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theMILOlab / SPATA2

A Toolbox for Spatial Transcriptomics Analysis
https://themilolab.github.io/SPATA2/
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CNV analysis #28

Closed YuanningEric closed 1 week ago

YuanningEric commented 2 years ago

Hi,

When running the CNV analysis on tumor #T275 using the runCnvAnalysis function, an error message occurred:

"13:24:54 Checking input for reference data. 13:24:54 Combining input and reference data. 13:24:55 Getting gene positions. Error in base::is.data.frame(gene_pos_df) : promise already under evaluation: recursive default argument reference or earlier problems?"

What may cause this problem?

kueckelj commented 2 years ago

Hello YuanningEric, sorry for the late reply.

Can you try the following code chunk:

spata_object <- runCnvAnalysis(object = spata_object, gene_pos_df = SPATA2::gene_pos_df)

Does that work?

Dantferno commented 2 years ago

Hi, thank you for the package :)

I faced the same issue as YuanningEric and your code allowed the CNV analysis to proceed

The output is the following with a warning at the end :

13:31:53 Checking input for reference data. 13:31:53 Combining input and reference data. 13:31:54 Getting gene positions. 13:31:54 Starting analysis pipeline. INFO [2022-07-19 13:31:53] ::order_reduce:Start. INFO [2022-07-19 13:31:53] .order_reduce(): expr and order match. INFO [2022-07-19 13:31:56] ::process_data:order_reduce:Reduction from positional data, new dimensions (r,c) = 21827,4825 Total=38947386 Min=0 Max=4373. INFO [2022-07-19 13:31:56] num genes removed taking into account provided gene ordering list: 5528 = 25.3264305676456% removed. INFO [2022-07-19 13:31:56] -filtering out cells < 100 or > Inf, removing 0.145078 % of cells INFO [2022-07-19 13:31:57] validating infercnv_obj 13:31:57 Removing genes from matrix with mean expression below threshold. INFO [2022-07-19 13:31:57] ::above_min_mean_expr_cutoff:Start INFO [2022-07-19 13:31:57] Removing 6430 genes from matrix as below mean expr threshold: 0.1 INFO [2022-07-19 13:31:57] validating infercnv_obj INFO [2022-07-19 13:31:57] There are 9869 genes and 4818 cells remaining in the expr matrix. 13:31:58 Removing low quality barcode spots. INFO [2022-07-19 13:32:01] no genes removed due to min cells/gene filter 13:32:01 Normalizing counts by sequencing depth. INFO [2022-07-19 13:32:01] normalizing counts matrix by depth INFO [2022-07-19 13:32:03] Computed total sum normalization factor as median libsize: 3928.000000 13:32:04 Conducting anscombe and logarithmic transformation. INFO [2022-07-19 13:32:04] transforming log2xplus1() 13:32:06 Applying maximal threshold bounds. INFO [2022-07-19 13:32:11] ::process_data:setting max centered expr, threshold set to: +/-: 2.6 13:32:13 Smoothing by chromosome. INFO [2022-07-19 13:32:12] smooth_by_chromosome: chr: 0 INFO [2022-07-19 13:32:16] smooth_by_chromosome: chr: 1 INFO [2022-07-19 13:32:21] smooth_by_chromosome: chr: 2 INFO [2022-07-19 13:32:26] smooth_by_chromosome: chr: 3 INFO [2022-07-19 13:32:31] smooth_by_chromosome: chr: 4 INFO [2022-07-19 13:32:35] smooth_by_chromosome: chr: 5 INFO [2022-07-19 13:32:40] smooth_by_chromosome: chr: 6 INFO [2022-07-19 13:32:44] smooth_by_chromosome: chr: 7 INFO [2022-07-19 13:32:49] smooth_by_chromosome: chr: 8 INFO [2022-07-19 13:32:53] smooth_by_chromosome: chr: 9 INFO [2022-07-19 13:32:58] smooth_by_chromosome: chr: 10 INFO [2022-07-19 13:33:02] smooth_by_chromosome: chr: 11 INFO [2022-07-19 13:33:07] smooth_by_chromosome: chr: 12 INFO [2022-07-19 13:33:12] smooth_by_chromosome: chr: 13 INFO [2022-07-19 13:33:16] smooth_by_chromosome: chr: 14 INFO [2022-07-19 13:33:20] smooth_by_chromosome: chr: 15 INFO [2022-07-19 13:33:24] smooth_by_chromosome: chr: 16 INFO [2022-07-19 13:33:28] smooth_by_chromosome: chr: 17 INFO [2022-07-19 13:33:34] smooth_by_chromosome: chr: 18 INFO [2022-07-19 13:33:37] smooth_by_chromosome: chr: 19 INFO [2022-07-19 13:33:42] smooth_by_chromosome: chr: 20 INFO [2022-07-19 13:33:46] smooth_by_chromosome: chr: 21 INFO [2022-07-19 13:33:49] smooth_by_chromosome: chr: 22 INFO [2022-07-19 13:33:53] smooth_by_chromosome: chr: 23 13:33:59 Centering cell expression across chromosome. INFO [2022-07-19 13:33:59] ::center_smooth across chromosomes per cell 13:34:03 Subtracting reference expression from observed expression. INFO [2022-07-19 13:34:02] ::subtract_ref_expr_from_obs:Start inv_log=TRUE, use_bounds=TRUE INFO [2022-07-19 13:34:02] subtracting mean(normal) per gene per cell across all data INFO [2022-07-19 13:34:06] -subtracting expr per gene, use_bounds=TRUE 13:34:09 Clearing noise via reference mean standard deviation. INFO [2022-07-19 13:34:09] invert_log2(), computing 2^x INFO [2022-07-19 13:34:10] denoising using mean(normal) +- sd_amplifier * sd(normal) per gene per cell across all data INFO [2022-07-19 13:34:10] :: clear_noise_via_ref_quantiles : removing noise between bounds: 0.967257138207395 - 1.0327428617926 13:34:11 Removing outliers. INFO [2022-07-19 13:34:11] ::remove_outlier_norm:Start out_method: average_bound lower_bound: NA upper_bound: NA INFO [2022-07-19 13:34:11] ::remove_outlier_norm:Start out_method: average_bound lower_bound: NA upper_bound: NA INFO [2022-07-19 13:34:11] ::remove_outlier_norm using method: average_bound for defining outliers. INFO [2022-07-19 13:34:15] outlier bounds defined between: 0.91917 - 1.12623 13:34:16 Defining significant tumor subclusters. INFO [2022-07-19 13:34:16] define_signif_tumor_subclusters(p_val=0.05 INFO [2022-07-19 13:34:16] define_signif_tumor_subclusters(), tumor: slide1 INFO [2022-07-19 13:38:31] cut height based on p_val(0.05) = 4.71875 and partition_method: qnorm INFO [2022-07-19 13:38:31] cut tree into: 30 groups INFO [2022-07-19 13:38:31] -processing slide1,slide1_s1 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s2 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s3 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s4 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s5 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s6 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s7 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s8 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s9 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s10 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s11 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s12 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s13 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s14 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s15 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s16 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s17 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s18 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s19 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s20 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s21 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s22 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s23 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s24 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s25 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s26 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s27 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s28 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s29 INFO [2022-07-19 13:38:31] -processing slide1,slide1_s30 INFO [2022-07-19 13:38:31] define_signif_tumor_subclusters(), tumor: ref INFO [2022-07-19 13:41:14] cut height based on p_val(0.05) = 3.12634 and partition_method: qnorm INFO [2022-07-19 13:41:14] cut tree into: 94 groups INFO [2022-07-19 13:41:14] -processing ref,ref_s1 INFO [2022-07-19 13:41:14] -processing ref,ref_s2 INFO [2022-07-19 13:41:14] -processing ref,ref_s3 INFO [2022-07-19 13:41:14] -processing ref,ref_s4 INFO [2022-07-19 13:41:14] -processing ref,ref_s5 INFO [2022-07-19 13:41:14] -processing ref,ref_s6 INFO [2022-07-19 13:41:14] -processing ref,ref_s7 INFO [2022-07-19 13:41:14] -processing ref,ref_s8 INFO [2022-07-19 13:41:14] -processing ref,ref_s9 INFO [2022-07-19 13:41:14] -processing ref,ref_s10 INFO [2022-07-19 13:41:14] -processing ref,ref_s11 INFO [2022-07-19 13:41:14] -processing ref,ref_s12 INFO [2022-07-19 13:41:14] -processing ref,ref_s13 INFO [2022-07-19 13:41:14] -processing ref,ref_s14 INFO [2022-07-19 13:41:14] -processing ref,ref_s15 INFO [2022-07-19 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13:41:14] -processing ref,ref_s94 13:41:14 Copy number variation pipeline completed. 13:41:14 Saving infercnv-object under 'test_cnv/infercnv-obj.RDS'. 13:41:27 Plotting results. INFO [2022-07-19 13:41:26] ::plot_cnv:Start INFO [2022-07-19 13:41:26] ::plot_cnv:Current data dimensions (r,c)=9869,4818 Total=47653086.2879817 Min=0.919170051016159 Max=1.12623258696454. INFO [2022-07-19 13:41:27] ::plot_cnv:Depending on the size of the matrix this may take a moment. INFO [2022-07-19 13:41:27] plot_cnv(): auto thresholding at: (0.873767 , 1.126233) INFO [2022-07-19 13:41:28] plot_cnv_observation:Start INFO [2022-07-19 13:41:28] Observation data size: Cells= 2704 Genes= 9869 INFO [2022-07-19 13:41:28] plot_cnv_observation:Writing observation groupings/color. INFO [2022-07-19 13:41:28] plot_cnv_observation:Done writing observation groupings/color. INFO [2022-07-19 13:41:28] plot_cnv_observation:Writing observation heatmap thresholds. INFO [2022-07-19 13:41:28] plot_cnv_observation:Done writing observation heatmap thresholds. INFO [2022-07-19 13:41:32] Colors for breaks: #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000 INFO [2022-07-19 13:41:32] Quantiles of plotted data range: 0.919170051016159,1,1,1,1.12623258696454 INFO [2022-07-19 13:41:34] plot_cnv_observations:Writing observation data to test_cnv/infercnv.outliers_removed.observations.txt INFO [2022-07-19 13:41:59] plot_cnv_references:Start INFO [2022-07-19 13:41:59] Reference data size: Cells= 2114 Genes= 9869 INFO [2022-07-19 13:44:49] plot_cnv_references:Number reference groups= 1 INFO [2022-07-19 13:44:49] plot_cnv_references:Plotting heatmap. INFO [2022-07-19 13:44:52] Colors for breaks: #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000 INFO [2022-07-19 13:44:52] Quantiles of plotted data range: 0.919170051016159,1,1,1,1.12623258696454 INFO [2022-07-19 13:44:53] plot_cnv_references:Writing reference data to test_cnv/infercnv.outliers_removed.references.txt 13:45:23 Summarizing cnv-results by chromosome. 13:45:25 Adding results to the spata-object's feature data. 13:45:25 Computing PCA based on cnv results. 13:45:32 Scaling data. 13:45:32 Done. 13:45:32 Done. Warning message: In addFeatures(object = object, feature_df = ordered_cnv_df2, overwrite = TRUE) : Only 2704 barcode-spots of 2706 were found in 'feature_df'. Not found barcode-spots obtain NAs for all features to be joined.

However the function getCnvResults(spata_obj) returns NULL

Good day to you

simonmfr commented 1 week ago

Hi @Dantferno, sorry for the long delay. In case this is still of interest, please retry with our updated SPATA2 v3 and the novel CNV analysis tutorial.