ParkRNASeq

Loading data and preparing

library("DESeq2")
library(DT)

directory <- "htseq_out"

DeSeq2 of all samples

Load metadata and output samples names

sampleTable <- read.table("metadata.tsv", sep="\t", header = TRUE)
sampleTable$condition <- factor(sampleTable$Health.PD)
print(sampleTable$ID)

 [1] "Po4S_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3" 
 [2] "Po4S_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4" 
 [3] "Po4S_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3" 
 [4] "Po4S_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4" 
 [5] "FF9S_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3" 
 [6] "FF9S_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4" 
 [7] "FF9S_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3" 
 [8] "FF9S_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4" 
 [9] "BL6S_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3" 
[10] "BL6S_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4" 
[11] "BL6S_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3" 
[12] "BL6S_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4" 
[13] "Tr5L_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3" 
[14] "Tr5L_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4" 
[15] "Tr5L_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3" 
[16] "Tr5L_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4" 
[17] "P12_1_7_11_22_1_EKDL220017801-1A_HKLKJDSX5_L3"  
[18] "P12_1_7_11_22_1_EKDL220017801-1A_HMNK7DSX5_L4"  
[19] "P12_1_7_11_22_2_EKDL220017801-1A_HKLKJDSX5_L3"  
[20] "P12_1_7_11_22_2_EKDL220017801-1A_HMNK7DSX5_L4"  
[21] "Park14_7_11_22_1_EKDL220017801-1A_HKLKJDSX5_L3" 
[22] "Park14_7_11_22_1_EKDL220017801-1A_HMNK7DSX5_L4" 
[23] "Park14_7_11_22_2_EKDL220017801-1A_HKLKJDSX5_L3" 
[24] "Park14_7_11_22_2_EKDL220017801-1A_HMNK7DSX5_L4" 
[25] "Po4S_DANs_F1_EKDL230000933-1A_HNHG2ALXX_L2"     
[26] "Po4S_DANs_F2_EKDL230000933-1A_HNHG2ALXX_L2"     
[27] "FF9S_DANs_E1_EKDL230000933-1A_HNHG2ALXX_L2"     
[28] "FF9S_DANs_E2_EKDL230000933-1A_HNHG2ALXX_L2"     
[29] "Bl6S_DANs_H1_EKDL230000933-1A_HNHG2ALXX_L2"     
[30] "Bl6S_DANs_H2_EKDL230000933-1A_HNHG2ALXX_L2"     
[31] "P12_1_DANs_D1_EKDL230000933-1A_HNHG2ALXX_L2"    
[32] "P12_1_DANs_D2_EKDL230000933-1A_HNHG2ALXX_L2"    
[33] "Park_14_cl_4_D_B1_EKDL230000933-1A_HNHG2ALXX_L2"
[34] "Park_14_cl_4_D_B2_EKDL230000933-1A_HNHG2ALXX_L2"

Load data and print number of transcripts

ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
                                       directory = directory,
                                       design= ~ condition)

print(ddsHTSeq@elementMetadata@nrows)

[1] 86402

Filter data to keep transcripts which have at least 10 reads in total

keep <- rowSums(counts(ddsHTSeq)) >= 10
ddsHTSeq <- ddsHTSeq[keep,]
print(ddsHTSeq@elementMetadata@nrows)

[1] 40528

Analysis

dds <- DESeq(ddsHTSeq)

estimating size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 591 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

resultsNames(dds) 

[1] "Intercept"         "condition_PD_vs_H"

res <- results(dds, name="condition_PD_vs_H")

resOrderedAll <- res[order(res$pvalue),]

summary(resOrderedAll)

out of 40523 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2133, 5.3%
LFC < 0 (down)     : 2928, 7.2%
outliers [1]       : 0, 0%
low counts [2]     : 7862, 19%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

datatable(as.data.frame(resOrderedAll@listData, row.names = resOrderedAll@rownames))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

DeSeq2 of SPb

Load metadata, filter SPb samples and output samples names

sampleTable <- read.table("metadata.tsv", sep="\t", header = TRUE)
sampleTable$condition <- factor(sampleTable$Health.PD)
sampleTable <- sampleTable[sampleTable$Cells.Msc.SPb=='SPb', ]
print(sampleTable$ID)

 [1] "Po4S_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3"
 [2] "Po4S_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4"
 [3] "Po4S_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3"
 [4] "Po4S_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4"
 [5] "FF9S_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3"
 [6] "FF9S_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4"
 [7] "FF9S_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3"
 [8] "FF9S_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4"
 [9] "BL6S_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3"
[10] "BL6S_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4"
[11] "BL6S_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3"
[12] "BL6S_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4"
[13] "Tr5L_7_11_2022_1_EKDL220017801-1A_HKLKJDSX5_L3"
[14] "Tr5L_7_11_2022_1_EKDL220017801-1A_HMNK7DSX5_L4"
[15] "Tr5L_7_11_2022_2_EKDL220017801-1A_HKLKJDSX5_L3"
[16] "Tr5L_7_11_2022_2_EKDL220017801-1A_HMNK7DSX5_L4"
[17] "P12_1_7_11_22_1_EKDL220017801-1A_HKLKJDSX5_L3" 
[18] "P12_1_7_11_22_1_EKDL220017801-1A_HMNK7DSX5_L4" 
[19] "P12_1_7_11_22_2_EKDL220017801-1A_HKLKJDSX5_L3" 
[20] "P12_1_7_11_22_2_EKDL220017801-1A_HMNK7DSX5_L4" 
[21] "Park14_7_11_22_1_EKDL220017801-1A_HKLKJDSX5_L3"
[22] "Park14_7_11_22_1_EKDL220017801-1A_HMNK7DSX5_L4"
[23] "Park14_7_11_22_2_EKDL220017801-1A_HKLKJDSX5_L3"
[24] "Park14_7_11_22_2_EKDL220017801-1A_HMNK7DSX5_L4"

Load data and print number of transcripts

ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
                                       directory = directory,
                                       design= ~ condition)

print(ddsHTSeq@elementMetadata@nrows)

[1] 86402

Filter data to keep transcripts which have at least 10 reads in total

keep <- rowSums(counts(ddsHTSeq)) >= 10
ddsHTSeq <- ddsHTSeq[keep,]
print(ddsHTSeq@elementMetadata@nrows)

[1] 35003

Analysis

dds <- DESeq(ddsHTSeq)

estimating size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

-- replacing outliers and refitting for 83 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)

estimating dispersions

fitting model and testing

resultsNames(dds) 

[1] "Intercept"         "condition_PD_vs_H"

res <- results(dds, name="condition_PD_vs_H")

resOrderedSPb <- res[order(res$pvalue),]

summary(resOrderedSPb)

out of 35003 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2644, 7.6%
LFC < 0 (down)     : 2810, 8%
outliers [1]       : 0, 0%
low counts [2]     : 5429, 16%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

datatable(as.data.frame(resOrderedSPb@listData, row.names = resOrderedSPb@rownames))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

DeSeq2 of Msc

Load metadata, filter Msc samples and output samples names

sampleTable <- read.table("metadata.tsv", sep="\t", header = TRUE)
sampleTable$condition <- factor(sampleTable$Health.PD)
sampleTable <- sampleTable[sampleTable$Cells.Msc.SPb=='Msc', ]
print(sampleTable$ID)

 [1] "Po4S_DANs_F1_EKDL230000933-1A_HNHG2ALXX_L2"     
 [2] "Po4S_DANs_F2_EKDL230000933-1A_HNHG2ALXX_L2"     
 [3] "FF9S_DANs_E1_EKDL230000933-1A_HNHG2ALXX_L2"     
 [4] "FF9S_DANs_E2_EKDL230000933-1A_HNHG2ALXX_L2"     
 [5] "Bl6S_DANs_H1_EKDL230000933-1A_HNHG2ALXX_L2"     
 [6] "Bl6S_DANs_H2_EKDL230000933-1A_HNHG2ALXX_L2"     
 [7] "P12_1_DANs_D1_EKDL230000933-1A_HNHG2ALXX_L2"    
 [8] "P12_1_DANs_D2_EKDL230000933-1A_HNHG2ALXX_L2"    
 [9] "Park_14_cl_4_D_B1_EKDL230000933-1A_HNHG2ALXX_L2"
[10] "Park_14_cl_4_D_B2_EKDL230000933-1A_HNHG2ALXX_L2"

Load data and print number of transcripts

ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
                                       directory = directory,
                                       design= ~ condition)

print(ddsHTSeq@elementMetadata@nrows)

[1] 86402

Filter data to keep transcripts which have at least 10 reads in total

keep <- rowSums(counts(ddsHTSeq)) >= 10
ddsHTSeq <- ddsHTSeq[keep,]
print(ddsHTSeq@elementMetadata@nrows)

[1] 34969

Analysis

dds <- DESeq(ddsHTSeq)

estimating size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

resultsNames(dds) 

[1] "Intercept"         "condition_PD_vs_H"

res <- results(dds, name="condition_PD_vs_H")

resOrderedMsc <- res[order(res$pvalue),]

summary(resOrderedMsc)

out of 34969 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 3565, 10%
LFC < 0 (down)     : 3357, 9.6%
outliers [1]       : 1, 0.0029%
low counts [2]     : 4068, 12%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

datatable(as.data.frame(resOrderedMsc@listData, row.names = resOrderedMsc@rownames))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

Compare results

print("All")

[1] "All"

print(resOrderedAll@rownames[0:10])

 [1] "ENSG00000230456.10" "ENSG00000162624.16" "ENSG00000269983.1" 
 [4] "ENSG00000186960.15" "ENSG00000180574.4"  "ENSG00000259551.2" 
 [7] "ENSG00000176165.13" "ENSG00000204179.11" "ENSG00000261213.1" 
[10] "ENSG00000229807.14"

print("SPb")

[1] "SPb"

print(resOrderedSPb@rownames[0:10])

 [1] "ENSG00000230456.10" "ENSG00000186960.15" "ENSG00000162624.16"
 [4] "ENSG00000177133.15" "ENSG00000165588.19" "ENSG00000259551.2" 
 [7] "ENSG00000257126.7"  "ENSG00000104059.5"  "ENSG00000176165.13"
[10] "ENSG00000269983.1" 

print("Msc")

[1] "Msc"

print(resOrderedMsc@rownames[0:10])

 [1] "ENSG00000163661.4"  "ENSG00000163873.10" "ENSG00000114204.16"
 [4] "ENSG00000105880.7"  "ENSG00000135048.14" "ENSG00000136750.13"
 [7] "ENSG00000230456.10" "ENSG00000006377.11" "ENSG00000231764.12"
[10] "ENSG00000155761.14"

All transcripts with adj p-val <1e-10

resOrderedAll <- na.omit(resOrderedAll)
resOrderedAllFiltered <- resOrderedAll[resOrderedAll$padj < 1e-10, ]

resOrderedAllFilteredNames <- resOrderedAllFiltered@rownames

resOrderedAllFilteredNames

 [1] "ENSG00000230456.10" "ENSG00000162624.16" "ENSG00000269983.1" 
 [4] "ENSG00000186960.15" "ENSG00000180574.4"  "ENSG00000259551.2" 
 [7] "ENSG00000176165.13" "ENSG00000204179.11" "ENSG00000261213.1" 
[10] "ENSG00000229807.14" "ENSG00000164900.5"  "ENSG00000224149.1" 
[13] "ENSG00000257126.7"  "ENSG00000293529.2"  "ENSG00000257748.3" 
[16] "ENSG00000170889.14" "ENSG00000224127.1"  "ENSG00000291220.2" 
[19] "ENSG00000225073.10" "ENSG00000232569.8"  "ENSG00000152192.8" 
[22] "ENSG00000104059.5"  "ENSG00000303412.1"  "ENSG00000257056.4" 
[25] "ENSG00000296808.1"  "ENSG00000099204.23" "ENSG00000277104.4" 
[28] "ENSG00000257522.8"  "ENSG00000183311.16" "ENSG00000233841.11"
[31] "ENSG00000154342.6"  "ENSG00000010932.17" "ENSG00000166407.14"
[34] "ENSG00000293141.2"  "ENSG00000223969.8"  "ENSG00000133115.12"
[37] "ENSG00000164330.18" "ENSG00000213722.10" "ENSG00000172238.6" 
[40] "ENSG00000226007.4"  "ENSG00000160321.16" "ENSG00000197134.13"
[43] "ENSG00000154065.17" "ENSG00000250138.5"  "ENSG00000227587.2" 
[46] "ENSG00000280811.2"  "ENSG00000157765.13" "ENSG00000198300.14"
[49] "ENSG00000110077.16" "ENSG00000235848.4"  "ENSG00000104899.8" 
[52] "ENSG00000280906.1"  "ENSG00000134245.18" "ENSG00000143869.7" 
[55] "ENSG00000203952.10" "ENSG00000170542.7"  "ENSG00000072182.14"
[58] "ENSG00000249621.2"  "ENSG00000263711.7"  "ENSG00000161328.11"
[61] "ENSG00000229344.1"  "ENSG00000275211.4"  "ENSG00000082556.14"
[64] "ENSG00000112511.18" "ENSG00000198400.14" "ENSG00000204711.9" 
[67] "ENSG00000147724.12"

Msc transcripts with adj p-val <1e-10

resOrderedMsc <- na.omit(resOrderedMsc)
resOrderedMscFiltered <- resOrderedMsc[resOrderedMsc$padj < 1e-10, ]

resOrderedMscFilteredNames <- resOrderedMscFiltered@rownames

resOrderedMscFilteredNames

  [1] "ENSG00000163661.4"  "ENSG00000163873.10" "ENSG00000114204.16"
  [4] "ENSG00000105880.7"  "ENSG00000135048.14" "ENSG00000136750.13"
  [7] "ENSG00000230456.10" "ENSG00000006377.11" "ENSG00000231764.12"
 [10] "ENSG00000155761.14" "ENSG00000029534.22" "ENSG00000249307.9" 
 [13] "ENSG00000113327.17" "ENSG00000124145.6"  "ENSG00000132975.8" 
 [16] "ENSG00000102466.17" "ENSG00000115844.11" "ENSG00000075702.19"
 [19] "ENSG00000157103.13" "ENSG00000158856.19" "ENSG00000188674.11"
 [22] "ENSG00000233237.10" "ENSG00000162630.6"  "ENSG00000213023.11"
 [25] "ENSG00000159784.18" "ENSG00000108684.15" "ENSG00000139998.16"
 [28] "ENSG00000155093.20" "ENSG00000172986.13" "ENSG00000147224.13"
 [31] "ENSG00000101057.16" "ENSG00000197872.11" "ENSG00000198300.14"
 [34] "ENSG00000105963.15" "ENSG00000154330.13" "ENSG00000101938.15"
 [37] "ENSG00000035403.18" "ENSG00000205363.7"  "ENSG00000160838.14"
 [40] "ENSG00000302022.1"  "ENSG00000058404.21" "ENSG00000081052.14"
 [43] "ENSG00000173210.21" "ENSG00000174776.12" "ENSG00000186471.13"
 [46] "ENSG00000151778.11" "ENSG00000169031.21" "ENSG00000167094.16"
 [49] "ENSG00000120278.17" "ENSG00000176165.13" "ENSG00000068615.20"
 [52] "ENSG00000171595.14" "ENSG00000063015.21" "ENSG00000105948.13"
 [55] "ENSG00000224223.1"  "ENSG00000170419.11" "ENSG00000158806.14"
 [58] "ENSG00000108176.15" "ENSG00000137571.11" "ENSG00000230062.7" 
 [61] "ENSG00000279041.1"  "ENSG00000160401.15" "ENSG00000103742.12"
 [64] "ENSG00000277639.3"  "ENSG00000090530.10" "ENSG00000165810.17"
 [67] "ENSG00000135144.8"  "ENSG00000137509.12" "ENSG00000099337.5" 
 [70] "ENSG00000128564.8"  "ENSG00000274320.2"  "ENSG00000164038.16"
 [73] "ENSG00000189159.16" "ENSG00000167900.12" "ENSG00000138061.13"
 [76] "ENSG00000257057.3"  "ENSG00000176788.9"  "ENSG00000104112.9" 
 [79] "ENSG00000140323.6"  "ENSG00000119326.15" "ENSG00000307001.1" 
 [82] "ENSG00000101958.14" "ENSG00000067840.13" "ENSG00000107485.18"
 [85] "ENSG00000109805.10" "ENSG00000291748.1"  "ENSG00000185736.16"
 [88] "ENSG00000133115.12" "ENSG00000101438.4"  "ENSG00000131951.12"
 [91] "ENSG00000137812.21" "ENSG00000130635.17" "ENSG00000147724.12"
 [94] "ENSG00000166897.16" "ENSG00000133138.20" "ENSG00000185201.18"
 [97] "ENSG00000122679.8"  "ENSG00000162624.16" "ENSG00000065609.15"
[100] "ENSG00000180574.4"  "ENSG00000155265.12" "ENSG00000110108.11"
[103] "ENSG00000110077.16" "ENSG00000157542.11" "ENSG00000205129.9" 
[106] "ENSG00000129159.9"  "ENSG00000197826.13" "ENSG00000008735.14"
[109] "ENSG00000124140.15" "ENSG00000100364.19" "ENSG00000224940.10"
[112] "ENSG00000058335.16" "ENSG00000163263.7"  "ENSG00000103160.12"
[115] "ENSG00000169255.15" "ENSG00000267890.1"  "ENSG00000269983.1" 
[118] "ENSG00000157110.16" "ENSG00000196990.10" "ENSG00000171303.8" 
[121] "ENSG00000184949.18" "ENSG00000144583.5"  "ENSG00000023902.14"
[124] "ENSG00000172260.15" "ENSG00000111206.13" "ENSG00000205835.9" 
[127] "ENSG00000185046.21" "ENSG00000087258.17" "ENSG00000115525.18"
[130] "ENSG00000165383.12" "ENSG00000100242.16" "ENSG00000181215.17"
[133] "ENSG00000176840.15" "ENSG00000168993.15" "ENSG00000101180.17"
[136] "ENSG00000228824.9"  "ENSG00000277104.4"  "ENSG00000114686.9" 
[139] "ENSG00000255690.3"  "ENSG00000250616.5"  "ENSG00000041353.10"
[142] "ENSG00000152661.9"  "ENSG00000165891.16" "ENSG00000145451.13"
[145] "ENSG00000153246.13" "ENSG00000204711.9"  "ENSG00000100678.20"
[148] "ENSG00000153347.10" "ENSG00000161647.19" "ENSG00000232569.8" 
[151] "ENSG00000189057.11" "ENSG00000108821.14" "ENSG00000100167.21"
[154] "ENSG00000103034.15" "ENSG00000135905.21" "ENSG00000261829.1" 
[157] "ENSG00000138111.14" "ENSG00000170324.21" "ENSG00000164087.8" 
[160] "ENSG00000080572.14" "ENSG00000067221.14" "ENSG00000171219.9" 
[163] "ENSG00000163075.13" "ENSG00000287910.2"  "ENSG00000152582.14"
[166] "ENSG00000055118.18"

SPb transcripts with adj p-val <1e-10

resOrderedSPb <- na.omit(resOrderedSPb)
resOrderedSPbFiltered <- resOrderedSPb[resOrderedSPb$padj < 1e-10, ]

resOrderedSPbFilteredNames <- resOrderedSPbFiltered@rownames

resOrderedSPbFilteredNames

 [1] "ENSG00000230456.10" "ENSG00000186960.15" "ENSG00000162624.16"
 [4] "ENSG00000177133.15" "ENSG00000165588.19" "ENSG00000259551.2" 
 [7] "ENSG00000257126.7"  "ENSG00000104059.5"  "ENSG00000176165.13"
[10] "ENSG00000269983.1"  "ENSG00000099204.23" "ENSG00000142611.17"
[13] "ENSG00000261213.1"  "ENSG00000164900.5"  "ENSG00000257748.3" 
[16] "ENSG00000204179.11" "ENSG00000176978.16" "ENSG00000198400.14"
[19] "ENSG00000170889.14" "ENSG00000224149.1"  "ENSG00000106236.4" 
[22] "ENSG00000180574.4"  "ENSG00000119138.5"  "ENSG00000168505.7" 
[25] "ENSG00000229807.14" "ENSG00000157765.13" "ENSG00000293529.2" 
[28] "ENSG00000224127.1"  "ENSG00000257056.4"  "ENSG00000010310.9" 
[31] "ENSG00000113721.14" "ENSG00000046653.15" "ENSG00000233841.11"
[34] "ENSG00000168389.18" "ENSG00000145934.17" "ENSG00000152192.8" 
[37] "ENSG00000092758.18" "ENSG00000063587.15" "ENSG00000140945.17"
[40] "ENSG00000236819.3"  "ENSG00000166407.14" "ENSG00000082556.14"
[43] "ENSG00000107338.10" "ENSG00000184347.16" "ENSG00000303412.1" 
[46] "ENSG00000267586.8"  "ENSG00000104899.8"  "ENSG00000165966.16"
[49] "ENSG00000164330.18" "ENSG00000254122.3"  "ENSG00000140287.11"
[52] "ENSG00000250786.5"  "ENSG00000257522.8"  "ENSG00000183098.12"
[55] "ENSG00000308894.1"  "ENSG00000287243.2"  "ENSG00000291220.2" 
[58] "ENSG00000154065.17" "ENSG00000296808.1"  "ENSG00000005302.19"
[61] "ENSG00000178033.6"  "ENSG00000003436.16" "ENSG00000248550.5" 
[64] "ENSG00000225073.10" "ENSG00000162641.20" "ENSG00000134222.16"
[67] "ENSG00000172458.5"  "ENSG00000235848.4"  "ENSG00000122971.9" 
[70] "ENSG00000170577.8"  "ENSG00000151276.24" "ENSG00000177728.17"
[73] "ENSG00000226007.4"  "ENSG00000198796.7"