The conda environment file (stochastic_env.yml) is provided, and it is encouraged to use it, so no library version mismatch happen.
Different parts of the project were implemented as either standalone python scripts, scripts as the part of the nextflow pipelines, Mathematica notebooks, or Jupyter notebooks (mostly for the later stages of the project)
Note: even though the pipeline scripts can be run manually, I highly suggest running them via nextflow (this allows running the whole pipeline from input to the final results without interruption and any intervention from the user)
The following documentation describes all the components in the roughly logical order (more or less in the order parts of the project are to be executed).
For the details on the methods and the project itself see the article pending
Nextflow creates a lot of temporary files! Even though this is sometimes useful (e.g. when you change a small part of the pipeline, and run the pipeline again with -resume option, it will rerun only the parts that have been affected), it is advised to keep an eye on the directory (work/
) and clean it from time to time.
For each dataset processed with this pipelines (and subsequent notebooks) the results directory structure is as follows:
Input: raw PANTHER families file (i.e. PTHR15.0_mouse.tsv)
Output:
Description: Extracts MGI, Ensembl, Uniprot and Entrez IDs. Which are then to be manually converted into gene symbols (e.g. with db2db) and to be again fed into the script. It, then, merges the family IDs with gene symbols. The log of the cleaning that I have performed is below:
Note: clean_panther.csv is provided in this repository
Input:
Output: *.csv (cleaned TPM dataset)
Description: Many datasets that we used had duplicated genes (e.g. Dopaminergic). Therefore the duplicates need to be disabiguated. The process applied:
Chromosome-wise pipeline is run by full_dataset_IC.nf and full_dataset_IC_no_BI.nf (with and without Bimodality Index filtering for TPM and UMI respectively). The final results of the pipeline: dichotomised genes, optimal thresholds for each gene, and IC calculations (both for the original distribution and for the shuffled chromosomes). Input parameters:
Examples:
time ./full_dataset_IC.nf --dataset `realpath Dopaminergic_TPM_clean.csv` --gff `realpath GRCm38.p6_genomic.gff` --type geomean --shuffle_N 1000 -resume;
time ./full_dataset_IC_no_BI.nf --dataset `realpath Alveolar_cells_Type_I_Merged_Batches.csv` --gff `realpath GRCm38.p6_genomic.gff` --type 3max --shuffle_N 1000 -resume;
Note: for the ease of use, all the pipelines are run with run_workflow.sh script, you can refer to it for more examples of commands to run
Note: all scripts that are run with pipelines are in bin/ directory
Note: for all pipelines, the results are saved under the directory corresponding to the name of the dataset file provided.
Input: GFF file
Output: $dataset_name/intermediate/chr*_filtered.csv -- CSV for each filtered chromosome separately
Description: The script reads the GFF file, splits it into chromosomes, extracts genes, filters them (remove predicted genes, and keep genes with the source BestRefSeq), and outputs results as a series of CSV files.
Input:
Output:
Description:
Calculates individual thresholds for each gene. For dichotomisation methods see Methods section in the article, or the corresponding code.
Input:
Output: $dataset_name/intermediate/$type/filtered_dichotomised_genes.csv -- filtered table of dichotomised genes
Description: Performes BI filtering (for TPM datasets). Other types of filtering can potentially be added.
Input:
Output:
Description: The process is run for each chromosome separately (potentially in parallel). The calculations are performed for all overlapping stretches along the chromosome. Stretch sizes 7, 14 and 21. The output rows record the start and end position in the chromosome (gene number, starting from 0, end -- non-inclusive), stretch size, number of genes in a stretch, observed and PB variances, IC, and mean number of expressed genes per cel.
Input:
Output: shuffled_IC.csv (in temporary directory)
Description: Performes the same calculations as calculate_IC.py, but shuffles the order of the genes in chromosomes before that. The process is run shuffle_N times.
Input:
Output: $dataset_name/results/$type/shuffled_IC.csv -- combined shuffled ICs
Description: When all the shuffled calculations are finished, the temporary files are combined together (adding the column to indicate iterations).
Family-wise pipeline is run by family_wise_IC.nf. The final results of the pipeline: dichotomised genes, optimal thresholds for each family, and IC calculations (both for the original distribution and for the shuffled chromosomes). Input parameters:
Examples:
time ./family_wise_IC.nf --dataset `realpath Dopaminergic_TPM_clean.csv` --familyset `realpath clean_panther4march.csv` --type geomean05 --shuffle_N 1000 -resume;
time ./family_wise_IC.nf --dataset `realpath klein.csv` --familyset `realpath clean_panther4march.csv` --type fm05 --shuffle_N 1000 -resume;
Input:
Output:
Description: Gets the overlap between the familyset and genes in a dataset, and filters out the families of size less than 5.
Input:
Output:
Description: Calculates family-wise thresholds and dichotomises genes accordingly.
Input:
Output: $dataset_name/$familyset_name/results/family_IC.csv
Description: Calculates IC for each family (from dichotomised genes). Reports family ID, family size, number of recorded genes, observed variance, PB variance, IC, and mean number of expressed genes per cell.
Input:
Output: shuffled_families.csv (temporary)
Description: The process run N times. Randomly shuffles assignment of genes to gene families.
Input:
Output: family_IC.csv (temporary)
Description: Performs the same calculation and reports the same output as the "Calculate IC" process, but on the shuffled familyset. The process is then run for each shuffled familyset (i.e. N times).
Input:
Output: $dataset_name/$familyset_name/results/shuffled_family_IC.csv
Description: Combines all shuffled familyset IC files together, adding the column to indicate iteration.
All of the below is to be run after the pipelines finished calculation, since most of the notebooks rely on the calculation results
Input:
Description: Performs Kolmogorov-Smirnov test and permutation test (for each stretch size separately). Plots the results, and saves the results as CSV in $dataset/ directory
Input:
Description: Combines information on statistical tests for all datasets and plots it (volcano and bar plots). The combined data is then saved in chr_stats_test_summary.csv
Input:
Description: Plots histograms marking different thresholds
Depends on the results of dataset_overlaps.ipynb, run it first Input:
Description: Produces table of overlaps (with verbose description) for chromosomal stretches -- overlap_chromosome_lower_analysis.xlsx
Description: Performs Kolmogorov-Smirnov test and permutation test (for each family size separately, for family sizes that have at least 30 families). Plots the results, and saves the results as CSV in $dataset/ directory
Depends on the results of dataset_overlaps.ipynb, run it first Input:
Description: Produces table of overlaps (with verbose description) for chromosomal stretches -- overlap_family_lower_analysis.xlsx
Input:
Description: Combines information on statistical tests for all datasets and plots it (volcano and bar plots). The combined data is then saved in family_stats_test_summary.csv
Plots heatmaps (on/off states) and histograms of number of expressed genes per cell for the chosen families.
Some notebooks perform analysis on both chromosome- and family-wise calculation results
Calculates lower and upper tail overlaps for both chromosome- and family-wise.
Output:
Transoforms data obtained with Mathematica for further analysis: $dataset/mathematica/fitted_distributions_thresholds.csv and $dataset/mathematica/fitted_distributions_dichotomised.csv
Input:
Description: Calculates correlation between dichotomisation methods, and plots pair-scatterplots and tables of correlation
Plots histograms of marker genes for Alveolar cells, and calculates accuracy of different dichotomisation methods
Input:
Description: Performs distribution fitting for individual genes, and searches for antimodes (if a distribution is bimodal)
Input:
Description: Reimplementation of the shuffling and the permutation test in Mathematica (family-wise only). Does both steps (shuffling and test) and produces the p-values as an output.
Couple functions that are used by nextflow pipeline specifically
[filename1, filename2, ...]
into the list of filenameschr18
)Parses Mathematica probability distributions (as text strings) into pomegranate objects (so they can be operated from python)


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[gecode-users] compilation question
Guido Tack
tack at ps.uni-sb.de
Tue Mar 11 19:36:40 CET 2008
Alejandro Arbelaez wrote:
> Hi all,
>
> I'm trying to compile gecode with gcc 4.2 and I'm getting the
> following error:
[...]
> BTW: my default compiler is i686-apple-darwin9-gcc-4.0.1 and I
> installed i686-apple-darwin9-gcc-4.2.1. Finally I configured gecode
> with the following characteristics
>
> ./configure --prefix=..... CC=g++-4.2 CXX=g++-4.2
That's the problem, you're trying to compile a C source file with g+
+. You should configure like this:
./configure --prefix=..... CC=gcc-4.2 CXX=g++-4.2
Cheers,
Guido
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Resolving Objects
There are types you can create immediately from the container and there are types that require registration. Generally any primitive type would require registration and all classes are available immediately.
You can use the Unity container to generate instances of any object that has a public constructor (in other words, objects that you can create using the new operator). When you call the Resolve method and specify the type that is not registered, the container simply generates and returns an instance of that type. However, the only time that this is realistically practical is when the object you are generating does not have ambiguities Unity could not resolve or contains dependency attributes that the container will use to inject dependent objects into the requested object.
The Unity container identifies type registrations and type mappings in the container using a type and, optionally, a name. The type is an interface or a class (usually an interface or base class) that the desired concrete object type implements or inherits. This identifies the mapping so that the container can retrieve the correct object type in response to a call to the Resolve
method. Where there is more than one mapping for the same type, the optional name differentiates these mappings and allows code to specify which of the mappings for that type to use.
The provision of both generic and non-generic overloads of many of the Unity container methods ensures that Unity can be used in languages that do not support generics. You can use either approach (the generic or the non-generic overloads) in your code and mix them as required.
The following topics describe how you can resolve objects using the Resolve or BuildUp methods:
- Resolving an Object by Type
- Resolving collections of Objects of a Particular Type
- Resolving Generic Types
- Deferring the Resolution of Objects
- Overrides
For more information about how you can configure Unity with type registrations and mappings, see Registration.
For more information about how you can perform dependency injection on existing object instances, see Using BuildUp to Wire Up Objects Not Created by the Container.


The Good Travel
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Info Bubble
You can add an info bubble to the grid, that will show all the values of a record in a w2tag when you click the info bubble icon.
Usually, you would add the info bubble only to one column. The demo below is just to demonstrate how versatile the info property can be.


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| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|
| 8/30/2018 |
Ogbonnaya Onu |
7b Oyadiran Estate Sabo,Yaba,Lagos |
07031977216 |
Fried Rice and Chicken |
I love my Chicken Grilled and spicy |
|


+++ title = "Enduro" linkTitle = "Enduro" +++ {{< blocks/section color="white" >}}
This is the homepage of Enduro.
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Artistic Integrity
Don’t worry about your artistic integrity. Selling out is harder than it looks.
Just be honest about what you want. Integrity will be there when you need it.
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Just be honest about what you want. Integrity will be there when you need it.


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Haide Wandaga is the Web developer of this site. She is currently studying at the University of the Cordilleras,a private school at Baguio City, as a second year. She is currently taking the course Bachelor of Science in information Technology majoring in Network and Security.
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All variables managed through the MPI tool information interface represent their values through typed buffers of a given length and type using an MPI datatype (similar to regular send/receive buffers). Since the initialization of the MPI tool information interface is separate from the initialization of MPI, MPI tool information interface routines can be called before MPI_INIT. Consequently, these routines can also use MPI datatypes before MPI_INIT. Therefore, within the context of the MPI tool information interface, it is permissible to use a subset of MPI datatypes as specified below before a call to MPI_INIT (or equivalent).
| MPI_INT |
| MPI_UNSIGNED |
| MPI_UNSIGNED_LONG |
| MPI_UNSIGNED_LONG_LONG |
| MPI_COUNT |
| MPI_CHAR |
| MPI_DOUBLE |
Rationale.
The MPI tool information interface relies mainly on unsigned datatypes for integer values since most variables are expected to represent counters or resource sizes. MPI_INT is provided for additional flexibility and is expected to be used mainly for control variables and enumeration types (see below).
Providing all basic datatypes, in particular providing all signed
and unsigned variants of integer types, would lead to a larger
number of types, which tools need to interpret. This would
cause unnecessary complexity in the implementation of tools
based on the MPI tool information interface.
( End of rationale.)
The MPI tool information interface only relies on a subset of the basic MPI
datatypes and does not use any derived MPI datatypes.
Table 14
lists all MPI datatypes that
can be returned by the MPI tool information interface to represent
its variables.
The use of the datatype MPI_CHAR in the MPI tool information interface implies a null-terminated character array, i.e., a string in the C language. If a variable has type MPI_CHAR, the value of the count parameter returned by MPI_T_CVAR_HANDLE_ALLOC and MPI_T_PVAR_HANDLE_ALLOC must be large enough to include any valid value, including its terminating null character. The contents of returned MPI_CHAR arrays are only defined from index 0 through the location of the first null character.
Rationale.
The MPI tool information interface requires a significantly simpler
type system than MPI itself. Therefore, only its required subset
must be present before
MPI_INIT (or equivalent) and MPI implementations
do not need to initialize the complete MPI
datatype system.
( End of rationale.)
For variables of type MPI_INT, an MPI implementation can
provide additional information by associating names with a fixed
number of values.
We refer to
this information in the following as an enumeration. In this case, the respective
calls that provide additional metadata for each control or performance
variable, i.e., MPI_T_CVAR_GET_INFO
(Section Control Variables
) and MPI_T_PVAR_GET_INFO
(Section Performance Variables
), return a handle of type
MPI_T_enum
that can be passed to the following functions to
extract additional information.
Thus, the MPI implementation can describe variables with a
fixed set of values that each represents a particular state.
Each enumeration type can have N different values, with a
fixed N that can be queried using MPI_T_ENUM_GET_INFO.
| MPI_T_ENUM_GET_INFO(enumtype, num, name, name_len) | |
| IN enumtype | enumeration to be queried (handle) |
| OUT num | number of discrete values represented by this enumeration (integer) |
| OUT name | buffer to return the string containing the name of the enumeration (string) |
| INOUT name_len | length of the string and/or buffer for name (integer) |
int MPI_T_enum_get_info(MPI_T_enum enumtype, int *num, char *name, int *name_len)
If enumtype is a valid enumeration, this routine returns the number of items represented by this enumeration type as well as its name. N must be greater than 0, i.e., the enumeration must represent at least one value.
The arguments name and name_len are used to return the name of the enumeration as described in Section Convention for Returning Strings .
The routine is required to return a name of at least length one. This name must be unique with respect to all other names for enumerations that the MPI implementation uses.
Names associated with individual values in each enumeration enumtype can be queried using MPI_T_ENUM_GET_ITEM.
| MPI_T_ENUM_GET_ITEM(enumtype, index, value, name, name_len) | |
| IN enumtype | enumeration to be queried (handle) |
| IN index | number of the value to be queried in this enumeration (integer) |
| OUT value | variable value (integer) |
| OUT name | buffer to return the string containing the name of the enumeration item (string) |
| INOUT name_len | length of the string and/or buffer for name (integer) |
int MPI_T_enum_get_item(MPI_T_enum enumtype, int index, int *value, char *name, int *name_len)
The arguments name and name_len are used to return the name of the enumeration item as described in Section Convention for Returning Strings .
If completed successfully, the routine returns the name/value pair that describes the enumeration at the specified index. The call is further required to return a name of at least length one. This name must be unique with respect to all other names of items for the same enumeration.