cSplotch is a hierarchical generative probabilistic model for analyzing Spatial Transcriptomics (ST) [1] data.
- Supports complex hierarchical experimental designs and model-based analysis of replicates
- Full Bayesian inference with Hamiltonian Monte Carlo (HMC) using the adaptive HMC sampler as implemented in Stan [2]
- Analysis of expression differences between anatomical regions and conditions using posterior samples
- Different anatomical annotated regions are modelled using a linear model
- Zero-inflated Poisson or Poisson likelihood for counts
- Conditional autoregressive (CAR) prior for spatial random effect
- Ability to deconvolve gene expression into cell type-specific signatures using compositional data gathered from histology images
- Use single-cell/single-nuclear expression data to calculate priors over expression in each cell type
We support the original ST array design (1007 spots, a diameter of 100 μm, and a center-to-center distance of 200 μm) by Spatial Transcriptomics AB, as well as Visium Spatial Gene Expression Solution by 10x Genomics, Inc., interfacing directly with file formats output by Spaceranger and Loupe Browser.
The cSplotch code in this repository supports single-, two-, and three-level experimental designs. These three different hierarchical models are illustrated below:
Tested on Python 3.5 and 3.7.
cSplotch has been tested on Mac and Linux. It has not been tested on Windows.
The following command installs the cSplotch Python module and compiles the Stan model in one go
$ pip install git+https://[email protected]/adaly/cSplotch.git
$ pip install --no-deps --force-reinstall --upgrade --install-option="--stan" git+https://[email protected]/adaly/cSplotch.gitFirst, the installation script downloads the latest version of CmdStan and compiles it. Second, the installation script uses the CmdStan installation to compile the Stan model.
As a result of this, the user will have the executables splotch, splotch_prepare_count_files, splotch_generate_input_files, and splotch_stan_model
$ splotch
usage: splotch [-h] -g GENE_IDX -d DATA_DIRECTORY -o OUTPUT_DIRECTORY -b BINARY -n NUM_SAMPLES -c NUM_CHAINS [-s]
splotch: error: argument -g is required
$ splotch_prepare_count_files
usage: splotch_prepare_count_files [-h] -c COUNT_FILES [COUNT_FILES ...]
[-s SUFFIX] [-d MINIMUM_DETECTION_RATE]
[-V/--Visium]
[-v]
splotch_prepare_count_files: error: the following arguments are required: -c/--count_files
$ splotch_generate_input_files
usage: splotch_generate_input_files [-h] -c COUNT_FILES [COUNT_FILES ...] -m
METADATA_FILE -s SCALING_FACTOR
[-l N_LEVELS]
[-d MINIMUM_SEQUENCING_DEPTH]
[-t MAXIMUM_NUMBER_OF_SPOTS_PER_TISSUE_SECTION]
[-n] [-z] [-o OUTPUT_DIRECTORY]
[-V] [-v]
[-p]
[-e SINGLE_CELL_ANNDATA] [-g GROUP_KEY] [-G GENE_NAMES]
splotch_generate_input_files: error: the following arguments are required: -c/--count_files, -m/--metadata_file, -s/--scaling_factor
$ splotch_stan_model
Usage: splotch_stan_model <arg1> <subarg1_1> ... <subarg1_m> ... <arg_n> <subarg_n_1> ... <subarg_n_m>
⋮
Failed to parse arguments, terminating StanFor splotch_prepare_count_files and splotch_generate_input_files, the inputs are assumed to be in the original ST format unless the -V/--Visium flag is passed. We will discuss the differences in input format in the subsequent sections.
The second installation method installs cSplotch and compiles the Stan model in separate steps.
The cSplotch Python module can be installed without compiling the Stan models as follows
$ pip install git+https://[email protected]/adaly/cSplotch.gitAs a result of this, the user will have the executables splotch, splotch_prepare_count_files, and splotch_generate_input_files
$ splotch
usage: splotch [-h] -g GENE_IDX -d DATA_DIRECTORY -o OUTPUT_DIRECTORY -b BINARY -n NUM_SAMPLES -c NUM_CHAINS [-s]
splotch: error: argument -g is required
$ splotch_prepare_count_files
usage: splotch_prepare_count_files [-h] -c COUNT_FILES [COUNT_FILES ...]
[-s SUFFIX] [-d MINIMUM_DETECTION_RATE]
[-V/--Visium]
[-v]
splotch_prepare_count_files: error: the following arguments are required: -c/--count_files
$ splotch_generate_input_files
usage: splotch_generate_input_files [-h] -c COUNT_FILES [COUNT_FILES ...] -m
METADATA_FILE -s SCALING_FACTOR
[-l N_LEVELS]
[-d MINIMUM_SEQUENCING_DEPTH]
[-t MAXIMUM_NUMBER_OF_SPOTS_PER_TISSUE_SECTION]
[-n] [-z] [-o OUTPUT_DIRECTORY]
[-V] [-v]
[-p]
[-e SINGLE_CELL_ANNDATA] [-g GROUP_KEY] [-G GENE_NAMES]
splotch_generate_input_files: error: the following arguments are required: -c/--count_files, -m/--metadata_file, -s/--scaling_factorFor splotch_prepare_count_files and splotch_generate_input_files, the inputs are assumed to be in the original ST format unless the -V/--Visium flag is passed. We will discuss the differences in input format in the subsequent sections.
CmdStan [2] can be installed as follows
$ STAN_VERSION=`curl -s https://api.github.com/repos/stan-dev/cmdstan/releases/latest | sed -n 's/.*"tag_name": "v\([^"]*\)",$/\1/p'`
$ cd $HOME
$ curl -LO https://github.com/stan-dev/cmdstan/releases/download/v"$STAN_VERSION"/cmdstan-"$STAN_VERSION".tar.gz
$ tar -xzvf cmdstan-"$STAN_VERSION".tar.gz
$ cd cmdstan-"$STAN_VERSION"
$ make build -j4This will install CmdStan in the directory $HOME/cmdstan-$STAN_VERSION.
The latest CmdStan user guide can be found at https://github.com/stan-dev/cmdstan/releases.
The cSplotch Stan models splotch_stan_model.stan and comp_splotch_stan_model.stan can be compiled using CmdStan as follows
$ cd $HOME
$ cd cmdstan-"$STAN_VERSION"
$ make $HOME/cSplotch/stan/splotch_stan_model
$ make $HOME/cSplotch/stan/comp_splotch_stan_model
--- Translating Stan model to C++ code ---
⋮Here we assume you have installed CmdStan in the directory $HOME/cmdstan-$STAN_VERSION and have the cSplotch code in the directory $HOME/cSplotch. Please change the paths if your environment differs.
After a successful compilation, you will have the binaries splotch_stan_model and comp_splotch_stan_model in the directory $HOME/cSplotch/stan
$ $HOME/cSplotch/stan/splotch_stan_model
$ $HOME/cSplotch/stan/comp_splotch_stan_model
Usage: [comp_]splotch_stan_model <arg1> <subarg1_1> ... <subarg1_m> ... <arg_n> <subarg_n_1> ... <subarg_n_m>
⋮
Failed to parse arguments, terminating StanThe main steps of cSplotch analysis are the following:
- Preparation of count files
splotch_prepare_count_files
- Annotation of ST spots
- Annotation of cell types
- Preparation of metadata table
- Preparation of input data files for cSplotch
splotch_generate_input_files
- cSplotch analysis
splotch
- Summarizing cSplotch output
splotch_compile_lambdassplotch_compile_betas
- Downstream analysis
Below we will describe these steps in detail.
In the directory examples, we have some example ST data [3]. We will use this example data set in this documentation to demonstrate the use of cSplotch.
The inputs to the count file preparation script differ depending on whether the user is supplying data from original ST or Visium ST. Both cases are outlined below.
The count files in the original ST workflow have the following tab-separated values (TSV) file format
| 32.06_2.04 | 31.16_2.04 | 14.07_2.1 | … | 28.16_33.01 | |
|---|---|---|---|---|---|
| A130010J15Rik | 0 | 0 | 0 | … | 0 |
| A230046K03Rik | 0 | 0 | 0 | … | 0 |
| A230050P20Rik | 0 | 0 | 0 | … | 0 |
| A2m | 0 | 1 | 0 | … | 0 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ |
| Zzz3 | 0 | 1 | 0 | … | 0 |
The rows and columns have gene identifiers and ST spot coordinates, respectively.
We have to make sure that the count files have the same indexing. This can be achieved using the provided Python script splotch_prepare_count_files
$ splotch_prepare_count_files --help
usage: splotch_prepare_count_files [-h] -c COUNT_DATA [COUNT_FILES ...]
[-s SUFFIX] [-d MINIMUM_DETECTION_RATE]
A script for preparing count files for cSplotch
optional arguments:
-h, --help show this help message and exit
-c COUNT_DATA [COUNT_FILES ...], --count_data [COUNT_FILES ...]
list of read count filenames
-s SUFFIX, --suffix SUFFIX
suffix to be added to the output filenames (default is .unified.tsv)
-d MINIMUM_DETECTION_RATE, --minimum_detection_rate MINIMUM_DETECTION_RATE
minimum detection rate (default is 0.02)When working with data from the Visium platform, count data are expected in the form of the output of spaceranger count, which produces a structured directory containing count and metadata information for each sample.
As with the original ST data, we must ensure that count files have the same indexing. This can still be achieved through the use of the splotch_prepare_count_files with the -V/--Visium flag set, which tells the script to expect paths to spaceranger output directories as input instead of stand-alone count files. The output of the script will be an additional Splotch-formatted count file created within the top level of each spaceranger output directory.
$ splotch_prepare_count_files --help
usage: splotch_prepare_count_files [-h] -c COUNT_DATA [SPACERANGER_COUNT_DIRS ...]
[-s SUFFIX] [-d MINIMUM_DETECTION_RATE]
[-V]
A script for preparing count files for cSplotch
optional arguments:
-h, --help show this help message and exit
-c COUNT_DATA [SPACERANGER_COUNT_DIRS ...], --count_data [SPACERANGER_COUNT_DIRS ...]
list of spaceranger count directories
-s SUFFIX, --suffix SUFFIX
suffix to be added to the sample name in creation of cSplotch count file within each spaceranger directory (default is .unified.tsv)
-d MINIMUM_DETECTION_RATE, --minimum_detection_rate MINIMUM_DETECTION_RATE
minimum detection rate (default is 0.02)
-V, --Visium data are from the Visium platformFor a sample with spaceranger output spaceranger_output/CN51_C2/, this script would create a file spaceranger_output/CN51_C2/CN51_C2.unified.tsv which would then serve as input to splotch_generate_input_files (discussed below).
For instance, the following command prepares the count files located in examples/Count_Tables [3]
$ splotch_prepare_count_files -c examples/Count_Tables/*_stdata_aligned_counts_IDs.txt
INFO:root:Reading 10 count files
INFO:root:We have detected 18509 genes
INFO:root:We keep 11313 genes after discarding the lowly expressed genes (detected in less than 2.00% of the ST spots)
INFO:root:The median sequencing depth across the ST spots is 2389To get the most out of the statistical model of cSplotch one has to annotate the ST spots based on their tissue context. These annotations will allow the model to share information across tissue sections, resulting in more robust conclusions.
To make the annotation step slightly less tedious in the original ST workflow, we have implemented a light-weight js tool called Span.
The annotation files have the following TSV file format
| 32.06_2.04 | 31.16_2.04 | 14.07_2.1 | … | 28.16_33.01 | |
|---|---|---|---|---|---|
| Vent_Med_White | 0 | 0 | 0 | … | 0 |
| Vent_Horn | 1 | 1 | 0 | … | 0 |
| Vent_Lat_White | 0 | 0 | 0 | … | 0 |
| Med_Grey | 0 | 0 | 0 | … | 0 |
| Dors_Horn | 0 | 0 | 0 | … | 0 |
| Dors_Edge | 0 | 0 | 0 | … | 1 |
| Med_Lat_White | 0 | 0 | 0 | … | 0 |
| Vent_Edge | 0 | 0 | 1 | … | 0 |
| Dors_Med_White | 0 | 0 | 0 | … | 0 |
| Cent_Can | 0 | 0 | 0 | … | 0 |
| Lat_Edge | 0 | 0 | 0 | … | 0 |
The rows and columns correspond to the user-define anatomical annotation regions (AAR) and ST spot coordinates, respectively. For instance, the spot 32.06_2.04 has the Vent_Horn annotation (i.e. located in ventral horn). The annotation category of each ST spot is one-hot encoded and we do not currently support more than one annotation category per ST spot.
10x Genomics have provided a tool for the exploration and annotation of Visium data called Loupe.
When working with Visium data, we expect annnotation files in the CSV format exported by the Loupe browser
| Barcode | Label |
|---|---|
| AAACACCAATAACTGC-1 | Vent_Med_White |
| AAACATGGTGAGAGGA-1 | Vent_Horn |
| AAACATTTCCCGGATT-1 | Lat_Edge |
| AAACCTAAGCAGCCGG-1 | Vent_Horn |
| AAACGAAGATGGAGTA-1 | Vent_Horn |
| AAACGAGACGGTTGAT-1 | Med_Lat_White |
| AAACGGGCGTACGGGT-1 | Vent_Horn |
The Barcode column specifies the spatial barcode associated with each spot in the Visium array (which can be associated with Visium array coordinates or pixel coordinates within the corresponding histology image using metadata in the spaceranger output directory) while the second column can be named according to any category type (Tissue, AAR, etc.) and contains a unique label for each spot.
ST spots without annotation categories are discarded by splotch_generate_input_files (please see Preparation of the input data files for Splotch). This behaviour can be useful when you want to discard some ST spots from the analysis based on the tissue histology.
Annotation files for the count files in examples/Counts_Tables are located in examples/Annotations [3].
In order to deconvolve the characteristic expression estimates (beta) into cell-type associated signatures, the user must provide additional annotation information on the cellular composition of each spot. For a defined number of cell types
Cellular composition data for each ST array should take the form of a (
| 32.06_2.04 | 31.16_2.04 | 14.07_2.1 | ... | 28.16_33.01 | |
|---|---|---|---|---|---|
| CellType1 | 0.0 | 0.15 | 0.0 | ... | 0.25 |
| CellType2 | 0.45 | 0.55 | 0.2 | ... | 0.25 |
| CellType3 | 0.55 | 0.3 | 0.8 | ... | 0.5 |
The metadata table contains information about the samples (i.e. count files). Additionally, the metadata table is used for matching count and annotation files.
The metadata table has the following TSV file format
| Name | Level 1 | Level 2 | Level 3 | Count file | Annotation file | Image file |
|---|---|---|---|---|---|---|
| L7CN36_C1 | G93A p120 | F | 1394 | examples/Count_Tables/L7CN36_C1_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/L7CN36_C1.tsv | examples/Images/L7CN36_C1_HE.jpg |
| L7CN36_C2 | G93A p120 | F | 1394 | examples/Count_Tables/L7CN36_C2_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/L7CN36_C2.tsv | examples/Images/L7CN36_C2_HE.jpg |
| L7CN30_C1 | WT p120 | M | 2967 | examples/Count_Tables/L7CN30_C1_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/L7CN30_C1.tsv | examples/Images/L7CN30_C1_HE.jpg |
| L7CN30_C2 | WT p120 | M | 2967 | examples/Count_Tables/L7CN30_C2_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/L7CN30_C2.tsv | examples/Images/L7CN30_C2_HE.jpg |
| L7CN69_D1 | WT p120 | M | 1310 | examples/Count_Tables/L7CN69_D1_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/L7CN69_D1.tsv | examples/Images/L7CN69_D1_HE.jpg |
| L7CN69_D2 | WT p120 | M | 1310 | examples/Count_Tables/L7CN69_D2_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/L7CN69_D2.tsv | examples/Images/L7CN69_D2_HE.jpg |
| CN96_E1 | WT p120 | F | 1040 | examples/Count_Tables/CN96_E1_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/CN96_E1.tsv | examples/Images/CN96_E1_HE.jpg |
| CN96_E2 | WT p120 | F | 1040 | examples/Count_Tables/CN96_E2_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/CN96_E2.tsv | examples/Images/CN96_E2_HE.jpg |
| CN93_E1 | G93A p120 | M | 975 | examples/Count_Tables/CN93_E1_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/CN93_E1.tsv | examples/Images/CN93_E1_HE.jpg |
| CN93_E2 | G93A p120 | M | 975 | examples/Count_Tables/CN93_E2_stdata_aligned_counts_IDs.txt.unified.tsv | examples/Annotations/CN93_E2.tsv | examples/Images/CN93_E2_HE.jpg |
Each sample (i.e. count file) has its own row in the metadata table. The columns Level 1, Count file, and Annotation file are mandatory. The column Level 2 is mandatory when using the two-level model. Whereas, the columns Level 2 and Level 3 are mandatory when using the three-level model. The columns Level 1, Level 2, and Level 3 define how the samples are analyzed using the linear multilevel model.
Visium data requires an additional column, "Spaceranger output", that points to the spaceranger output directory associated with each sample.
Deconvolutional analysis requires an additional column, "Composition file", that points to the cell type composition file associated with each sample.
The user can include additional columns at their own discretion. For instance, we will use the column Image file in Tutorial.ipynb.
The metadata table containing information about the provided example data set can be found at examples/metadata.tsv.
Stan can only read data in R dump format, in which variables are declared as scalars, sequences, or general arrays and defined in terms of their dimensionalities and values. Therefore, we have to make the ST data compatible with the data types in Stan (e.g. Stan does not have ragged array or dictionary data types).
To make this step less painful for the user, we have provided the script splotch_generate_input_files
$ splotch_generate_input_files --help
usage: splotch_generate_input_files [-h] -c COUNT_FILES [COUNT_FILES ...] -m
METADATA_FILE -s SCALING_FACTOR
[-l N_LEVELS]
[-d MINIMUM_SEQUENCING_DEPTH]
[-t MAXIMUM_NUMBER_OF_SPOTS_PER_TISSUE_SECTION]
[-n] [-z] [-o OUTPUT_DIRECTORY]
[-V] [-v] [-p] [-e SINGLE_CELL_ANNDATA]
[-g GROUP_KEY] [-G GENE_SYMBOLS]
A script for generating input data for cSplotch
optional arguments:
-h, --help show this help message and exit
-c COUNT_FILES [COUNT_FILES ...], --count_files COUNT_FILES [COUNT_FILES ...]
list of read count filenames (output from splotch_prepare_count_files)
-m METADATA_FILE, --metadata_file METADATA_FILE
metadata filename
-s SCALING_FACTOR, --scaling_factor SCALING_FACTOR
scaling_factor (e.g. median sequencing depth over
spots)
-l N_LEVELS, --n_levels N_LEVELS
number of levels in the linear model (default is 3)
-d MINIMUM_SEQUENCING_DEPTH, --min_depth MINIMUM_SEQUENCING_DEPTH
minimum number of UMIs per spot (default is 100)
-t MAXIMUM_NUMBER_OF_SPOTS_PER_TISSUE_SECTION, --t MAXIMUM_NUMBER_OF_SPOTS_PER_TISSUE_SECTION
number of spots threshold for identifying overlapping
tissue sections (default is 2000)
-n, --no-car disable the conditional autoregressive prior
-z, --no-zip use the Poisson likelihood instead of the zero-
inflated Poisson likelihood
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
output_directory (default is data)
-V, --Visium data are from the Visium platform (default is false)
-v, --version show program's version number and exit
-p, --compositional deconvolve expression into cell-type specific signatures using
compositional annotations
-e, --empirical_priors
use single-cell AnnData object (HDF5 format) to inform priors
over gene expression in each cell type (--compositional only)
-g, --sc-group-key key in single-cell AnnData.obs containing cell type annotations
(--compositional only; must match cell type naming in annotation files)
-G, --sc-gene-symbols key in single-cell AnnData.var corresponding to gene names in ST count data
(--compositional only; must match gene names in count files)The input files are saved in subdirectories (i.e. 100 files per subdirectory to limit the number of files per directory) in the directory specified using -o OUTPUT_DIRECTORY. There will be as many input files as there were genes in the prepared count files (please see Preparation of count files). Moreover, the input file indices match with the line numbers of the prepared count files (header line is not taken into account); i.e. the data of the gene on the 4th row is saved in the file OUTPUT_DIRECTORY/0/data_3.R.
Note that splotch_generate_input_files process the intersection of the count files provided using -c COUNT_FILES and the count files listed in the metadata table provided using -m METADATA_FILE . Therefore, the metadata table might cover additional samples.
Additionally, the file OUTPUT_DIRECTORY/information.p is created to help the interpretation of the results. The use of the file information.p is demonstrated in Tutorial.ipynb.
Note that the input data have to be generated differently for the single-, two-, and three-level models. To generate the data files for the single-, and two-, or three-level models please provide -l 1, -l 2, or -l 3, respectively. Moreover, with -l 1 we only consider the Level 1 column in metadata table, whereas with -l 2 we will consider the Level 1 and Level 2 columns. As expected, with -l 3 we will consider the Level 1, Level 2, and Level 3 columns.
The zero-inflated Poisson likelihood is the default but the Poisson likelihood can be enabled by providing -z.
The CAR prior can be disabled by providing -n.
As an example, we have provided Level 1, Level 2, and Level 3 metadata on the samples in examples/metadata.tsv and we wish to analyze the data using the three-level model with the CAR prior and zero-inflated Poisson likelihood . In this case, we can prepare the input data files for cSplotch as follows
$ splotch_generate_input_files -c examples/Count_Tables/*_stdata_aligned_counts_IDs.txt.unified.tsv -m examples/metadata.tsv -s 2389.0 -l 3 -o data_three
INFO:root:Reading metadata
INFO:root:Initializing
INFO:root:beta_level_1[1] := G93A p120
INFO:root:beta_level_2[1] := G93A p120 F
INFO:root:beta_level_3[1] := G93A p120 F 1394
INFO:root:beta_level_2[2] := G93A p120 M
INFO:root:beta_level_3[2] := G93A p120 M 975
INFO:root:beta_level_1[2] := WT p120
INFO:root:beta_level_2[3] := WT p120 M
INFO:root:beta_level_3[3] := WT p120 M 2967
INFO:root:beta_level_3[4] := WT p120 M 1310
INFO:root:beta_level_2[4] := WT p120 F
INFO:root:beta_level_3[5] := WT p120 F 1040
⋮
INFO:root:Saving additional information (data_three/information.p)
INFO:root:FinishedThe input data files are saved in the directory data_three.
Please make sure you have compiled the cSplotch models before continuing with this section.
The provided Bash script splotch is a wrapper for running cSplotch
$ splotch -h
usage: splotch [-h] -g GENE_IDX -d DATA_DIRECTORY -o OUTPUT_DIRECTORY
-b BINARY -n NUM_SAMPLES -c NUM_CHAINS [-s]
A wrapper for running cSplotch
optional arguments:
-h show this help message and exit
-g gene index
-d data directory
-o output directory
-b cSplotch binary
-n number of samples
-c number of chains
-s store summary statistics instead of full posteriors for all variables besides betas (much more memory-efficient!)The wrapper will start separate processes for independent MCMC chains, and it will concatenate the samples from the HMC chains.
Note that splotch forks a process for each requested chain (-c NUM_CHAINS).
Moreover, splotch follows the same subdirectory scheme (in -c OUTPUT_DIRECTORY) as splotch_generate_input_files.
The use of parallel computing to speed up the computation is trivial because cSplotch is embarrassingly parallel across genes.
As an example, let us assume that we want to analyze Gfap; to find the corresponding index we can run the following cryptic command
GENE="Gfap"; echo $(($(grep -wn "$GENE" $(cut -f$(head -n1 examples/metadata.tsv | tr "\t" "\n" | grep -n "Count file"| cut -d: -f1) examples/metadata.tsv | sed -n '2p')|cut -f1 | cut -d: -f1)-1))
3661The command is a bit complicated because we first have to find the index of the column Count file in examples/metadata.tsv to find a relevant count file. Whereas, if you are willing to supply the name of a count file, then the following command is sufficient
$ GENE="Gfap"; COUNT_FILE="examples/Count_Tables/CN93_E1_stdata_aligned_counts_IDs.txt.unified.tsv"; echo $(($(grep -wn "$GENE" "$COUNT_FILE" |cut -d: -f1)-1))
3661The 3661st (not taking into account the header line) line of the prepared count files (please see Preparation of count files) corresponds to Gfap.
In the previous section, we ran splotch_generate_input_files with the arguments -l 3 -o data_three. Thus, we can analyze the example ST data on Gfap as follows
$ splotch -g 3661 -n 200 -c 4 -b splotch_stan_model -d data_three -o output_three
method = sample (Default)
sample
num_samples = 200
num_warmup = 200
save_warmup = 0 (Default)
thin = 1 (Default)
adapt
⋮
Elapsed Time: 89.3442 seconds (Warm-up)
26.6335 seconds (Sampling)
115.978 seconds (Total)The output is saved in the file output_three/36/combined_3661.csv.
Because the output from cSplotch can be quite large, depending on the number of genes and spots in the dataset, we provide two command-line tools for summarizing the output in HDF5-formatted AnnData files:
$ splotch_compile_lambdas -h
usage: splotch_compile_lambdas [-h] -m META_FILE -i INPUT_DIR -o OUTPUT_DIR -d DEST_FILE
Summarizes all spot-level variables, including raw counts, predicted cellular composition, and inferred expression rates (lambda).
arguments:
-h show this help message and exit
-m path to cSplotch metadata file
-i top-level directory containing csplotch input files
-o top-level directory containing SUMMARIZED (combined_*.hdf5, generated with -s option to splotch) csplotch output files
-d destination file (.hdf5)$ splotch_compile_betas -h
usage: splotch_compile_betas [-h] -i INPUT_DIR -o OUTPUT_DIR -d DEST_FILE -l LEVEL
Summarizes MROI- and cell-specific variables, namely mean and standard deviation about characteristic expression rates (beta) in each MROI and cell type for each unique condition at the specified model level.
arguments:
-h show this help message and exit
-i top-level directory containing csplotch input files
-o top-level directory containing SUMMARIZED (combined_*.hdf5, generated with -s option to splotch) csplotch output files
-d destination file (.hdf5)
-l model level (1, 2 or 3) at which to output summary statistics of beta.Please see Tutorial.ipynb, as well as the other provided Jupyter notebooks.
[1] Ståhl, Patrik L., et al. "Visualization and analysis of gene expression in tissue sections by spatial transcriptomics." Science 353.6294 (2016): 78-82.
[2] Carpenter, Bob, et al. "Stan: A probabilistic programming language." Journal of Statistical Software 76(1) (2017).
[3] Maniatis, Silas, et al. "Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis." Science 364.6435 (2019): 89-93.