。
非常感谢任何帮助或建议。
models <- lapply(frms, function(x) anova(lm(x, data = tcga_mrna.pcs55)))
试试吧！我搜索了很多但无法找到一个简单的解决方案所以这是一个如何将较短的数据帧放入列表中的建议。这很乏味，但是一旦你得到一个列表，你就可以将你的操作应用于列表的每个元素：
我找到的最近的解决方案在这里：
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
输出：
> 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