show velocity Error....'from' must be a finite number

[Jemkon]

Hello,

I have got the following error while running show.velocity.on.embedding.cor function.

fit.quantile <- 0.02
rvel.cd <-gene.relative.velocity.estimates(emat,nmat,deltaT=1,kCells=25,cell.dist=cell.dist,fit.quantile=fit.quantile)

calculating cell knn ... done 
calculating convolved matrices ... done
fitting gamma coefficients ... done. succesfful fit for 483 genes
filtered out 139 out of 483 genes due to low nmat-emat correlation
filtered out 60 out of 344 genes due to low nmat-emat slope
calculating RNA velocity shift ... done
calculating extrapolated cell state ... done

show.velocity.on.embedding.cor(emb,rvel.cd,n=200,scale='sqrt',cell.colors=ac(cell.colors,alpha=0.5),cex=0.8,arrow.scale=3,show.grid.flow=TRUE,min.grid.cell.mass=0.5,grid.n=40,arrow.lwd=1,do.par=F,cell.border.alpha = 0.1)

delta projections ... sqrt knn ... transition probs ... done
calculating arrows ... grid estimates ... Error in seq.default(rx[1], rx[2], length.out = grid.n) : 
  'from' must be a finite number

Any idea what's wrong here and how to correct it?

Many thanks

[topherconley]

I am getting the same error. I dug into the code and the internalcc variable calculated is all 'not a number', i.e. NaN. Check out the output from my debugger

Browse[2]> str(cc) num [1:6004, 1:6004] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... Browse[2]> all(is.nan(cc)) [1] TRUE

Note the cc variable is calculated by colDeltaCor(em, (sqrt(abs(nd)) * sign(nd)), nthreads = n.cores). The transition probability tp variable is a function of cc so it too becomes a NaN. These errors eventually cause the seq(from, to, length.out) command to fail because the range variables rx are NaN.

I am stuck too. Help please.

[rosshandler]

Hi guys,

I had the same error and then realise cell names in the embedding were not matching those in the velocity object. In this case, cc is NaN because em and emb are empty. That was it.

Best, Ivan

[pkharchenko]

Thanks for the hint, Ivan!

If others keep encountering this error, please share an example dataset to help us replicate it.

Thank you, -peter.

On May 23, 2018, at 15:23, rosshandler notifications@github.com wrote:

Hi guys,

I had the same error and then realise cell names in the embedding were not matching those in the velocity object. In this case, cc is NaN because em and emb are empty. That was it.

Best, Ivan

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.

[topherconley]

Ivan, thank you for your reporting; however, this is not the same problem I have. Further investigation shows the deltaE object (unscaled transcriptional change ) returned from gene.relative.velocity.estimates is virtually all zeros (values are about 2e-15 at largest). This result is used as nd in the colDeltaCor(em, (sqrt(abs(nd)) * sign(nd)), nthreads = n.cores) call. The NaN values are a result of nd having a standard deviation of zero for most of its columns when evaluated for its correlation to em.

[Fhennig94]

Hello all,

I am getting the same error :

`

emat <- filter.genes.by.cluster.expression(emat,cluster.label,min.max.cluster.average = 0.2) nmat <- filter.genes.by.cluster.expression(nmat,cluster.label,min.max.cluster.average = 0.05) length(intersect(rownames(emat),rownames(nmat))) [1] 1357 fit.quantile <- 0.02 rvel.cd <- gene.relative.velocity.estimates(emat,nmat,deltaT=1,kCells=25,fit.quantile=fit.quantile) calculating cell knn ... done calculating convolved matrices ... done fitting gamma coefficients ... done. succesfful fit for 1357 genes filtered out 61 out of 1357 genes due to low nmat-emat correlation filtered out 79 out of 1296 genes due to low nmat-emat slope calculating RNA velocity shift ... done calculating extrapolated cell state ... done show.velocity.on.embedding.cor(emb,rvel.cd,n=200,scale='sqrt',cex=0.8,arrow.scale=3,show.grid.flow=TRUE,min.grid.cell.mass=0.5,grid.n=40,arrow.lwd=1,do.par=F,cell.border.alpha = 0.1) delta projections ... sqrt knn ... transition probs ... done calculating arrows ... done grid estimates ... Error in seq.default(rx[1], rx[2], length.out = grid.n) : 'from' must be a finite number`

Any help is greatly appreciated. Let me know if you need me to share my data and what would be the best way to do so.

Thanks, Fiona

[kiter2520]

Same problem here.

show.velocity.on.embedding.cor(fourh.emb,fourh.rvel.cd,n=300,scale='sqrt',cell.colors=ac(fourh.cell.colors,alpha=0.5),cex=0.8,arrow.scale=5,show.grid.flow=TRUE,min.grid.cell.mass=0.5,grid.n=40,arrow.lwd=1,do.par=F,cell.border.alpha = 0.1) delta projections ... sqrt knn ... transition probs ... done calculating arrows ... done grid estimates ... Error in seq.default(rx[1], rx[2], length.out = grid.n) : 'from' must be a finite number

Let me know how best to share my data so you can troubleshoot.

[somnathtagore]

Hi,

we can do this: emat <- emat[,colSums(is.na(emat))<nrow(emat)]

to remove all NAs...

The script runs smoothly then.

[chent5]

I have successfully run velocyto on one dataset. When I switch to another dataset, I get the same error...

Let me know if any other information you need me to share. Thank you so much!

[FrancesWong]

Hi, I'm also getting the same error. I've run velocyto successfully on another dataset but I keep getting an error with this dataset. I split it in half and one subset works where as the other subset has the same error. This dataset has 30 000 cells.

Error: delta projections ... sqrt knn ... transition probs ... done calculating arrows ... done grid estimates ... Error in seq.default(rx[1], rx[2], length.out = grid.n) : 'from' must be a finite number Calls: show.velocity.on.embedding.cor -> seq -> seq.default

I tried the NA remove but it's not resolving the problem:

emat2 <- emat[,colSums(is.na(emat))<nrow(emat)] emat3 <- emat[,colSums(is.infinite(emat))<nrow(emat)] emat4 <- emat[,colSums(is.finite(emat))<nrow(emat)]

dim(emat) [1] 723 29943 dim(emat2) [1] 723 29943 dim(emat3) [1] 723 29943 dim(emat4) [1] 723 0

All numbers are finite, there are no Na, NaN, or infinites to remove. I also double checked the nmat object doesn't have na errors.

Thanks you so much in advanced, please let me know if any other information can help trouble shoot.

Best, -Frances

[takahirosuzuki1980]

I got the same error. But once I simply changed the average parameter of filter.genes.by.cluster.expression, It worked. though I do not know why...

Takahiro

[flying-sheep]

Digging in: The error occurs because cc <- colDeltaCorLog10(...) returns a matrix with a values that are NA, which causes Matrix::colSums(tp) to be all-NaN as well, ultimately causing embArrows(coords, tp, ...) to be all-NA.

The relevant lines:

https://github.com/velocyto-team/velocyto.R/blob/d7790346cb99f49ab9c2b23ba70dcf9d2c9fc350/R/momentum_routines.R#L1077

https://github.com/velocyto-team/velocyto.R/blob/d7790346cb99f49ab9c2b23ba70dcf9d2c9fc350/R/momentum_routines.R#L1100-L1102

The NA values occur for me because in vel$current, a cell had all-NaN values: vel$current[, 'SCG71:AAGTTGTCACCTTGCC'] == c(NaN, NaN, ...)

[flying-sheep]

OK, there’s more problems. If I eliminate this cell, I still get NAs in the result of colDeltaCorLog10 and friends, which propagate to tp and then to the result of arsd <- embArrows(...).

I can’t say I really understand colDeltaCor*, and if there’s a bug…

https://github.com/velocyto-team/velocyto.R/blob/d7790346cb99f49ab9c2b23ba70dcf9d2c9fc350/src/routines.cpp#L99-L109

[pkharchenko]

colDeltaCor functions calculate for each cell (column E_i of a matrix E) correlation between E-E_i and D_i (difference vector expected from velocity of the cell I). colDeltaSqrt and Log10 variants calculate correlation on the sqrt or log10 transformed values of E-E_i. This is done to figure out towards what cells in the neighborhood of a cell i should the velocity D_i be projected.

I am not sure what’s causing the issue here ... perhaps you have some duplicated cells. Is it just the diagonal values of the return matrix that are NaN? Can you share an example?

On Jul 17, 2019, at 06:07, Philipp A. notifications@github.com wrote:

OK, there’s more problems. If I eliminate this cell, I still get NAs in the result of colDeltaCorLog10, which propagate to tp and then to

I can’t say I really understand colDeltaCorLog10, and if there’s a bug…

https://github.com/velocyto-team/velocyto.R/blob/d7790346cb99f49ab9c2b23ba70dcf9d2c9fc350/src/routines.cpp#L99-L109

— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

[flying-sheep]

Not just the diagonals. The cells where those NAs occur are mostly all-zero in E, and the few that aren’t are equal:

na_idx <- as.data.frame(which(is.na(cc), arr.ind = TRUE))
cbind(
    apply(as.matrix(vel$current)[, na_idx$row], 2, sum),
    apply(as.matrix(vel$current)[, na_idx$col], 2, sum)
)

Output

``` ANames A BNames B 1 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 2 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 3 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 4 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 5 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 6 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 7 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 8 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 9 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 10 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 11 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 12 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 13 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 14 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 15 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:AAACTGTGAGCAGAAC 0.00000 16 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 17 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 18 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 19 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 20 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 21 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 22 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 23 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 24 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 25 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 26 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 27 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 28 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 29 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 30 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:AACTGCCTTCGAAGCT 0.00000 31 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 32 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 33 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 34 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 35 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 36 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 37 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 38 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 39 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 40 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 41 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 42 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 43 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 44 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 45 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:AAGTTGTCTCCTTATT 0.00000 46 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 47 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 48 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 49 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 50 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 51 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 52 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 53 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 54 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 55 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 56 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 57 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 58 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 59 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 60 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:ACAAGTAGATGGGCAC 0.00000 61 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 62 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 63 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 64 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 65 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 66 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 67 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 68 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 69 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 70 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 71 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 72 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 73 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 74 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 75 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:AGAATCAATGCCATCG 0.00000 76 SCG71:TACCTAAGCTCCGCAT 58.82353 SCG71:AGGTAAGCTTGCACGC 58.82353 77 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 78 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 79 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 80 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 81 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 82 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 83 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 84 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 85 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 86 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 87 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 88 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 89 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 90 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 91 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:ATAGATGTCTTCTGGA 0.00000 92 SCG71:CAGACTCGAGCGTAGG 125.00000 SCG71:ATTGGCCCAAACAGGG 125.00000 93 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 94 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 95 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 96 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 97 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 98 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 99 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 100 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 101 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 102 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 103 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 104 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 105 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 106 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 107 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:CAAGGGTTATGCATGG 0.00000 108 SCG71:ATTGGCCCAAACAGGG 125.00000 SCG71:CAGACTCGAGCGTAGG 125.00000 109 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 110 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 111 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 112 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 113 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 114 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 115 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 116 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 117 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 118 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 119 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 120 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 121 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 122 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 123 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:CACTAACCTACGTGCT 0.00000 124 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 125 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 126 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 127 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 128 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 129 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 130 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 131 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 132 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 133 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 134 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 135 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 136 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 137 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 138 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:GAGGAGTGTGGGATTC 0.00000 139 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 140 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 141 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 142 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 143 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 144 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 145 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 146 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 147 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 148 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 149 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 150 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 151 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 152 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 153 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:GATTTCCCTAACCCGT 0.00000 154 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 155 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 156 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 157 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 158 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 159 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 160 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 161 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 162 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 163 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 164 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 165 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 166 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 167 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 168 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:GCGTGCAACTTCAGGT 0.00000 169 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 170 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 171 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 172 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 173 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 174 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 175 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 176 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 177 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 178 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 179 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 180 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 181 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 182 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 183 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:GTGCGAAGACGCCATT 0.00000 184 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 185 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 186 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 187 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 188 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 189 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 190 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 191 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 192 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 193 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 194 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 195 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 196 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 197 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 198 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:GTTACTAGTGTGTCCT 0.00000 199 SCG71:AGGTAAGCTTGCACGC 58.82353 SCG71:TACCTAAGCTCCGCAT 58.82353 200 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 201 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 202 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 203 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 204 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 205 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 206 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 207 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 208 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 209 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 210 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 211 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 212 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 213 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 214 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:TCATTTCACTCGCGTA 0.00000 215 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 216 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 217 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 218 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 219 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 220 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 221 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 222 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 223 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 224 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 225 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 226 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 227 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 228 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 229 SCG71:TGGTTAACCAGGTGTC 0.00000 SCG71:TCGACTGCGCTTTCCA 0.00000 230 SCG71:AAACTGTGAGCAGAAC 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 231 SCG71:AACTGCCTTCGAAGCT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 232 SCG71:AAGTTGTCTCCTTATT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 233 SCG71:ACAAGTAGATGGGCAC 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 234 SCG71:AGAATCAATGCCATCG 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 235 SCG71:ATAGATGTCTTCTGGA 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 236 SCG71:CAAGGGTTATGCATGG 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 237 SCG71:CACTAACCTACGTGCT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 238 SCG71:GAGGAGTGTGGGATTC 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 239 SCG71:GATTTCCCTAACCCGT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 240 SCG71:GCGTGCAACTTCAGGT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 241 SCG71:GTGCGAAGACGCCATT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 242 SCG71:GTTACTAGTGTGTCCT 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 243 SCG71:TCATTTCACTCGCGTA 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 244 SCG71:TCGACTGCGCTTTCCA 0.00000 SCG71:TGGTTAACCAGGTGTC 0.00000 ```

[pkharchenko]

Yes, the if there are two identical cells (i.e. two all-0 E columns), that would lead to NAs … most likely those entries in the output matrix can be simply replaced with 0s, but let me look at it.

On Jul 17, 2019, at 06:53, Philipp A. notifications@github.com wrote:

Not just the diagonals. The cells where those NAs occur are mostly all-zero in E:

na_idx <- as.data.frame(which(is.na(cc), arr.ind = TRUE )) cbind( apply(as.matrix( vel$current)[, na_idx$row], 2, sum ), apply(as.matrix( vel$current)[, na_idx$col], 2, sum ) )

Output — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

[flying-sheep]

In the code, em is gene.relative.velocity.estimates(...)$current, the “current normalized expression state”.

I’m using your example data from http://pklab.med.harvard.edu/velocyto/mouseBM/SCG71.loom In the raw data, there’s no cells with identical expression profile.

Therefore the cells are only identical after gene.relative.velocity.estimates did its thing. How does that happen? Did I miss a preprocessing step?

In any case, the function should warn if it’s returning junk that can’t be further processed. I spent far too much time debugging this when gene.relative.velocity.estimates could simply raise a warning telling me about $current containing all-NA cells and duplicate cells now, and that I should use $valid_cells to filter emat, nmat, and cell.dist and try again.

If I hackily remove duplicated and NA cells in a loop, it will work. It looks weird though:

Original	Mine

[RobertKSuter]

I am having the same issue.

However, if I pre-filter out cells more stringently based on nFeatures and nUMI the problem has gone away. Thanks!

[whelena]

I have the same issue here. I've just started using velocyto.R with the seurat wrapper, not sure how to trouble shoot as it is all automated

[flying-sheep]

I gave up and switched to scvelo. I don’t want to advertise as it’s from my lab and I’m biased, so I’m only saying that it worked immediately, and with more reasonable results (Probably because I forgot lib size normalization when trying velocyto). If someone wants to try my hack to eliminate problematic cells, here it is:

velocity_estimates_hack <- function(spliced, unspliced, avg_spliced, avg_unspliced, dists, ...)
  emat <- filter.genes.by.cluster.expression(spliced,   cluster_labels, min.max.cluster.average = avg_spliced)
  nmat <- filter.genes.by.cluster.expression(unspliced, cluster_labels, min.max.cluster.average = avg_unspliced)
  dists <- as(dists, 'sparseMatrix')

  while (TRUE) {
    vel <- gene.relative.velocity.estimates(emat, nmat, cell.dist = as.dist(dists), ...)

    idx_avail <- apply(vel$current, 2, function(cell) all(!is.na(cell)))
    idx_uniqu <- !duplicated(as.matrix(vel$current), MARGIN = 2)
    keep_cells <- idx_avail & idx_uniqu
    if (all(keep_cells)) break
    if (!all(idx_avail)) message('Eliminating NA cells: ',        paste(colnames(emat)[!idx_avail], collapse = ', '))
    if (!all(idx_uniqu)) message('Eliminating duplicate cells: ', paste(colnames(emat)[!idx_uniqu], collapse = ', '))
    emat <- emat[, keep_cells, drop = FALSE]
    nmat <- nmat[, keep_cells, drop = FALSE]
    dists <- dists[keep_cells, keep_cells]
  }
  list(emat = emat, nmat = nmat, dists = dists, vel = vel)
}

[pkharchenko]

Thank you, Philipp for the suggested patch.

Unfortunately, I can’t seem to replicate the error. As suggested, I’ve ran the SCG71 tutorial example (http://pklab.med.harvard.edu/velocyto/notebooks/R/SCG71.nb.html) on several R versions that I had locally (R3.6.1, R3.5.3 and R3.5.1) and in each case it worked fine.

If someone could point me at an example that may be easier to reproduce, I would appreciate it!

Thanks!

On Jul 19, 2019, at 03:25, Philipp A. notifications@github.com wrote:

I gave up and switched to scvelo. I don’t want to advertise as it’s from my lab and I’m biased, so I’m only saying that it worked immediately, and with more reasonable results. If someone wants to try my hack to eliminate problematic cells, here it is:

velocity_estimates_hack <- function(spliced, unspliced, avg_spliced, avg_unspliced, dists, ... )

emat <- filter.genes.by.cluster.expression(spliced, cluster_labels, min.max.cluster.average = avg_spliced )

nmat <- filter.genes.by.cluster.expression(unspliced, cluster_labels, min.max.cluster.average = avg_unspliced )

dists <- as(dists, 'sparseMatrix' )

while (TRUE ) {

vel <- gene.relative.velocity.estimates(emat, nmat, cell.dist = as.dist(dists), ... )

idx_avail <- apply(vel$current, 2, function(cell) all(!is.na(cell )))

idx_uniqu <- !duplicated(as.matrix(vel$current), MARGIN = 2 )

keep_cells <- idx_avail & idx_uniqu

if (all(keep_cells)) break

if (!all(idx_avail)) message('Eliminating NA cells: ', paste(colnames(emat)[!idx_avail], collapse = ', ' ))

if (!all(idx_uniqu)) message('Eliminating duplicate cells: ', paste(colnames(emat)[!idx_uniqu], collapse = ', ' ))

emat <- emat[, keep_cells, drop = FALSE ]

nmat <- nmat[, keep_cells, drop = FALSE ]

dists <- dists[keep_cells, keep_cells ] }

list(emat = emat, nmat = nmat, dists = dists, vel = vel ) }

— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

[mbassalbioinformatics]

Having the same issue and would appreciate any help.
@pkharchenko, happy to share the data confidentially to help you track down what the issue could be. Im trying to get velocyto to work on an indrop dataset.

[pkharchenko]

Can you please paste the error traceback() and sessionInfo(). Thank you, -peter.

On Jul 22, 2019, at 17:33, mbassalbioinformatics notifications@github.com wrote:

Having the same issue and would appreciate any help. @pkharchenko, happy to share the data confidentially to help you track down what the issue could be. Im trying to get velocyto to work on an indrop dataset.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or mute the thread.

[mbassalbioinformatics]

traceback()

4: stop("'from' must be a finite number")
3: seq.default(rx[1], rx[2], length.out = grid.n)
2: seq(rx[1], rx[2], length.out = grid.n)
1: show.velocity.on.embedding.cor(emb_matrix, rvel.cd, n = 200, 
       scale = "sqrt", cell.colors = ac(cell.colors, alpha = 0.5), 
       cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, 
       grid.n = 40, arrow.lwd = 1, do.par = F, cell.border.alpha = 0.1, 
       n.cores = 35)

sessioninfo()

Rversion 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] SparseData_1.0.0    Biobase_2.44.0      BiocGenerics_0.30.0 forcats_0.4.0       stringr_1.4.0       dplyr_0.8.3         purrr_0.3.2        
 [8] readr_1.3.1         tidyr_0.8.3         tibble_2.1.3        ggplot2_3.2.0       tidyverse_1.2.1     pagoda2_0.1.0       igraph_1.2.4.1     
[15] velocyto.R_0.6      Matrix_1.2-17      

loaded via a namespace (and not attached):
 [1] httr_1.4.0        bit64_0.9-7       jsonlite_1.6      splines_3.6.1     hdf5r_1.2.0       modelr_0.1.4      assertthat_0.2.1  triebeard_0.3.0  
 [9] urltools_1.7.3    cellranger_1.1.0  remotes_2.1.0     pillar_1.4.2      backports_1.1.4   lattice_0.20-38   glue_1.3.1        rvest_0.3.4      
[17] colorspace_1.4-1  pkgconfig_2.0.2   broom_0.5.2       haven_2.1.1       scales_1.0.0      processx_3.4.1    brew_1.0-6        mgcv_1.8-28      
[25] generics_0.0.2    withr_2.1.2       lazyeval_0.2.2    cli_1.1.0         magrittr_1.5      crayon_1.3.4      readxl_1.3.1      ps_1.3.0         
[33] fansi_0.4.0       nlme_3.1-140      MASS_7.3-51.4     xml2_1.2.0        pkgbuild_1.0.3    Rook_1.1-1        tools_3.6.1       prettyunits_1.0.2
[41] hms_0.5.0         munsell_0.5.0     cluster_2.1.0     irlba_2.3.3       callr_3.3.1       pcaMethods_1.76.0 compiler_3.6.1    rlang_0.4.0      
[49] grid_3.6.1        rstudioapi_0.10   rjson_0.2.20      base64enc_0.1-3   gtable_0.3.0      curl_3.3          R6_2.4.0          lubridate_1.7.4  
[57] bit_1.1-14        zeallot_0.1.0     utf8_1.1.4        rprojroot_1.3-2   stringi_1.4.3     Rcpp_1.0.1        vctrs_0.2.0       tidyselect_0.2.5 

[mbassalbioinformatics]

@pkharchenko, any updates regarding this?

[cemalley]

I would also appreciate any updates about this error. Using the Seurat wrapper for velocyto.R. @pkharchenko

[cstrlln]

getting same error, any news?

[ashokpatowary]

@pkharchenko

I am also having same error. Any suggestions ?

emat <- emat[,colSums(is.na(emat))<nrow(emat)] is not solving the issue

[GrigoriiNos]

Keep having the same error, even though there is no NaNs or NAs, can it be related to 5'chemistry?

[Clara22]

Hi, I was having the same error when running 'show.velocity.on.embedding.cor'

delta projections ... sqrt knn ... transition probs ... done
calculating arrows ... done
grid estimates ... Error in seq.default(rx[1], rx[2], length.out = grid.n) : 
  'from' must be a finite number

From the different comments above I thought about eliminating any duplicated columns from emat and nmat. I did the following:

emat<-emat[,-which(duplicated(as.matrix(emat), MARGIN = 2))]
nmat<-nmat[,-which(duplicated(as.matrix(nmat), MARGIN = 2))]

For the newbies like me,

1. With 'emat[,...]' I am subsetting emat.
2. I don't specify anything before the comma in 'emat[,...]' to keep all rows.
3. I use '-' to EXCLUDE those columns defined after it.
4. 'which(duplicated(as.matrix(emat), MARGIN = 2))' should give the column numbers of those columns duplicated, those I want to exclude. I needed 'as.matrix()' because emat is not a standard matrix. 'MARGIN=2' tells 'duplicated()' to check the columns of 'as.matrix(emat)' (for a matrix 1 indicates rows, 2 indicates columns).

Before running I would recommend to check what

which(duplicated(as.matrix(emat), MARGIN = 2))

gives back for emat and nmat. In my case it was a dozen columns=cells and it was exactly the same columns for both emat and nmat.

I hope this can help some of you but use with caution since I am not an expert and I have not fully checked what these columns are.

[polojacky]

Just the same issue. I just modified the code of function show.velocity.on.embedding.cor to remove NA of ars and arsd. And it worked. ars = na.omit(ars) arsd = na.omit(arsd)

No idea if there are other problems.

[BioInfoUCI]

I've found the same error, even though there are no NANs or NAs in my data. Any suggestions, please?

[vickyrev]

I had the same issue and solved it by filtering cells by nFeature_spliced more stringently before running SCTransform. Hope it helps!

[christensensm]

@vickyrev I found the same to help for me. Thanks!

[abysslover]

@pkharchenko In my case, all the matrices become NA after the following statements. I changed two things. (1) Matrix::colSums(tp) This function needs to be called with a "na.rm = TRUE" parameter in order to avoid NA-only result. (2) Any NA values in the sparse matrix should be converted to zero.

ORIGINAL

cat("transition probs ... ")
tp <- exp(cc/corr.sigma) * emb.knn
tp <- t(t(tp)/Matrix::colSums(tp))   # Matrix::colSums(tp) makes every element NA
tp <- as(tp, "dgCMatrix")
cat("done\n")
...
# without replacing NAs with zeros, arsd becomes an NA matrix
arsd <- data.frame(t(**embArrows(emb, tp, arrow.scale, 
                                   n.cores)**))

CHANGED

cat("transition probs ... ")
tp <- exp(cc/corr.sigma) * emb.knn
tp <- t(t(tp)/Matrix::colSums(tp, na.rm =TRUE))
tp <- as.matrix(tp)
tp[is.na(tp)] <- 0
tp <- as(tp, "dgCMatrix")
cat("done\n")

I hope that this helps anyone who encounters this problem.

[hk20013106]

I am having the same issue.

However, if I pre-filter out cells more stringently based on nFeatures and nUMI the problem has gone away. Thanks!

This works for me. Thanks!

[jordan841220]

Very weird... Nothing from the above answers works for me but I accidentally solve the problem simply by adding the argument "n.cores = 10" in show.velocity.on.embedding.cor()......

[yixudong]

@pkharchenko In my case, all the matrices become NA after the following statements. I changed two things. (1) Matrix::colSums(tp) This function needs to be called with a "na.rm = TRUE" parameter in order to avoid NA-only result. (2) Any NA values in the sparse matrix should be converted to zero.

ORIGINAL

cat("transition probs ... ")
tp <- exp(cc/corr.sigma) * emb.knn
tp <- t(t(tp)/Matrix::colSums(tp))   # Matrix::colSums(tp) makes every element NA
tp <- as(tp, "dgCMatrix")
cat("done\n")
...
# without replacing NAs with zeros, arsd becomes an NA matrix
arsd <- data.frame(t(**embArrows(emb, tp, arrow.scale, 
                                   n.cores)**))

CHANGED

cat("transition probs ... ")
tp <- exp(cc/corr.sigma) * emb.knn
tp <- t(t(tp)/Matrix::colSums(tp, na.rm =TRUE))
tp <- as.matrix(tp)
tp[is.na(tp)] <- 0
tp <- as(tp, "dgCMatrix")
cat("done\n")

I hope that this helps anyone who encounters this problem. This works for me. Thanks!

[hakobyansiras]

Dear all, we also faced this issue during our analysis. After detailed debugging of the problem, I found the issue which comes from the part where correlation coefficients are calculated. Please check your data to see if any of the samples from spliced and unspliced matrixes have 0 standard deviations (most probably nonfiltered cells, which have all 0s). This 0 SD samples lead to NaN values from correlation calculation, which leads to the same error "'from' must be a finite number".

You need to either remove those 0 SD cells or set all NaN values to 0 in arsd table in the body of show.velocity.on.embedding.cor function.

[amingson]

Dear all, we also faced this issue during our analysis. After detailed debugging of the problem, I found the issue which comes from the part where correlation coefficients are calculated. Please check your data to see if any of the samples from spliced and unspliced matrixes have 0 standard deviations (most probably nonfiltered cells, which have all 0s). This 0 SD samples lead to NaN values from correlation calculation, which leads to the same error "'from' must be a finite number".

You need to either remove those 0 SD cells or set all NaN values to 0 in arsd table in the body of show.velocity.on.embedding.cor function.

In response to this suggestion, I have modified the source code and made it available for everyone to use. source("./show.velocity.on.embedding.cor.my.R") environment(show.velocity.on.embedding.cor.my) <- environment(show.velocity.on.embedding.cor) p1 <- show.velocity.on.embedding.cor.my(ldatumap,rvel.cd,n=30,scale='sqrt',cell.colors=ac(colors,alpha=0.5), cex=0.8,arrow.scale=2,show.grid.flow=T,min.grid.cell.mass=1.0,grid.n=50,arrow.lwd=1,do.par=F, cell.border.alpha =0.1,USE_OPENMP=1,n.cores=50,main="Cell Velocity")

modified R script

show.velocity.on.embedding.cor.my <- function (emb, vel, n = 100, cell.colors = NULL, corr.sigma = 0.05, show.grid.flow = FALSE, grid.n = 20, grid.sd = NULL, min.grid.cell.mass = 1, min.arrow.size = NULL, arrow.scale = 1, max.grid.arrow.length = NULL, fixed.arrow.length = FALSE, plot.grid.points = FALSE, scale = "log", nPcs = NA, arrow.lwd = 1, xlab = "", ylab = "", n.cores = defaultNCores(), do.par = T, show.cell = NULL, cell.border.alpha = 0.3, cc = NULL, return.details = FALSE, expression.scaling = FALSE, ...) { randomize <- FALSE if (do.par) par(mfrow = c(1, 1), mar = c(3.5, 3.5, 2.5, 1.5), mgp = c(2, 0.65, 0), cex = 0.85) celcol <- "white" if (is.null(show.cell)) { celcol <- cell.colors[rownames(emb)] } plot(emb, bg = celcol, pch = 21, col = ac(1, alpha = cell.border.alpha), xlab = xlab, ylab = ylab, ...) em <- as.matrix(vel$current) ccells <- intersect(rownames(emb), colnames(em)) em <- em[, ccells] emb <- emb[ccells, ] nd <- as.matrix(vel$deltaE[, ccells]) cgenes <- intersect(rownames(em), rownames(nd)) nd <- nd[cgenes, ] em <- em[cgenes, ] if (randomize) { nd <- t(apply(nd, 1, function(x) (rbinom(length(x), 1, 0.5) 2 - 1) abs(sample(x)))) } if (is.null(cc)) { cat("delta projections ... ") if (scale == "log") { cat("log ") cc <- colDeltaCorLog10(em, (log10(abs(nd) + 1) sign(nd)), nthreads = n.cores) } else if (scale == "sqrt") { cat("sqrt ") cc <- colDeltaCorSqrt(em, (sqrt(abs(nd)) sign(nd)), nthreads = n.cores) } else if (scale == "rank") { cat("rank ") cc <- colDeltaCor((apply(em, 2, rank)), (apply(nd, 2, rank)), nthreads = n.cores) } else { cat("linear ") cc <- colDeltaCor(em, nd, nthreads = n.cores) } colnames(cc) <- rownames(cc) <- colnames(em) diag(cc) <- 0 } cat("knn ... ") if (n > nrow(cc)) { n <- nrow(cc) } emb.knn <- balancedKNN(t(emb), k = n, maxl = nrow(emb), dist = "euclidean", n.threads = n.cores) diag(emb.knn) <- 1 cat("transition probs ... ") tp <- exp(cc/corr.sigma) emb.knn tp <- t(t(tp)/Matrix::colSums(tp)) tp <- as(tp, "dgCMatrix") cat("done\n") if (!is.null(show.cell)) { i <- match(show.cell, rownames(emb)) if (is.na(i)) stop(paste("specified cell", i, "is not in the embedding")) points(emb, pch = 19, col = ac(val2col(tp[rownames(emb), show.cell], gradient.range.quantile = 1), alpha = 0.5)) points(emb[show.cell, 1], emb[show.cell, 2], pch = 3, cex = 1, col = 1) di <- t(t(emb) - emb[i, ]) di <- di/sqrt(Matrix::rowSums(di^2)) arrow.scale di[i, ] <- 0 dir <- Matrix::colSums(di tp[, i]) dic <- Matrix::colSums(di (tp[, i] > 0)/sum(tp[, i] > 0)) dia <- dir - dic suppressWarnings(arrows(emb[colnames(em)[i], 1], emb[colnames(em)[i], 2], emb[colnames(em)[i], 1] + dic[1], emb[colnames(em)[i], 2] + dic[2], length = 0.05, lwd = 1, col = "blue")) suppressWarnings(arrows(emb[colnames(em)[i], 1], emb[colnames(em)[i], 2], emb[colnames(em)[i], 1] + dir[1], emb[colnames(em)[i], 2] + dir[2], length = 0.05, lwd = 1, col = "red")) suppressWarnings(arrows(emb[colnames(em)[i], 1] + dic[1], emb[colnames(em)[i], 2] + dic[2], emb[colnames(em)[i], 1] + dir[1], emb[colnames(em)[i], 2] + dir[2], length = 0.05, lwd = 1, lty = 1, col = "grey50")) suppressWarnings(arrows(emb[colnames(em)[i], 1], emb[colnames(em)[i], 2], emb[colnames(em)[i], 1] + dia[1], emb[colnames(em)[i], 2] + dia[2], length = 0.05, lwd = 1, col = "black")) } else { cat("calculating arrows ... ") arsd <- data.frame(t(embArrows(emb, tp, arrow.scale, n.cores))) arsd[is.na(arsd)] <- 0 rownames(arsd) <- rownames(emb) if (expression.scaling) { tpb <- tp > 0 tpb <- t(t(tpb)/colSums(tpb)) es <- as.matrix(em %% tp) - as.matrix(em %% as.matrix(tpb)) pl <- pmin(1, pmax(0, apply(as.matrix(vel$deltaE[, colnames(es)]) es, 2, sum)/sqrt(colSums(es es)))) arsd <- arsd pl } ars <- data.frame(cbind(emb, emb + arsd)) colnames(ars) <- c("x0", "y0", "x1", "y1") colnames(arsd) <- c("xd", "yd") rownames(ars) <- rownames(emb) cat("done\n") if (show.grid.flow) { cat("grid estimates ... ") rx <- range(c(range(ars$x0), range(ars$x1))) ry <- range(c(range(ars$y0), range(ars$y1))) gx <- seq(rx[1], rx[2], length.out = grid.n) gy <- seq(ry[1], ry[2], length.out = grid.n) if (is.null(grid.sd)) { grid.sd <- sqrt((gx[2] - gx[1])^2 + (gy[2] - gy[1])^2)/2 cat("grid.sd=", grid.sd, " ") } if (is.null(min.arrow.size)) { min.arrow.size <- sqrt((gx[2] - gx[1])^2 + (gy[2] - gy[1])^2) 0.01 cat("min.arrow.size=", min.arrow.size, " ") } if (is.null(max.grid.arrow.length)) { max.grid.arrow.length <- sqrt(sum((par("pin")/c(length(gx), length(gy)))^2)) 0.25 cat("max.grid.arrow.length=", max.grid.arrow.length, " ") } garrows <- do.call(rbind, lapply(gx, function(x) { cd <- sqrt(outer(emb[, 2], -gy, "+")^2 + (x - emb[, 1])^2) cw <- dnorm(cd, sd = grid.sd) gw <- Matrix::colSums(cw) cws <- pmax(1, Matrix::colSums(cw)) gxd <- Matrix::colSums(cw arsd$xd)/cws gyd <- Matrix::colSums(cw arsd$yd)/cws al <- sqrt(gxd^2 + gyd^2) vg <- gw >= min.grid.cell.mass & al >= min.arrow.size cbind(rep(x, sum(vg)), gy[vg], x + gxd[vg], gy[vg] + gyd[vg]) })) colnames(garrows) <- c("x0", "y0", "x1", "y1") if (fixed.arrow.length) { suppressWarnings(arrows(garrows[, 1], garrows[, 2], garrows[, 3], garrows[, 4], length = 0.05, lwd = arrow.lwd)) } else { alen <- pmin(max.grid.arrow.length, sqrt(((garrows[, 3] - garrows[, 1]) par("pin")[1]/diff(par("usr")[c(1, 2)]))^2 + ((garrows[, 4] - garrows[, 2]) par("pin")[2]/diff(par("usr")[c(3, 4)]))^2)) suppressWarnings(lapply(1:nrow(garrows), function(i) arrows(garrows[i, 1], garrows[i, 2], garrows[i, 3], garrows[i, 4], length = alen[i], lwd = arrow.lwd))) } if (plot.grid.points) points(rep(gx, each = length(gy)), rep(gy, length(gx)), pch = ".", cex = 0.1, col = ac(1, alpha = 0.4)) cat("done\n") if (return.details) { cat("expression shifts .") scale.int <- switch(scale, log = 2, sqrt = 3, 1) if (!expression.scaling) { tpb <- tp > 0 tpb <- t(t(tpb)/colSums(tpb)) es <- as.matrix(em %% tp) - as.matrix(em %% as.matrix(tpb)) } cat(".") gs <- do.call(cbind, parallel::mclapply(gx, function(x) { cd <- sqrt(outer(emb[, 2], -gy, "+")^2 + (x - emb[, 1])^2) cw <- dnorm(cd, sd = grid.sd) gw <- Matrix::colSums(cw) cws <- pmax(1, Matrix::colSums(cw)) cw <- t(t(cw)/cws) gxd <- Matrix::colSums(cw arsd$xd) gyd <- Matrix::colSums(cw arsd$yd) al <- sqrt(gxd^2 + gyd^2) vg <- gw >= min.grid.cell.mass & al >= min.arrow.size if (any(vg)) { z <- es %% cw[, vg] } else { NULL } }, mc.cores = n.cores, mc.preschedule = T)) if (scale == "log") { nd <- (log10(abs(nd) + 1) sign(nd)) } else if (scale == "sqrt") { nd <- (sqrt(abs(nd)) sign(nd)) } cat(".") gv <- do.call(cbind, parallel::mclapply(gx, function(x) { cd <- sqrt(outer(emb[, 2], -gy, "+")^2 + (x - emb[, 1])^2) cw <- dnorm(cd, sd = grid.sd) gw <- Matrix::colSums(cw) cws <- pmax(1, Matrix::colSums(cw)) cw <- t(t(cw)/cws) gxd <- Matrix::colSums(cw arsd$xd) gyd <- Matrix::colSums(cw arsd$yd) al <- sqrt(gxd^2 + gyd^2) vg <- gw >= min.grid.cell.mass & al >= min.arrow.size if (any(vg)) { z <- nd %% cw[, vg] } else { NULL } }, mc.cores = n.cores, mc.preschedule = T)) cat(". done\n") return(invisible(list(tp = tp, cc = cc, garrows = garrows, arrows = as.matrix(ars), vel = nd, eshifts = es, gvel = gv, geshifts = gs, scale = scale))) } } else { apply(ars, 1, function(x) { if (fixed.arrow.length) { suppressWarnings(arrows(x[1], x[2], x[3], x[4], length = 0.05, lwd = arrow.lwd)) } else { ali <- sqrt(((x[3] - x[1]) par("pin")[1]/diff(par("usr")[c(1, 2)]))^2 + ((x[4] - x[2]) * par("pin")[2]/diff(par("usr")[c(3, 4)]))^2) suppressWarnings(arrows(x[1], x[2], x[3], x[4], length = min(0.05, ali), lwd = arrow.lwd)) } }) } } return(invisible(list(tp = tp, cc = cc))) }