GSEA Report for Dataset preliminary

Enrichment in phenotype: NGP (3 samples)

• 71 / 170 gene sets are upregulated in phenotype NGP
• 1 gene sets are significant at FDR < 25%
• 27 gene sets are significantly enriched at nominal pvalue < 1%
• 27 gene sets are significantly enriched at nominal pvalue < 5%
• Snapshot of enrichment results
• Detailed enrichment results in html format
• Detailed enrichment results in excel format (tab delimited text)
• Guide to interpret results

Enrichment in phenotype: HG (3 samples)

• 99 / 170 gene sets are upregulated in phenotype HG
• 42 gene sets are significantly enriched at FDR < 25%
• 51 gene sets are significantly enriched at nominal pvalue < 1%
• 51 gene sets are significantly enriched at nominal pvalue < 5%
• Snapshot of enrichment results
• Detailed enrichment results in html format
• Detailed enrichment results in excel format (tab delimited text)
• Guide to interpret results

Dataset details

• The dataset has 15526 features (genes)
• No probe set => gene symbol collapsing was requested, so all 15526 features were used

Gene set details

• Gene set size filters (min=15, max=500) resulted in filtering out 16 / 186 gene sets
• The remaining 170 gene sets were used in the analysis
• List of gene sets used and their sizes (restricted to features in the specified dataset)

Gene markers for the NGP versus HG comparison

• The dataset has 15526 features (genes)
• # of markers for phenotype NGP: 7743 (49.9% ) with correlation area 49.8%
• # of markers for phenotype HG: 7783 (50.1% ) with correlation area 50.2%
• Detailed rank ordered gene list for all features in the dataset
• Heat map and gene list correlation profile for all features in the dataset
• Butterfly plot of significant genes

Global statistics and plots

• Plot of p-values vs. NES
• Global ES histogram

Other

• Parameters used for this analysis

Comments

• There were duplicate row identifiers in the specified dataset. One id was arbitarilly choosen. Details are below Generally, this is OK, but if you want to avoid this automagic, edit your dataset so that all row ids are unique # of row ids in original dataset: 16035 # of row UNIQUE ids in original dataset: 15526 # The duplicates were C1orf43 ABCB1 ACAA1 ACAD10 ACAT2 ACOT9 AGTR1 AKR1B10 AKR1B10 ALDH1A1 ALYREF ALYREF AMD1 ANAPC15 ANG ANG APH1B SPCS3 ATG4A BCL2A1 BGLAP BGLAP C1R C1S CAPZA1 CATSPERG CCL8 CD1D CDC34 CES1 CES1 CES1 CES1 CES2 CES2 CES2 CHIA CLCA4 CLDN34 CLEC2D CLEC2D CLEC2D CLEC2D CLEC4C CSF2RB CS CSTA CST9L CYP4F8 CYP4F8 CYP4F8 CYP4F2 DCAF12L2 DDX3X DEGS1 DNAH7 DNAH7 DNMT3B DYNLL1 DYNLT1 DYNLT1 DYNLT1 ECI2 EIF1AX EIF1AX EIF3J ENO1 ERMARD FBXW12 FBXW12 FBXW12 FBXW12 FBXW12 FBXW12 FBXW12 FTL G6PD GAL3ST2 GAL3ST2 GBP6 GBP2 GBP6 GBP6 GBP3 GBP6 GBP6 CYCS GAPDH AC113348.1 COX5B AC113348.1 HAVCR1 CDK2AP1 CCL27 CWC22 ERVW-1 GAPDH SYCP3 EIF1AX EIF1AX EIF1AX DGCR2 EIF1AX CLUAP1 EIF1AX POTEA EIF1AX EIF1AX COX17 AC007375.1 AC005774.1 AC005774.1 AC007375.1 AC023389.1 AC009121.2 RNU6-4P AC009121.2 RNU6-3P AC023389.1 SNORA38 RNU6-4P AL391095.1 AC009121.2 AC005774.1 AC007375.1 RNU6-4P RNU6-154P AC009121.2 RNU6-3P AC007375.1 AC009121.2 SNORD121A SNORA24B RNU6-4P AC009121.2 AC009121.2 AC115085.1 AC007375.1 AC009121.2 RNU6-906P SNORA65 RNU6-4P RNU6-4P AC023389.1 RNU6-4P SNORA65 ANTXRL ERMARD ARFIP1 GAPDH EIF1AX CCDC142 AC113348.1 ALYREF AP4B1 ALYREF KIAA0753 ATN1 CYP4V2 BET1L AHCY DHRS7 C3orf14 TES B4GAT1 EIF1AX BHMT SYCP3 EIF1AX ALYREF EIF1AX EIF1AX ZNF34 SYCP3 EIF1AX EIF1AX RNF26 ADRM1 GML GON7 GPC2 GSDMC GSDMC GSTM1 GSTM1 GSTM1 GSTP1 GTF3C2 GZMB GZMH GZMH H2AL1RP H2AL1RP HAVCR1 HBQ1 C5 HMGA1 HMGN2 HNRNPA3 IDI2 IFFO2 MNDA MNDA IFI27L2 IFIT1B IFIT1B IFIT3 IRGM IRGM ISCA1 ISOC2 KATNAL1 KIFC1 KLRB1 KLRB1 KLRB1 KNG1 KPNA2 KRT33A KRT33A KRTAP4-3 LARP7 LGALSL LRCH4 LRRC8A MAGEA10 MAPK1IP1L MAPKAPK5 MED20 METTL7A METTL7A MFAP1 MFSD4B MFSD4B CLEC10A MMP1 MNDA MORC2 MPLKIP MTERF1 MX1 MYEF2 NDUFB11 NLRP1 NLRP4 NLRP4 NLRP4 NLRP4 NLRP9 NSMCE3 NUDT11 NUP50 NUTF2 OAS1 OAS1 OAS1 OAS1 OR5W2 OR4C15 OR4X2 OR4B1 OR2Y1 OR2Y1 OR2Y1 OR2Y1 OR2Y1 OR5B3 OR5B3 OR5B3 OR5P2 OR7G2 OR7G2 OR8B12 OR2H1 OR2H1 OR2H1 OXCT2 PAGR1 PAM16 PBLD PCDHB5 PCDHB5 PCDHB5 PCDHB5 PCDHB4 PCDHB5 PCDHB4 PDE2A FRAT1 PFKFB2 MPEG1 PHF11 PHF11 PHF11 PICK1 PIN1 PLP2 PLSCR1 PLSCR1 PNP POT1 PPHLN1 PPIH PRDX6 PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRL PRND PRPS1 PSME2 RBM12B RBM4 RBM8A RBMXL1 RDH16 RDH16 RDH16 REG1A RHBG RNASEK RNF113A RNF26 RNF7 RNU6-4P RPL10 RPL29 RPL35 ZNF429 ZNF429 RSPH3 RTL8C RTL8C RUSC1 SAP18 SCD SEC61B SECTM1 SELENBP1 SERPINA1 SERPINA1 SERPINA3 SERPINA3 SERPINA3 SERPINA3 SERPINA3 SERPINA3 SERPINA3 SERPINA3 SERPINB1 SERPINB1 SERPINB6 SERPINB6 SERPINB6 SERPINB6 SERPINB9 SERPINB9 SERPINB9 SERPINB9 SERPINB9 SERPINB9 SERPINB9 SFT2D1 SH2D1B SIAH1 CD33 SLC12A6 SLC22A13 SLC22A5 SLC6A20 SLCO1A2 SLCO1A2 SLCO6A1 SLFN11 SYCP3 SMIM17 SMIM40 SNORA20 SNORD58B SNORD93 SPINK9 SPOP SRP54 SRP54 STYX SYCP3 TAF9 TAGAP TCTE3 SPOP SPOP SPOP SPOP TES DCUN1D1 TEX19 HAVCR1 TIMM8A TMEM45A TP53RK UCHL3 UGT1A6 UGT1A6 UNC93A VN1R5 VN1R5 VN1R4 VN1R5 VN1R5 VN1R5 VN1R5 VN1R5 VN1R5 VN1R5 VWA5A SYCP3 ZBTB9 ZNF233 ZNF233 ZNF233 ZNF429 ZNF274 ZNF775 ZNF429 ZNF429 ZNF429 ZNF845 AC008758.6 ZNF429 AC008758.6 ZNF429 ZNF780A ZNF780A ZNF780A AC008758.6 ZNF780A ZNF429 AC008758.6 ZNF845 ZNF775 ZNF845 ZNF845 ZNF845 ZNF845 ZNF34 AC008758.6 AC008758.6 AC008758.6 ZNF34 ZNF429 ZNF780A ZNF101 ZNF101 ZNF101 ZNF429 AC008758.6 AC008758.6 ZNF429 ZNF429 AC008758.6 ZNF233 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 AC008758.6 AC008758.6 AC008758.6 TCF20 ZNF101 ZNF101 ZNF101 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZNF34 ZKSCAN4 ZNRD2 RNA5SP443
• Timestamp used as random seed: 1591809283127
• Warning: Phenotype permutation was performed but the number of samples in class A is < 7, phenotype: phenotype.cls#NGP_versus_HG_repos
• Warning: Phenotype permutation was performed but the number of samples in class B is < 7, phenotype: phenotype.cls#NGP_versus_HG_repos
• With small datasets, there might not be enough random permutations of sample labels to generate a sufficient null distribution. In such cases, gene_set randomization might be a better choice.