Error in cor(x, y, use = use, method = method)

[zhangqiannn]

hi,
first, i wanna thank you for developing this package. It is wonderful in cancer research.
i try to use function SpaCET_obj1 <- SpaCET.deconvolution(SpaCET_obj1, cancerType="CESC", coreNo=8) in my data
the error is as following:

Error in cor(x, y, use = use, method = method) : 
  long vectors not supported yet: cov.c:722
Calls: SpaCET.deconvolution -> inferMal_cor -> corMat -> <Anonymous> -> cor

i have two sample with different size, the smaller can run normally, the larger not.
i'd like to know why and how to solve this problem?
Best,
Qian

[beibeiru]

Hi, Qian,

Would you please deposit your larger sample (which reports errors) in the following folder? I will figure out the bug. Thanks.

Best,
Beibei

[zhangqiannn]

Hi, Beibei, Thank you for your reply. I've uploaded the data in the folder. Best, Qian

…

On Feb 21, 2023, at 22:40, Beibei Ru ***@***.***> wrote: Hi, Qian, Would you please deposit your larger sample (which reports errors) in the following folder? I will figure out the bug. Thanks. https://connecthkuhk-my.sharepoint.com/:f:/g/personal/bbru_connect_hku_hk/EpzSwS9WfOZDrELV9aIeO1UBLUu6PibkUG16Ffvx_dxL4g?e=Z8y4wm Best, Beibei — Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AYTS4WVB2KMFJFRNMFWQYEDWYTHULANCNFSM6AAAAAAVCXFRW4>. You are receiving this because you authored the thread.

[zhangqiannn]

Hi, Beibei, I’m surprisingly find this software works so well in my other small samples just now. I truly want to apply it in larger samples. Did the data upload successfully? Looking forward to your reply. Best, Qian

…

On Feb 27, 2023, at 10:08, 张倩 ***@***.***> wrote: Hi, Beibei, Thank you for your reply. I've uploaded the data in the folder. Best, Qian > On Feb 21, 2023, at 22:40, Beibei Ru ***@***.***> wrote: > > > Hi, Qian, > > Would you please deposit your larger sample (which reports errors) in the following folder? I will figure out the bug. Thanks. > > https://connecthkuhk-my.sharepoint.com/:f:/g/personal/bbru_connect_hku_hk/EpzSwS9WfOZDrELV9aIeO1UBLUu6PibkUG16Ffvx_dxL4g?e=Z8y4wm > > Best, > Beibei > > — > Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AYTS4WVB2KMFJFRNMFWQYEDWYTHULANCNFSM6AAAAAAVCXFRW4>. > You are receiving this because you authored the thread. >

[beibeiru]

Hi, Qian,

Thank you for uploading your data and I can access them right now.
I am wondering whether your larger sample is ONE visium dataset? Because I observe 58,432 spots in the larger sample.

Best,
Beibei

[zhangqiannn]

Hi, Beibei, The sequencing platform used for this sample is Stereo-seq. The data is derived from a single sample, rather than a mix of samples. Best, Qian

…

On Feb 28, 2023, at 00:05, Beibei Ru ***@***.***> wrote: Hi, Qian, Thank you for uploading your data and I can access them right now. I am wondering whether your larger sample is ONE visium dataset? Because I observe 58,432 spots in the larger sample. Best, Beibei — Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AYTS4WSI2Z3VWKE2GYCSMA3WZTGEDANCNFSM6AAAAAAVCXFRW4>. You are receiving this because you authored the thread.

[beibeiru]

Sure. Thank you for the clarification. I will let you know once I fix it. Thanks.

Best,
Beibei

[beibeiru]

Hi, Qian,

I notice that the spot size of Stereo-seq is 0.22 um. In your dataset, you got 58,432 bins finally.
Just wondering what the size of a bin is. Do you mind telling me how you transform the Stereo-seq data from spot-level to bin-level? Thanks.

Best,
Beibei

[zhangqiannn]

Hi, Beibei, Standard DNB chips have spots with approximately 220 nm diameter and a center-to-center distance of 500 or 715 nm. Here, we used the distance of center to center is 500 nm. The capture spots were grouped into bins to include sufficient genes per bin. The bin size we selected is bin 50 (50 × 50 spots, i.e., 24.72 × 24.72 μm).This article provides a detailed introduction to the principles of Stereo-Seq. Title: Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Original link: https://www.cell.com/cell/fulltext/S0092-8674(22)00399-3?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867422003993%3Fshowall%3Dtrue Best, Qian

…

On Mar 1, 2023, at 10:16, Beibei Ru ***@***.***> wrote: Hi, Qian, I notice that the spot size of Stereo-seq is 0.22 um. In your dataset, you got 58,432 bins finally. Just wondering what the size of a bin is. Do you mind telling me how you transform the Stereo-seq data from spot-level to bin-level? Thanks. Best, Beibei — Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AYTS4WQQOLR55M2RHU7GVKDWZ2WOZANCNFSM6AAAAAAVCXFRW4>. You are receiving this because you authored the thread.

[beibeiru]

Hi, Qian,

I have updated SpaCET to process a larger ST dataset (e.g., >50,000 spots or bins). Please reinstall SpaCET and try the following code. I also deposited the results that I got in the following link.

# reinstall SpaCET
remove.packages("SpaCET")
devtools::install_github("data2intelligence/SpaCET")
library(SpaCET)

# load data
SpaCET_obj <- readRDS("SpaCET_raw.rds")

# modify the input data
SpaCET_obj@input$image$path <- NA
SpaCET_obj@input$image$grob <- NA
SpaCET_obj@input$platform <- "Stereo-seq"

# save fig1 for EPCAM expression
pdf("fig1.pdf",  width = 7.5, height = 7)
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "GeneExpression", 
  spatialFeatures=c("EPCAM"),
  pointSize = 0.01, 
  nrow=1
)
dev.off()

SpaCET_obj <- SpaCET.quality.control(SpaCET_obj)

# save fig2 for QC
pdf("fig2.pdf",  width = 15, height = 7)
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "QualityControl", 
  spatialFeatures=c("UMI","Gene"),
  pointSize = 0.01, 
  nrow=1
)
dev.off()

# run deconvolution
SpaCET_obj <- SpaCET.deconvolution(SpaCET_obj, cancerType="CESC", coreNo=8)

# save fig3 for cell type fraction
pdf("fig3.pdf",  width = 50, height = 35)
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "CellFraction", 
  spatialFeatures=c("All"), 
  pointSize = 0.01, 
  nrow=5
)
dev.off()

# save new data
saveRDS(SpaCET_obj, file = "SpaCET_new.rds")

Please let me know if you have any further questions.

Best,
Beibei

[zhangqiannn]

Hi, Beibei, First of all, thank you very much for your efforts for my larger sample. I have reinstalled SpaCET and started testing it. Best, Qian

…

On Mar 6, 2023, at 11:56, Beibei Ru ***@***.***> wrote: Hi, Qian, I have updated SpaCET to process a larger ST dataset (e.g., >50,000 spots or bins). Please reinstall SpaCET and try the following code. I also deposited the results that I got in the following link. https://connecthkuhk-my.sharepoint.com/:f:/g/personal/bbru_connect_hku_hk/EpzSwS9WfOZDrELV9aIeO1UBLUu6PibkUG16Ffvx_dxL4g?e=BOMpjO # reinstall SpaCET remove.packages("SpaCET") devtools::install_github("data2intelligence/SpaCET") library(SpaCET) # load data SpaCET_obj <- readRDS("SpaCET_raw.rds") # modify the input data ***@***.***$image$path <- NA ***@***.***$image$grob <- NA ***@***.***$platform <- "Stereo-seq" # save fig1 for EPCAM expression pdf("fig1.pdf", width = 7.5, height = 7) SpaCET.visualize.spatialFeature( SpaCET_obj, spatialType = "GeneExpression", spatialFeatures=c("EPCAM"), pointSize = 0.01, nrow=1 ) dev.off() SpaCET_obj <- SpaCET.quality.control(SpaCET_obj) # save fig2 for QC pdf("fig2.pdf", width = 15, height = 7) SpaCET.visualize.spatialFeature( SpaCET_obj, spatialType = "QualityControl", spatialFeatures=c("UMI","Gene"), pointSize = 0.01, nrow=1 ) dev.off() # run deconvolution SpaCET_obj <- SpaCET.deconvolution(SpaCET_obj, cancerType="CESC", coreNo=10) # save fig3 for cell type fraction pdf("fig3.pdf", width = 50, height = 35) SpaCET.visualize.spatialFeature( SpaCET_obj, spatialType = "CellFraction", spatialFeatures=c("All"), pointSize = 0.01, nrow=5 ) dev.off() # save new data saveRDS(SpaCET_obj, file = "SpaCET_new.rds") Please let me know if you have any further questions. Best, Beibei — Reply to this email directly, view it on GitHub <#7 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AYTS4WRRKBP3MTFU44XALKLW2VOAJANCNFSM6AAAAAAVCXFRW4>. You are receiving this because you authored the thread.