degPatterns: Error in lower.to.upper.tri.inds(n) : 'n' must be >= 2
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3.0 years ago
salamandra ▴ 410

When running this:

clusters <- degPatterns(cluster_rlog, metadata = TableBJ, time = "condition", col=NULL)

I get this error:

Error in lower.to.upper.tri.inds(n) : 'n' must be >= 2

and also get a warning:

Large number of genes given. Please,make sure is not an error. NormallyOnly DE genes are useful for this function.

How to solve the error?

My data looks like this:

head(cluster_rlog)
                    BJ_1      BJ_2      BJ_3 d15_CD49f+_1 d15_CD49f+_2 d25_CD49f+_1 d25_CD49f+_2 d25_CD49f+_3
ENSG00000000003  8.416135  8.458166  8.366318     7.870605     7.977721     7.790931     7.857108     8.043796
ENSG00000000457  7.374256  7.478103  7.333263     7.574723     7.376089     7.797221     7.623953     7.572987
ENSG00000001084  8.051259  8.078769  8.149138     8.368629     8.486787     8.241352     8.401051     8.431956

                d25_CD49f+CD34+_1 d25_CD49f+CD34+_2 d25_CD49f+CD34+_3
ENSG00000000003          8.005091          8.215726          8.231341
ENSG00000000457          7.805268          7.804865          7.776632
ENSG00000001084          8.335795          8.529618          8.769456

TableBJ
          sampleName         fileName time        celltype           condition
1               BJ_1 1A_ATCACG_counts   d0              BJ               d0_BJ
2               BJ_2 2A_CAGATC_counts   d0              BJ               d0_BJ
3               BJ_3 3A_ATCACG_counts   d0              BJ               d0_BJ
4       d15_CD49f+_1 2B_ACTTGA_counts  d15 CD49fposCD34neg d15_CD49fposCD34neg
5       d15_CD49f+_2 3B_CGATGT_counts  d15 CD49fposCD34neg d15_CD49fposCD34neg
6       d25_CD49f+_1 1C_TTAGGC_counts  d25 CD49fposCD34neg d25_CD49fposCD34neg
7       d25_CD49f+_2 2C_GATCAG_counts  d25 CD49fposCD34neg d25_CD49fposCD34neg
8       d25_CD49f+_3 3C_TTAGGC_counts  d25 CD49fposCD34neg d25_CD49fposCD34neg
9  d25_CD49f+CD34+_1 1E_ACAGTG_counts  d25 CD49fposCD34pos d25_CD49fposCD34pos
10 d25_CD49f+CD34+_2 2E_GGCTAC_counts  d25 CD49fposCD34pos d25_CD49fposCD34pos
11 d25_CD49f+CD34+_3 3F_GCCAAT_counts  d25 CD49fposCD34pos d25_CD49fposCD34pos

Complete code follows:

library("DESeq2")
sampleNames <- c("BJ_1", "BJ_2", "BJ_3", "d15_CD49f+_1", "d15_CD49f+_2", "d25_CD49f+_1", "d25_CD49f+_2", "d25_CD49f+_3", "d25_CD49f+CD34+_1", "d25_CD49f+CD34+_2", "d25_CD49f+CD34+_3")

sampleFiles <- c("1A_ATCACG_counts", "2A_CAGATC_counts", "3A_ATCACG_counts", "2B_ACTTGA_counts", "3B_CGATGT_counts", "1C_TTAGGC_counts", "2C_GATCAG_counts", "3C_TTAGGC_counts", "1E_ACAGTG_counts", "2E_GGCTAC_counts", "3F_GCCAAT_counts")

time <- factor(c(rep('d0',3), rep('d15',2),rep('d25',6)))

celltype <- factor(c(rep('BJ',3),rep('CD49fposCD34neg',5),rep('CD49fposCD34pos',3)))

condition <- factor(paste0(time,'_',celltype))

TableBJ <- data.frame(sampleName = sampleNames, fileName = sampleFiles, time = time, celltype = celltype, condition=condition)

ddsBJ <- DESeqDataSetFromHTSeqCount(sampleTable = TableBJ, design= ~ condition)
ddsHTSeqBJ <- ddsBJ[rowSums(counts(ddsBJ)) > 1, ]
rld <- rlog(ddsHTSeqBJ, blind=TRUE)
dds_lrt <- DESeq(ddsHTSeqBJ, test="LRT", reduced = ~ 1)
dds_res <- results(dds_lrt, alpha = 0.05)
ddsdatres <- as.data.frame(dds_res)
ddsdatres <- ddsdatre[!is.na(ddsdatre$padj),] 
expgenes <- ddsdatres
res.sig <- ddsdatres[ddsdatres$padj < 0.05,] # select just genes < 0.05
res.sig <- res.sig[order(res.sig$padj),] # and then sort this table by p-value (smaller p-values on top):
rld_mat <- assay(rld)
cluster_rlog <-subset(rld_mat, row.names(rld_mat)%in%row.names(res.sig))

library('DEGreport')
rownames(TableBJ) <- TableBJ[,1]
clusters <- degPatterns(cluster_rlog, metadata = TableBJ, time = "condition", col=NULL)
RNA-seq clustering • 1.2k views
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