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type parameter in formula composition about sccomp HOT 15 CLOSED

MartaCasado commented on June 2, 2024

type parameter in formula composition

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Comments (15)

JovMaksimovic commented on June 2, 2024 1

Thanks @stemangiola - it now works for me.

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stemangiola commented on June 2, 2024

Hello @MartaCasado, they should not. Could you please paste here the two estimate tibbles for the two comparisons?

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MartaCasado commented on June 2, 2024

Hi @stemangiola,

Sorry for the delay in my response.

What differs when I change the conditions' order are the boxplots (the significant cell types), but not the credible interval plots. With tibbles do you mean the output of res? I attach both results below.

Aged condition first:
`> res1

# A tibble: 16 × 9
   cell_group            parameter   covariate c_lower c_effect c_upper   c_pH0   c_FDR count_data       
   <chr>                 <chr>       <chr>       <dbl>    <dbl>   <dbl>   <dbl>   <dbl> <list>           
 1 Macrophage_3          (Intercept) NA         1.17     1.29    1.41   0       0       <tibble [6 × 10]>
 2 Macrophage_3          typeadult   type      -0.0246   0.192   0.416  0.530   0.273   <tibble [6 × 10]>
 3 Macrophage_1          (Intercept) NA         0.523    0.670   0.801  0       0       <tibble [6 × 10]>
 4 Macrophage_1          typeadult   type      -1.18    -0.898  -0.639  0       0       <tibble [6 × 10]>
 5 Monocyte_3            (Intercept) NA         0.116    0.264   0.404  0.180   0.0485  <tibble [6 × 10]>
 6 Monocyte_3            typeadult   type       0.309    0.587   0.860  0.00400 0.00200 <tibble [6 × 10]>
 7 Monocyte_1            (Intercept) NA        -0.343   -0.125   0.0635 0.765   0.138   <tibble [6 × 10]>
 8 Monocyte_1            typeadult   type      -0.616   -0.213   0.177  0.473   0.209   <tibble [6 × 10]>
 9 Macrophage_2          (Intercept) NA        -0.522   -0.307  -0.115  0.133   0.0266  <tibble [6 × 10]>
10 Macrophage_2          typeadult   type      -0.366    0.0298  0.429  0.824   0.470   <tibble [6 × 10]>
11 Monocyte_2            (Intercept) NA        -0.629   -0.413  -0.210  0.0208  0.00526 <tibble [6 × 10]>
12 Monocyte_2            typeadult   type      -0.443   -0.0376  0.373  0.804   0.420   <tibble [6 × 10]>
13 Proliferative myeloid (Intercept) NA        -0.629   -0.440  -0.270  0.00551 0.00138 <tibble [6 × 10]>
14 Proliferative myeloid typeadult   type      -0.338    0.0615  0.468  0.770   0.356   <tibble [6 × 10]>
15 Dendritic cell        (Intercept) NA        -1.17    -0.925  -0.687  0       0       <tibble [6 × 10]>
16 Dendritic cell        typeadult   type      -0.198    0.281   0.744  0.358   0.121   <tibble [6 × 10]>`

Adult condition first:

`> res2

# A tibble: 16 × 9
   cell_group            parameter   covariate c_lower c_effect c_upper   c_pH0   c_FDR count_data       
   <chr>                 <chr>       <chr>       <dbl>    <dbl>   <dbl>   <dbl>   <dbl> <list>           
 1 Macrophage_3          (Intercept) NA          1.14    1.27    1.38   0       0       <tibble [6 × 10]>
 2 Macrophage_3          typeaged    type       -0.483  -0.236  -0.0146 0.373   0.181   <tibble [6 × 10]>
 3 Macrophage_1          (Intercept) NA          0.513   0.659   0.793  0       0       <tibble [6 × 10]>
 4 Macrophage_1          typeaged    type        0.599   0.871   1.15   0       0       <tibble [6 × 10]>
 5 Monocyte_3            (Intercept) NA          0.113   0.253   0.393  0.222   0.0412  <tibble [6 × 10]>
 6 Monocyte_3            typeaged    type       -0.890  -0.612  -0.322  0.00350 0.00175 <tibble [6 × 10]>
 7 Monocyte_1            (Intercept) NA         -0.368  -0.138   0.0398 0.723   0.173   <tibble [6 × 10]>
 8 Monocyte_1            typeaged    type       -0.206   0.179   0.595  0.538   0.252   <tibble [6 × 10]>
 9 Macrophage_2          (Intercept) NA         -0.424  -0.221  -0.0326 0.413   0.0943  <tibble [6 × 10]>
10 Macrophage_2          typeaged    type       -0.230   0.151   0.550  0.603   0.311   <tibble [6 × 10]>
11 Monocyte_2            (Intercept) NA         -0.654  -0.428  -0.216  0.0183  0.00511 <tibble [6 × 10]>
12 Monocyte_2            typeaged    type       -0.409   0.0151  0.445  0.815   0.425   <tibble [6 × 10]>
13 Proliferative myeloid (Intercept) NA         -0.657  -0.453  -0.248  0.00726 0.00181 <tibble [6 × 10]>
14 Proliferative myeloid typeaged    type       -0.477  -0.0818  0.299  0.723   0.370   <tibble [6 × 10]>
15 Dendritic cell        (Intercept) NA         -1.18   -0.928  -0.690  0       0       <tibble [6 × 10]>
16 Dendritic cell        typeaged    type       -0.793  -0.292   0.243  0.346   0.117   <tibble [6 × 10]>
`

Thank you,
Marta

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stemangiola commented on June 2, 2024

These small changes could be due to the algorithm not being deterministic, or slightly tight priors on the intercept.

I would suggest, use the formula ~ 0 + factor_of_interest and use contrasts argument for the hypothesis testing, to have a perfect symmetric outcome.

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MartaCasado commented on June 2, 2024

Thanks for the feedback!

I've used the following:

res = data |> sccomp_glm( formula_composition = ~ 0 + type, formula_variability = ~ 1, contrasts = c(typeadult - typeaged), percent_false_positive = 5, .sample = sample, .cell_group = cell_group, )

However, I get this error:

sccomp says: outlier identification first pass - step 1/3 [ETA: ~20s] Error in data_spread_to_model_input(., formula_composition, !!.sample, : object 'typeadult' not found

Thanks!!

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stemangiola commented on June 2, 2024

try to use

formula_variability = ~ 0 + type

if that fixes it, I have to fix in case variability has no covariates

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MartaCasado commented on June 2, 2024

thank you! I've tried, but I still get the same error.

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stemangiola commented on June 2, 2024

Are you sure one of your covariate values is "adult", can you show me the distinct(type) of your input table?

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MartaCasado commented on June 2, 2024

I use as input the Seurat object.

> unique(data$type)
[1] adult aged
Levels: aged adult

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stemangiola commented on June 2, 2024

Can you send here input data and code?

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MartaCasado commented on June 2, 2024

input data:

> data
An object of class Seurat 
12815 features across 3781 samples within 1 assay 
Active assay: RNA (12815 features, 1516 variable features)
 2 dimensional reductions calculated: pca, umap

code:

data$cell_group = data$final.annot
data$sample = data$replicate
data$type = data$age

res = data |> 
  sccomp_glm( formula_composition = ~ type, 
              formula_variability = ~ 0 + type, 
              contrasts = c(typeadult - typeaged), 
              percent_false_positive = 5, 
              .sample = sample, 
              .cell_group = cell_group, )

Thank you!!

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stemangiola commented on June 2, 2024

This is the correct usage

res = data |> 
  sccomp_glm( formula_composition = ~ 0 + type, 
              formula_variability = ~ 0 + type, 
              contrasts = c(typeadult - typeaged), 
              percent_false_positive = 5, 
              .sample = sample, 
              .cell_group = cell_group, )

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MartaCasado commented on June 2, 2024

Hi @stemangiola, I've tried that, but I still get this:

sccomp says: outlier identification first pass - step 1/3 [ETA: ~20s]
Error in data_spread_to_model_input(., formula_composition, !!.sample,  : 
  object 'typeadult' not found

Thank you

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JovMaksimovic commented on June 2, 2024

Hi @stemangiola,
I have come across the same error with my single cell RNAseq data.

The code I'm trying to run:

res <-
  seu |>
  sccomp_glm( 
    formula_composition = ~ 0 + Disease, 
    formula_variability = ~ 0 + Disease, 
    contrasts =  c(DiseaseHealthy - DiseaseCF, 
                   DiseaseHealthy - DiseaseCSLD, 
                   DiseaseHealthy - DiseaseWheeze),
    .sample = sample.id,
    .cell_group = predicted.Broad)

The error:

Error in data_spread_to_model_input(., formula_composition, !!.sample,  : 
  object 'DiseaseHealthy' not found

My input data:

 > seu
An object of class Seurat 
35369 features across 190480 samples within 4 assays 
Active assay: refAssay (17656 features, 0 variable features)
 3 other assays present: RNA, prediction.score.Annotation, prediction.score.Broad
> levels(seu$Disease)
[1] "CF"      "CSLD"    "Healthy" "Wheeze"

Session information:

> sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /usr/lib64/libblas.so.3.4.2
LAPACK: /usr/lib64/liblapack.so.3.4.2

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] edgeR_3.38.1                sccomp_0.99.30              SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.1
 [5] Biobase_2.56.0              GenomicRanges_1.48.0        GenomeInfoDb_1.32.2         IRanges_2.30.0             
 [9] S4Vectors_0.34.0            BiocGenerics_0.42.0         MatrixGenerics_1.8.1        matrixStats_0.62.0         
[13] limma_3.52.2                speckle_0.99.0              paletteer_1.4.0             patchwork_1.1.2            
[17] sp_1.5-0                    SeuratObject_4.1.0          Seurat_4.1.1                glue_1.6.2                 
[21] here_1.0.1                  forcats_0.5.1               stringr_1.4.0               dplyr_1.0.9                
[25] purrr_0.3.4                 readr_2.1.2                 tidyr_1.2.0                 tibble_3.1.7               
[29] ggplot2_3.3.6               tidyverse_1.3.1             BiocStyle_2.24.0            workflowr_1.7.0

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stemangiola commented on June 2, 2024

Hello @JovMaksimovic , try adding quotes

res <-
  seu |>
  sccomp_glm( 
    formula_composition = ~ 0 + Disease, 
    formula_variability = ~ 0 + Disease, 
    contrasts =  c("DiseaseHealthy - DiseaseCF", 
                   "DiseaseHealthy - DiseaseCSLD", 
                   "DiseaseHealthy - DiseaseWheeze"),
    .sample = sample.id,
    .cell_group = predicted.Broad)

I have updated the documentation.

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type parameter in formula composition about sccomp HOT 15 CLOSED

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