Wohnregal Apartments and Ateliers / FAR frohn&rojas
- Curated by Paula Pintos
- about 8 hours
Apartments · Berlin, Germany
- version="1.0" encoding="UTF-8"? Architects: FAR frohn&rojas
- AreaArea of this architecture projectArea: 1040.0 m2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2019
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: David von Becker
- Design Team Marc Frohn, Mario Rojas Toledo, Max Koch, Ulrike vandenBerghe, Lisa Behringer, Ruth Meigen, Martin Gjoleka, Felix Schöllhorn, Pan Hu, Julius Grün, Erik Tsurumaki, Katharina Wiedwald
- Engineering IB Paasche
More Specs Less Specs
Text description provided by the architects. The “Wohnregal” is a 6-story building housing life/work ateliers. It was built using pre-cast concrete elements common in industrial warehouse construction. Underlying the choice for this serial construction technique was the ambition to bridge two apparently contradictory challenges the housing market in Berlin is facing.
On the one hand, industrial prefabrication offers the benefits of serial construction techniques such as cost-savings and shorter construction timelines thus addressing the rising construction cost for housing. Countering preconceptions that serial construction automatically implies a standardization of the inhabitable unit itself, the “Wohnregal” at the same time offers a maximum range of different life/work ateliers for an ever-broadening bandwidth of urban lifestyles.
The pre-cast concrete structure with its long-spanning TT-ceilings facilitates such variety as it offers a clear span from façade to façade without any structural walls needed on the interior.
A curtain wall consisting of large-scale standard sliding doors is used as the façade. It allows for the interior to be opened up to its surroundings during the summer months turning the living space as a whole into a loggia. At the same time this curtain wall offers a maximum presence of the gridded pre-cast concrete structure both to the inside as well as to the surrounding.


Below charts show data collected at the L1 libration point (auto updated every minute) between the Earth and the Sun (compliments of NOAA SWPC's ACE & DSCOVR satellites), about 1,500,000km from Earth - which gives us up to 1 hour advance warning to get to a good spot once a storm hits. more info
The DSCOVR is America’s first operational deep space satellite, which orbits one million miles from Earth. Positioned between the sun and Earth at Lagrange point 1, it is able to maintain a constant view of the sun and sun-lit side of Earth - which helps to increase the accuracy of predicting the likelihood of an Aurora.
This improved accuracy is very important - because predicting an Aurora on Earth with any confidence is extremely difficult, due the high number of impacting variables - most of which are hard to quantify, or predict with accuracy (many experienced 'Aurora Chasers' might call it an art).
That is why this tool is geared towards making conservative forecasts with higher confidence, instead of longer range predictions based on historically unreliable data.
Yep. Life is hard as an Aurora Chaser, and so are long-range weather forecasts.
This tool was mostly just built so we wouldn't have to get up at intervals through
the night to check the data - we can just set an alarm and have the system send us an alert
when the storm hits L1.
For long-range predictions, the Aurora Australis Forecaster is can be used in a three-step approach:


CORRECTOR Creates a corrector element in old a AT version (Obsolete) CORRECTOR('FAMILYNAME',LENGTH,ANGLE,'METHOD') creates a new family in the FAMLIST - a structure with fields FamName family name Length is set to 0 for marker type KickAngle [kickx, kicky] in radians (small) - unis of d(x,y)/ds PassMethod name of the function on disk to use for tracking returns assigned index in the FAMLIST that is uniquely identifies the family NOTES 1. Obsolete: use atcorrector instead See also atdrift, atquadrupole, atsextupole, atsbend, atskewquad, atmultipole, atthinmultipole, atmarker, atcorrector
0001 function z = corrector(fname,L,kickangle, method) 0002 %CORRECTOR Creates a corrector element in old a AT version (Obsolete) 0003 %CORRECTOR('FAMILYNAME',LENGTH,ANGLE,'METHOD') 0004 % creates a new family in the FAMLIST - a structure with fields 0005 % FamName family name 0006 % Length is set to 0 for marker type 0007 % KickAngle [kickx, kicky] in radians (small) - unis of d(x,y)/ds 0008 % PassMethod name of the function on disk to use for tracking 0009 % 0010 % returns assigned index in the FAMLIST that is uniquely identifies 0011 % the family 0012 % 0013 % NOTES 0014 % 1. Obsolete: use atcorrector instead 0015 % 0016 % See also atdrift, atquadrupole, atsextupole, atsbend, atskewquad, 0017 % atmultipole, atthinmultipole, atmarker, atcorrector 0018 0019 ElemData.FamName = fname; % add check for identical family names 0020 ElemData.Length = L; 0021 if length(kickangle) == 2 0022 ElemData.KickAngle = reshape(kickangle,1,2); 0023 else 0024 ElemData.KickAngle = [0, 0]; 0025 warning(['Family: ',fname,' - KickAngle is not a 2-vector. Set both components to 0']); 0026 end 0027 0028 ElemData.PassMethod = method; 0029 0030 ElemData.PolynomA=[kickangle(2)]; 0031 ElemData.PolynomB=[kickangle(1)]; 0032 ElemData.NumIntSteps=1; 0033 ElemData.MaxOrder=1; 0034 0035 global FAMLIST 0036 z = length(FAMLIST)+1; % number of declared families including this one 0037 FAMLIST{z}.FamName = fname; 0038 FAMLIST{z}.NumKids = 0; 0039 FAMLIST{z}.KidsList= []; 0040 FAMLIST{z}.ElemData= ElemData; 0041


[1]
when on walks can be desperate to grab the leather lead to play tug of war .
[2]
dog jumped up trying to grab the lead & she was bitten badly on the arm .


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{% extends "base-structure.html" %} {% block title %} Homepage {% endblock %} {% block head%} {{super()}} {% endblock %} {% block page_content %}
This is my resume website. It was designed using a combination of Flask, Flask Script, Jinja, Bootstrap, Fontawesome, and jQuery. Navigate
to the Courses link in order to see some of the courses I've partaken in and click the About link to get a bit of background information on me.


Bayesian Framework¶
Given data, \({\boldsymbol{y}}\), in the form of time-series of daily counts and the model predictions \({\boldsymbol{n}}\) for the number of new symptomatic counts daily for the same time range, we employ a Bayesian framework to calibrate the epidemiological model parameters. The discrepancy between the data and the model is written as
Here, \(\Theta\) is a vector of model parameters, and \({\epsilon}\) represents the statistical discrepancy between the model and the data. The elements of \(\Theta\) depend on the number of epidemic waves being modeled.
where \(\Theta^{(j)}\) are the parameter for the j-th wave of infections, K is the number of waves and \(\Theta^{(\epsilon)}\) are parameters for the error model. The parameters for each wave are given by
For the first epidemic wave \(\Delta t_1=t_0\).
The error model encapsulates, in this context, both errors in the observations as well as errors due to imperfect modeling choices. The observation errors include variations due to testing capabilities as well as errors when tests are interpreted. Values for the vector of parameters \(\Theta\) is estimated in the form of a multivariate PDF via Bayes theorem
where \(p(\Theta\vert\boldsymbol{y})\) is the posterior distribution we are seeking after observing the data \({\boldsymbol{y}}\), \(p(\boldsymbol{y}\vert\Theta)\) is the likelihood of observing the data \({\boldsymbol{y}}\) for a particular set of values for the model parameters \(\Theta\), and \(p(\Theta)\) encapsulates any prior information available for the model parameters. Bayesian methods are well-suited for dealing with heterogeneous sources of uncertainty, in this case from our modeling assumptions, i.e. model and parametric uncertainties, as well as the communicated daily counts of COVID-19 new cases, i.e. experimental errors.
Likelihood Construction¶
The library provides options for both deterministic and stochastic formulations for the incubation model. In the former case the mean and standard deviation of the incubation model are fixed at their nominal values and the model prediction \(n_i\) for day \(t_i\) is a scalar value that depends on $Theta$ only. In the latter case, the incubation model is stochastic with mean and standard deviation of its natural logarithm treated as Student’s t and $chi^2$ random variables, respectively, as discussed in Incubation Rate Model Section. Let us denote the underlying independent random variables by \({\boldsymbol \xi}=\{\xi_\mu,\xi_\sigma,\}\). The model prediction \(n_j({\boldsymbol{\xi}})\) for day j is now a random variable induced by ${bm xi}$ plugged into Single Wave Model or Multi-Wave Model and \({\boldsymbol n}({\boldsymbol\xi})\) is a random vector.
Deterministic Incubation Model¶
PRIME provides options for both Gaussian and negative binomial formulations for the statistical discrepancy \(\epsilon\) between \({\boldsymbol n}\) and \({\boldsymbol y}\). In the first approach we assume \(\epsilon\) has a zero-mean Multivariate Normal (MVN) distribution. Given the sparsity of data, correlations across time are currently neglected and the liklihood \(p({\boldsymbol y}\vert\Theta)\) is computed as
with
The additive, \(\sigma_a\), and multiplicative, \(\sigma_a\), components of the error model \(\Theta^{(\epsilon)}=\{\sigma_a,\sigma_a\}\) will be inferred jointly with the model parameters. In practive, PRIME infers the logarithm of these parameters to ensure they remain positive.
The second approach assumes a negative-binomial distribution for the discrepancy between data and model predictions. The negative-binomial distribution is commonly used in epidemiology to model overly dispersed data, e.g. in case where the standard deviation exceeds the mean [Lloyd2007]. For this modeling choice, the likelihood of observing the data given a choice for the model parameters is given by
where \(\alpha>0\) is the dispersion parameter, and
is the binomial coefficient. For simulations employing a negative binomial distribution of discrepancies, the logarithm of the dispersion parameter \(\alpha\) will be inferred jointly with the other model parameters.
Stochastic Incubation Model¶
For the {it stochastic incubation model} the likelihood reads as
The second expression in the right-hand side above assumes independence of the discrepancies between different days. Unlike the deterministic incubation model, the likelihood components for each day \(\pi_{n_i(\Theta),{\boldsymbol\xi}}(y_i)\) are not analytically tractable anymore since they now incorporate contributions from \({\boldsymbol \xi}\), i.e. from the variability of the parameters of the incubation model. One can evaluate the likelihood via kernel density estimation (KDE) by sampling \({\boldsymbol \xi}\) for each sample of \(\Theta\), and combining these samples with samples of the assumed discrepancy \(\epsilon\), in order to arrive at an estimate of \(\pi_{n_i(\Theta),{\boldsymbol\xi}}(y_i)\). In fact, by sampling a single value of \({\boldsymbol \xi}\) for each sample of \(\Theta\), one achieves an unbiased estimate of the likelihood \(\pi_{n_i(\Theta),{\boldsymbol\xi}}(y_i)\), and given the independent-component assumption, it also leads to an unbiased estimate of the full likelihood \(\pi_{{\boldsymbol n}(\Theta),{\boldsymbol\xi}}({\boldsymbol y})\).
Posterior Distribution Sampling¶
A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior density \(p(\Theta\vert\boldsymbol{y})\). MCMC is a class of techniques that allows sampling from a posterior distribution by constructing a Markov Chain that has the posterior as its stationary distribution. In particular, PRIME uses a an adaptive Metropolis algorithm [Haario2001]. Given the construction corresponding to the stochastic incubation model presented above, we employ the unbiased estimate of the approximate likelihood. This is the essence of the pseudo-marginal MCMC algorithm [Andrieu2009] guaranteeing that the accepted MCMC samples correspond to the posterior distribution. At each MCMC step we draw a random sample \(\xi\) from its distribution, and then we estimate the likelihood in a way similar to the deterministic incubation model.
Fig. 4 displays 1D and 2D joint marginal distributions based on two-wave model results. We use the Raftery-Lewis diagnostic [Raftery1992] to determine the number of MCMC samples required for converged statistics corresponding to stationary posterior distributions for \(\Theta\). The required number of samples is of the order \(o(10^5-10^6)\) depending on the geographical region employed in the inference. The resulting Effective Sample Size [Kass1998] varies between 8,000 and 15,000 samples depending on each parameter which is sufficient to estimate joint distributions for the model parameters.
Posterior Predictive Tests¶
We will employ Bayesian posterior-predictive distributions [Lynch2004] to assess the predictive skill of the statistical model. The Bayesian posterior-predictive distribution, defined below is computed by marginalization of the likelihood over the posterior distribution of model parameters \(\Theta\):
The posterior predictive distribution \(p_{\mathrm{pp}}\left(\boldsymbol{y}^{\mathrm{(pp)}}\vert\boldsymbol{y}\right)\) is estimated through sampling, using the parameter samples readily available from the MCMC exploration of the parameter space, i.e. similar to results shown in Fig.~ref{fig:mcmc}. Typically we subsample the MCMC chain to about 10-15K samples that will be used to generate posterior predictive statistics. After the model evaluations \(\boldsymbol{y}=\boldsymbol{n}(\Theta)\) are completed, we add random noise consistent with the likelihood model settings presented in ref{sec:lk}. The resulting samples are used to compute summary statistics corresponding to \(p_{\mathrm{pp}}\left(\boldsymbol{y}^{\mathrm{(pp)}}\vert\boldsymbol{y}\right)\).
The posterior-predictive distribution results can be used in hindcast mode, to check how well the model follows the data, and for short-term forecasts for the spread dynamics of this disease. In the hindcast regime, the infection rate is convolved with the incubation rate model to generate statistics for \(\boldsymbol{y}^{\mathrm{(pp)}}\) that will be compared against \(\boldsymbol{y}\), the data used to infer the model parameters. The same functional form can be used to generate statistics for \(\boldsymbol{y}^{\mathrm{(pp)}}\) beyond the set of dates for which data was available. We limit these forecasts to 7–10 days as our infection rate model does not count for changes in social dynamics that can significantly impact the epidemic over a longer timerange.
Model Selection¶
Quantitative comparisons between models can be made with several metrics defined in the following sections.
AIC¶
The Akaike Information Criteria (AIC) [Akaike1974] is defined as
where \(m_{\Theta}\) is the number of parameters in \(\Theta\) and \(L_{max}\) is the maximum value of the likelihood \(p(\boldsymbol{y}\vert\Theta)\). This is estimated by the maximum likelihood in the MCMC chain. Given a choice of models, the model with the smallest AIC value is considered to be the highest quality model.
BIC¶
The Bayesian Information Criteria (BIC) [Schwarz1978] is defined as
where \(d\) is the number of observations, equal to the length of the array \(\boldsymbol{n}\). Given a choice of models, the model with the smallest BIC value is considered to be the highest quality model.
CRPS¶
The Continuous Ranked Probability Score (CRPS) [Gneiting2007] measures the difference between the CDF of the provided data and that of the forecast/predicted data, i.e., data generated based on the posterior predictive distribution. It is computed by summing up marginal distributions for each day that data is available
where \(y^{(pp)}_j \equiv y^{(pp)}(t_j)\) is new daily case predictions on day \(j\) obtained via the posterior-predictive distribution, \(y_j \equiv y(t_j)\) is new daily case data on day \(j\) and \(\mathcal{F}_{pp,j}\) is the 1-D marginal posterior predictive CDF for day \(j\) computed using 1-D marginal posterior predictive distributions
where
is the marginal 1-D posterior predictive density corresponding to day \(j\), based on \(p_{pp}\left( \boldsymbol{y}^{(pp)} | \boldsymbol{y} \right)\). Here, \(d\boldsymbol{y}^{(pp)}_{\sim j} \equiv dy^{(pp)}_1 \cdots dy^{(pp)}_{j-1} dy^{(pp)}_{j+1} \cdots dy^{(pp)}_d\). The CDF of the provided case data \(\boldsymbol{y}\) is approximated as a Heaviside function centered at \(y_j\), \(\mathcal{H}_{y_j}(y^{(pp)}_j) = 1_{y^{(pp)}_j \ge y_j}\). Like AIC and BIC, the model with the smallest value of CRPS is considered to be of higher quality than other models.
- Akaike1974
Akaike, H., A new look at the statistical model identification, Annals of Statistics (2009)
- Andrieu2009
- Gneiting2007
Gneiting T., Raftery A., Strictly Proper Scoring Rules, Prediction, and Estimation (2007)
- Haario2001
Haario H., Saksman E., Tamminen J., An adaptive Metropolis algorithm, Bernoulli (2001)
- Kass1998
- Lloyd2007
- Lynch2004
- Raftery1992
Raftery A.E., Lewis S., How Many Iterations in the Gibbs Sampler?, Bayesian Statistics 4 (1992)
- Schwarz1978
Schwarz G., Estimating the Dimension of a Model, Bayesian Statistics 4 (1992)


After graduating in the spring, I worked at 360Insights over the summer. The work was good and all, but the best part about summer work is that I have the time in the evening to go out and film.
I bought a Sony VX1000 at the start of the summer. This camera and the MK1 fisheye lense with it creates the best camera for skateboarding that ever existed. The colours are gorgeous. The sound is so crisp that a lot of skaters can recognize the camera used in footage without seeing any visuals. The MK1 fisheye is perfect for skateboarding. Because the VX1000 is so cheap, I can find replacement parts very easy too. I also started to experiment with Super 8 cameras in some of the later footage. The personality shots compliment the skating so well and give a warm feel to the montages. I have plans to start using Hi8 camera footage too next summer. Hi8 tapes are a lot cheaper than Super 8 film and a ton cheaper to process.
Skateboarding and filming go hand in hand for me. I cannot have one without the other. The footage I got with my friends was some of the best footage I've ever got. I loved travelling around Ontario during the trips and finding obscure skate spots that a lot of people in Oshawa have never seen before. I made great friends that I will hopefully continue to film with next summer.


vignettes/articles/cmd_qc.Rmd
cmd_qc.Rmd
This pipeline will import data from single-cell preprocessing algorithms (e.g. CellRanger), generate various quality control metrics (e.g. doublet scores), and output results in standard data containers (e.g. SingleCellExperiment). Both the original droplet matrix and the filtered cell matrix will be processed. This pipeline is focused on single cell data generated from microfluidic devices (e.g. 10X).
If you are using docker image to run the pipeline, please skip this section and refer sections Running the pipeline and Docker and Singularity Images. Docker image has all dependencies installed properly so that you can use it directly on any machine.
This pipeline depends on Python package described below. If these packages is not installed or not at the proper version, please install them by running the following code:
--user h5py==2.9.0
pip3 install --user Scrublet
pip3 install --user virtualenv
pip3 install --user scanpy pip3 install
The script will automatically try to install the “singleCellTK” package from Bioconductor if available.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("singleCellTK")
For each tool, the pipeline expect the data in a certain format within a specific directory structure. For example, data generated with CellRanger V3 is expected to be under a sample folder (specified with the “-s” flag) and a base folder (specified with the “-b” flag). Some tools prepend the sample name onto the output files instead of making a separate subdirectory (e.g. BUStools and SEQC). The combination of –base_path (“-b”) and –sample (“-s”) should specify the location of the data files: For a comprehensive description of expected input directory structure of import functions, refer documentation here
The required arguments are as follows:
-b, --basePath (required). Base path for the output from the preprocessing algorithm
-P, --preproc (required). Algorithm used for preprocessing. One of One of 'CellRangerV2', 'CellRangerV3', 'BUStools', 'STARSolo', 'SEQC', 'Optimus', 'DropEst', 'SceRDS', 'CountMatrix' and 'AnnData'.
-s, --sample (required). Name of the sample. This will be prepended to the cell barcodes.
-o, --directory (required). Output directory. A new subdirectory will be created with the name "sample". R, Python, and FlatFile directories will be created under the "sample" directory containing the data containers with QC metrics. Default ".". More information about output directory structure is explained in "Understanding outputs" section below.
-F, --outputFormat (required). The output format of this QC pipeline. Currently, it supports R (RDS), FlatFile, Python (AnnData) and HTAN (manifest files that meets HTAN requirement).
-S, --splitSample (required). Save a SingleCellExperiment object for each sample. Default is TRUE. If FALSE, the data of all samples will be combined into one SingleCellExperiment object and this object will be outputed.
The optional arguments are as follows. Their usage depend on type of data and user-defined behaviour.
-g, --gmt (optional). GMT file containing gene sets for quality control.
-t, --delim (optional, required when -g is specified). Delimiter used in GMT file. Default "\t".
-G, --genome (optional). The name of genome reference. This is only required for CellRangerV2 data.
-y, --yaml (optional). YAML file used to specify parameters of QC functions called by singleCellTK QC pipeline. Please check "Specify parameters for QC algorithms" section for details.
-c, --cellData (optional). The full path of the RDS file or Matrix file of the cell matrix. This would be use only when --preproc is SceRDS or CountMatrix.
-r, --rawData (optional). The full path of the RDS file or Matrix file of the droplet matrix. This would be provided only when --preproc is SceRDS or CountMatrix.
-C, --cellPath (optional). The directory contains matrix.mtx.gz, features.tsv.gz and barcodes.tsv.gz files originally generated by 10x CellrangerV2 or CellrangerV3 (files in the filtered_feature_bc_matrix directory). This argument only works when --preproc is CellRangerV2 or CellRangerV3. Default is NULL. If 'base_path' is NULL, 'cellPath' or 'rawPath' should be specified.
-R, --rawPath (optional). The directory contains matrix.mtx.gz, features.tsv.gz and barcodes.tsv.gz files originally generated by 10x CellrangerV2 or CellrangerV3 (files in the raw_feature_bc_matrix directory). This argument only works when --preproc is CellRangerV2 or CellRangerV3. Default is NULL. If 'base_path' is NULL, 'cellPath' or 'rawPath' should be specified.
-d, --dataType. Type of data as input. Default is Both, which means taking both droplet and cell matrix as input. If set as 'Droplet', it will only processes droplet data. If set as 'Cell', it will only processes cell data.
-D, --detectCells. Detect cells from droplet matrix. Default is FALSE. This argument is only eavluated when -d is 'Droplet'. If set as TRUE, cells will be detected and cell matrixed will be subset from the droplet matrix. Also, quality control will be performed on the detected cell matrix.
-m, --cellDetectMethod. Methods to detect cells from droplet matrix. Default is 'EmptyDrops'. This argument is only eavluated when -D is 'TRUE'. Other options could be 'Knee' or 'Inflection'. More information is provided in the section below.
-n, --numCores. Number of cores used to run the pipeline. By default is 1. Parallel computing is enabled if -n is greater than 1.
-T, --parallelType. Type of parallel computing used for parallel computing. Parallel computing used in this pipeline depends on "BiocParallel" package. Default is 'MulticoreParam'. It can be 'MulticoreParam' or 'SnowParam'. This argument will be evaluated only when numCores > 1.
-i, --studyDesign. The txt file containing the desrciption of the study design. Default is NULL. This would be shown at the begining the html report of cell and droplet QC.
-L, --subTitle. The subtitle used in the cell and droplet QC HTML report. Default is None. The subtitle can contain information of the sample, like sample name, etc. If -S is set as TRUE, the length of subsitle should be the same as the length of samples. If -S is set as FALSE, the length of subtitle should be one or NULL.
To run the pipeline script, users will need to download the ‘SCTK_runQC.R’ here.
This pipeline enables different ways to import CellrangerV2/CellrangerV3 data for flexibility.
For CellRangerV3:
Rscript SCTK_runQC.R \-b /base/path \
-P CellRangerV3 \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F R,Python,FlatFile,HTAN
For CellRangerV2, the reference used by cellranger needs to be specified by -G/–genome:
Rscript SCTK_runQC.R \-b /base/path \
-P CellRangerV2 \
-s SampleName \
-o Output_Directory \
-S TRUE \
-G hg19 \
-F R,Python,FlatFile,HTAN
As shown in the Import data into SCTK section, -b specify the base path and usually it’s the folder where you ran 10x cellranger count. -s specify the sample name, which has to be same as the name of the sample folder under the base folder. The folder layout would look like the following:
├── BasePath
└── SampleName
├── outs
| ├── filtered_feature_bc_matrix
| | ├── barcodes.tsv.gz
| | ├── features.tsv.gz
| | └── matrix.mtx.gz
| ├── raw_feature_bc_matrix
| | ├── barcodes.tsv.gz
| | ├── features.tsv.gz
| | └── matrix.mtx.gz
...
Rscript SCTK_runQC.R \-P CellRangerV2 \
-C /path/to/cell/matrix \
-R /path/to/droplet/matrix \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F R,Python,FlatFile,HTAN
In this case, you must skip -b arguments and you can also skip -G argument for CellRangerV2 data.
 Basically, the templated is shown below:
Rscript SCTK_runQC.R \-b /base/path \
-P Preprocessing_Algorithm \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F R,Python,FlatFile,HTAN
User can choose to run QC pipeline on only one of the droplet count or cell count matrix, instead of running on both. In this case, the pipeline will only take single input and perform quality control on it. One of the example is shown below:
Rscript SCTK_runQC.R \-b /base/path \
-P Preprocessing_Algorithm \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F R,Python,FlatFile,HTAN \
-d Droplet \
-D TRUE \
-m EmptyDrops
-d argument is used to specify which count matrix to used in the QC pipeline. Default is “Both”, which means take both droplet and cell count data as input.
If -d argument is set as “Droplet”, the QC pipeline will only take droplet count matrix as input and perform quality control. You can choose whether to detect cells from the droplet matrix by setting -D as TRUE. If yes, cell count matrix will be detected and the pipeline will also perform quality control on this matrix and output the result. You could further define the method used to detect cells from droplet matrix by setting -m argument. -m could be one of “EmptyDrops”, “Knee” or “Inflection”. “EmptyDrops” will keep cells that pass the “runEmptyDrops” function test. “Knee” and “Inflection” will keep cells that pass the knee or inflection point returned from “runBarcodeRankDrops” function.
If -d argument is set as “Cell”, the QC pipeline will only take cell count matrix as input and perform quality control. A figure showing the analysis steps and outputs of different inputs is shown below:
User can specify parameters for QC algorithms in this pipeline with a yaml file (supplied with -y/–yamlFile argument). The current supported QC algorithms including doublet dection (bcds, cxds, cxds_bcds_hybrid, doubletFinder, doubletCells and scrublet), decontamination (decontX), emptyDrop detection (emptyDrops) and barcodeRankDrops (barcodeRanks). A summary of each function is shown below:
An example of QC parameters yaml file is shown below:
---
Params: ### should not be omitted
bcds:
ntop: 600
cxds:
ntop: 600
cxds_bcds_hybrid:
nTop: 600
decontX:
maxIter: 600
emptyDrops:
lower: 50
niters: 5000
testAmbient: True
barcodeRanks:
lower: 50
The format of yaml file can be found here. The parameters should be consistent with the parameters of each QC function in singleCellTK package. Parameters that are not defined in this yaml file will use the default value. Please refer reference for detailed information about arguments in each QC function.
Quantifying the level of gene sets can be useful quality control. For example, the percentage of counts from mitochondrial genes can be an indicator or cell stress or death.
Users can pass a GMT file (with -g/–gmt argument) to the pipeline with one row for each gene set. The first column should be the name of the gene set (e.g. mito). The second column for each gene set in the GMT file (i.e. the description) should contain the location of where to look for the matching IDs in the data. If set to ‘rownames’, then the gene set IDs will be matched with the row IDs of the data matrix. If a character string or an integer index is supplied, then gene set IDs will be matched to IDs the that column of feature table.
SCTK QC pipeline enables parallel computing to speed up the analysis. Parallel computing is enabled by setting -n/–numCores > 1. The -n/–numCores is used to set the number of cores used for the pipeline.
The backend of parallel computing is supported by “BiocParallel” package. Therefore, users can select different types of parallel evaluation by setting -T/–parallelType argument. Default is “MulticoreParam”. Currently, “MulticoreParam” and “SnowParam” is supported for -T argument. However, “MulticoreParam” is not supported by Windows system. Winows user can choose “SnowParam” as the backend of parallel computing.
The output directory is created under the path specified by -o/–directory argument. Each sample is stoed in the subdirectory (named by -s/–sample argument) within this output direcotry. Within each sample directory, the each output format will be separated into subdirectories. The output file hierarchy is shown below:
(root; output directory)
├── level3Meta.csv
├── level4Meta.csv
├── sample1_dropletQC.html
├── sample1_cellQC.html
└── sample1
├── R
| ├── sample1_Droplets.rds
| └── sample1_Cells.rds
├── Python
| ├── Droplets
| | └── sample1.h5ad
| └── Cells
| └── sample1.h5ad
├── FlatFile
| ├── Droplets
| | ├── assays
| | | └── sample1_counts.mtx.gz
| | ├── metadata
| | | └── sample1_metadata.rds
| | ├── sample1_colData.txt.gz
| | └── sample1_rowData.txt.gz
| └── Cells
| ├── assays
| | └── sample1_counts.mtx.gz
| ├── metadata
| | └── sample1_metadata.rds
| ├── reducedDims
| | ├──sample1_decontX_UMAP.txt.gz
| | ├──sample1_scrublet_TSNE.txt.gz
| | └──sample1_scrublet_UMAP.txt.gz
| ├── sample1_colData.txt.gz
| └── sample1_rowData.txt.gz
└── sample1_QCparameters.yaml
singleCellTK is available to use with both Docker and Singularity. This container be used as a portal to the singleCellTK command line interface. The work was done in R. The singleCellTK docker image is available from Docker Hub.
If you have not used docker before, you can follow the instruction to install and set up docker in Windows, Mac or Linux.
The Docker image can be obtained by running:
docker pull campbio/sctk_qc
Docker image will not access files in your host file system by default. To get access to the files on your machine, you can properly set up a mount volume. Noted that the transcriptome data and GMT file needed to be accessible to the container via mounted volume. In the below example, mount volumn is enabled for accessing input and output directory using argument -v. The transcriptome and GMT files inside /path/to/data is now available in /SCTK_docker folder inside the docker. To learn more about mounted volumes, please check out this post.
The usage of each argument is the same as running command line analysis. Here is an example code to perform quanlity control of CellRangerV3 data singleCellTK docker:
docker run --rm -v /path/to/data:/SCTK_docker \
-it campbio/sctk_qc:1.7.5 \
-b /SCTK_docker/cellranger \
-P CellRangerV3 \
-s pbmc_100x100 \
-o /SCTK_docker/result/tenx_v3_pbmc \
-g /SCTK_docker/mitochondrial_human_symbol.gmt \
-S TRUE \
-F R,Python,Flatfile,HTAN
The Singulatiry image can easily be built using Docker Hub as a source:
singularity pull docker://campbio/sctk_qc:1.7.6
The usage of singleCellTK Singularity image is very similar to that of Docker. In Singularity 3.0+, the mount volume is automatically overlaid.
It’s recommended to re-set the home directory when you run singularity. Singularity will mount $HOME path on your machine by default, which might contain your personal R/Python library folder. If we don’t re-set the home to mount, singularity will try to use R/Python libraries which are not built within the singularity image and cause some conflicts. You can point to some “sanitized home”, which is different from $HOME path on your machine, using argument -H/–home. Besides, you can use argument –bind/-B to specify your own mount volume, which is the path that contains the dataset and will be used to store the output of QC pipeline. The example is shown as below:
singularity run --home=/PathToSanitizedHome \
--bind /PathToData:/data sctk_qc_1.7.6.sif \
-P CellRangerV3 \
-s gencodev34_pbmc_1k_v3 \
-b /data/gencodev34_pbmc_1k_v3
-o /data/result/gencodev34_pbmc_1k_v3 \
-S TRUE \
-F R,Python,FlatFile,HTAN \
-n 15 \
-T MulticoreParam
Also, you might want to specify cpu architecture when run the Singularity image on BU SCC using ‘#$ -l cpu_arch=broadwell|haswell|skylake|cascadelake’ command. Because the python packages are compiled by SIMD instructions that are only compatible on these cpu architectures. If you are runnning singularity on other cluster, please contact IT helps about how to specify cpu architecture when you run the singularity image.


This documentation is intended for the developer wanting to integrate into the IoT (Internet of Things) Platform:
Building applications to the the HotSchedules IoT Platform Cloud APIs Building mobile/html applications either as standalone html applications or to be part of Bodhi Mobile Building a data integration application into your point of sale system to be stored and accessed from our cloud solution


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| Passed | Failed | Others | |
|---|---|---|---|
| @Sanity | 1 | 0 | 0 |
| @TestDropdown | 1 | 0 | 0 |
| @TestDropdown_VerifyAllText | 1 | 0 | 0 |


The Shiba Inu is a Japanese dog breed. There are six original distinct breeds of dog from Japan, and the Shiba Inu is the smallest one.
The largest in the group is the Akita. There aren’t many red and white Akitas in the United States, but the few that I have seen, look just like larger versions of the red Shiba Inu. And not in the Schwarzenegger-DeVito sort of way.
Between the Akita Inu and the Shiba Inu, are the medium sized Japanese dog breeds, including the Shikoku, Kai, Ainu, and Kishu. These six Japanese dog breeds have very similar appearance and temperaments. They all belong to the Spitz dog family.
The Shiba Inu is a keen dog with quite the personality. Complex, dignified, and hard-to-please. An indelible personality trait of the Shiba Inu is their aloofness. Shiba Inus are not usually friendly to strangers. In fact, Shiba Inus are not particularly affectionate with their owners either.
They will of course, be excited and happy to see you when you get home – but after a while they tend to let you know that “had enough” of your presence. Shiba Inus do have a charming side as well. They seem to be aware of their unrivaled beauty and aren’t afraid to use it to their advantage. Shibas are not really fond of other dogs – especially of the same sex. Some Shiba Inus may display aggression to other dogs and humans – especially if the Shiba Inu was bred irresponsibly by a puppy mill or backyard breeder. However, a properly bred Shiba Inu that conforms to breed standards should not be aggressive.
Shibas shed — a lot. It has been said that they shed twice a year, but some owners quip that it lasts for six months at a time. Unless a Shiba is a “long coat,” the coat does not mat, so infrequent brushing doesn’t hurt the dog, but brushing or combing during periods of heavy shedding will reduce the amount of hair around the house. Blowing the dog with a strong blow-dryer or a shop vacuum in reverse is a good way to remove loose hair, dirt, and dandruff and to check for fleas. Most dogs learn to like the blower, as it feels good and doesn’t scrape the skin or pull the coat. Don’t let it get too hot, though. Shibas often object to nail trimming. Start a puppy early, but if it becomes a major struggle, let a professional do it.
The Shiba Inu should do well on a high-quality dog food, whether commercially manufactured or home-prepared with your veterinarian’s supervision and approval. Any diet should be appropriate to the dog’s age (puppy, adult, or senior).Some Shibas are picky, and others will eat anything in sight. Some dogs are prone to getting overweight, so watch your dog’s calorie consumption and weight level; you should be able to feel the dog’s ribs and backbone, but not see them.. Treats can be an important aid in training, but giving too many can cause obesity. Learn about which human foods are safe for dogs, and which are not. Check with your vet if you have any concerns about your dog’s weight or diet. Clean, fresh water should be available at all times.


Norman's Cay is one of the largest islands in the Exumas and a favourite stop for yachts because of the surrounding reefs and secure anchorage.
It has an interesting history, as the island used to be under the control of drug lord Carlos Lehder. The old airstrip, partially submerged C46 plane wreck and abandoned buildings are remnants of this drug trade era.
Diving on the plane wreck is reported to be spectacular.
Fort Capital reports they have purchased 110+ acres at the SW tip of the island and 180+ at the NE tip. They have named their development White Tail Resort. In Jan 2015, construction of an extension to the runway and a marina basin was in evidence.
There is good holding in hard sand off the SW coast near the airport. Note the airstrip is being extended by the resort developers.
There is also good holding at the edges of the channel at the mouth of the interior lagoon on the Southern end of the Cay. Take care with the strong tidal currents here. Do not anchor in the approach channel to the south of the pier.
The remains of a crashed DC3 can be seen in the interior lagoon, although it is now mostly submerged.
There are no dinghy docks here currently. Beach landings only.
Last updated March 2016.


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