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TIGS (Tumor Immunogenicity Score) project https://doi.org/10.7554/eLife.49020

Home Page: https://xsliulab.github.io/tumor-immunogenicity-score

License: Apache License 2.0

R 99.46% Shell 0.54%
analysis-report reproducible-analysis manuscript tumor-immunogenicity immunotherapy biomarker-discovery

tumor-immunogenicity-score's Issues

TODO

  • 整理一个 table S5 将 FIgure 5 用到的所有基因列表放进去
  • Figure 5 comb TMB 的单独做一个图
  • 生存图加粗

Error in 'row.names'

Hi, Can you please help me resolve this issue. Tried everything but nothing is working.
applyGSVA(merged_geneList, group_col = "Cell_type", gene_col = "Symbol", ExprMatList = list(RNASeq_pancan), method = "gsva") -> res_pancan.GSVA

Error in .rowNamesDF<-(x, value = value) :
duplicate 'row.names' are not allowed
In addition: Warning message:
non-unique values when setting 'row.names': ‘?|100130426’, ‘?|100133144’, ‘?|100134869’, ‘?|10357’, ‘?|10431’, ‘?|136542’, ‘?|155060’, ‘?|26823’, ‘?|280660’, ‘?|317712’, ‘?|340602’, ‘?|388795’, ‘?|390284’, ‘?|391343’, ‘?|391714’, ‘?|404770’, ‘?|441362’, ‘?|442388’, ‘?|553137’, ‘?|57714’, ‘?|645851’, ‘?|652919’, ‘?|653553’, ‘?|728045’, ‘?|728603’, ‘?|728788’, ‘?|729884’, ‘?|8225’, ‘?|90288’, ‘A1BG’, ‘A1CF’, ‘A2BP1’, ‘A2LD1’, ‘A2M’, ‘A2ML1’, ‘A4GALT’, ‘A4GNT’, ‘AAA1’, ‘AAAS’, ‘AACS’, ‘AACSL’, ‘AADAC’, ‘AADACL2’, ‘AADACL3’, ‘AADACL4’, ‘AADAT’, ‘AAGAB’, ‘AAK1’, ‘AAMP’, ‘AANAT’, ‘AARS’, ‘AARS2’, ‘AARSD1’, ‘AASDH’, ‘AASDHPPT’, ‘AASS’, ‘AATF’, ‘AATK’, ‘ABAT’, ‘ABCA1’, ‘ABCA10’, ‘ABCA11P’, ‘ABCA12’, ‘ABCA13’, ‘ABCA17P’, ‘ABCA2’, ‘AB [... truncated]

Called from: .rowNamesDF<-(x, value = value)

Browse[1]>

Also I removed duplicated row.names. still have the same problem.
merged_geneList[duplicated(merged_geneList$Symbol), ]

A tibble: 0 × 4

… with 4 variables: Cell_type , Symbol , Name , inRNAseq

GSE 36150 GPL not found 

cannot open URL 'https://ftp.ncbi.nlm.nih.gov/geo/platforms/GPL5nnn/GPL5175/annot/GPL5175.annot.gz': HTTP status was '404 Not Found'Annotation GPL not available, so will use submitter GPL instead
File stored at: 
../data/GEOdata/GPL5175.soft

Can I apply this method to just only one sample analysis?

Hi, I want to apply this method to analysis new sample RNA-seq data. It seems that it does not work for one sample, but when I change GSVA method to ''ssgsea", it runs.

I want to know if "ssgsea" for one sample's output meaningful?

Thanks!


> applyGSVA(merged_geneList_common, group_col = "Cell_type", gene_col = "Symbol", ExprMatList =list(tpm_mat_common), method = "gsva") -> res_GSVA
Error in .local(expr, gset.idx.list, ...) : 
  Less than two genes in the input expression data matrix
In addition: Warning messages:
1: In .local(expr, gset.idx.list, ...) :
  516 genes with constant expression values throuhgout the samples.
2: In .local(expr, gset.idx.list, ...) :
 
 Error in .local(expr, gset.idx.list, ...) : 
  Less than two genes in the input expression data matrix 
> applyGSVA(merged_geneList_common, group_col = "Cell_type", gene_col = "Symbol", ExprMatList =list(tpm_mat_common), method = "ssgsea") -> res_GSVA
Estimating ssGSEA scores for 26 gene sets.
  |                                                                                                                                                                                    |   0%Using parallel with 4 cores
  |====================================================================================================================================================================================| 100%
Warning message:
In .local(expr, gset.idx.list, ...) :
  516 genes with constant expression values throuhgout the samples.
> res_GSVA
[[1]]
           aDC Angiogenesis       APM     B cells CD8 T cells Cytotoxic cells          DC Eosinophils        iDC Macrophages Mast cells Neutrophils NK CD56bright cells NK CD56dim cells
TPM -0.1124977 -0.001057342 0.5979771 -0.04583635   0.2513935     -0.02563596 -0.03601384  0.07173081 0.04598423   0.1912207 -0.2183319  0.03906639          0.01845954      -0.06450661
     NK cells        pDC     T cells T helper cells Tcm cells Tem cells   Tfh cells  Tgd cells  Th1 cells Th17 cells    Th2 cells Treg cells
TPM 0.0761799 -0.3843792 -0.00658121      0.2967451 0.1318536 0.1282695 -0.05952038 -0.1437911 -0.0240773  0.0296016 -0.006756854 -0.4020229

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